Customer-Validated Converged Solution Supports Microsoft SQL Server Very Large Database Configuration

 
Customer-Validated Converged Solution Supports Microsoft SQL Server Very Large Database Configuration
W   H   I   T   E   P A   P   E   R

                                      Customer-Validated Converged
                                      Solution Supports Microsoft
                                      SQL Server Very Large Database
                                      Configuration

                                      Hitachi Virtual Storage Platform with Hitachi Compute Blade
                                      2000 Consolidates High-Transaction Deployments

                                      By Scott Davis, Mike Becker

                                      December 2012
Customer-Validated Converged Solution Supports Microsoft SQL Server Very Large Database Configuration
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    Table of Contents
    Introduction                                                         3
    Solution Overview                                                    4
    Key Solution Components                                              6
          Hardware Components                                            6
          Software Components                                           11
    Solution Design                                                     14
          Very Large Database Implementation for Microsoft SQL Server   14
          Storage Architecture                                          14
          Storage Configuration                                         16
          SAN Architecture                                              20
          Network Architecture                                          21
          Compute Blade Configuration                                   23
    Engineering Validation                                              24
          Test Methodology                                              25
          Test Results                                                  26
          Latency Measured by SQLIO and IOMeter                         28
    Conclusion                                                          31
    Appendix A — Contributors                                           32
Customer-Validated Converged Solution Supports Microsoft SQL Server Very Large Database Configuration
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    Introduction
    Many organizations are experiencing tremendous growth in data and must scale their infrastructure
    to keep pace with the data explosion. Data volumes are stressing the ability of storage to scale
    in performance to manage the data as well as to scale in capacity nondisruptively. Standalone
    servers are reaching bottlenecks in memory, processing power and I/O capabilities as data volumes
    increase.

    The very large database (VLDB) solution from Hitachi Data Systems integrates with Microsoft SQL
    Server 2008 R2. This integration provides a framework to consolidate SQL OLTP and other types of
    high-transaction deployments on a single platform capable of handling current storage and server
    loads reliably while providing nondisruptive growth into the future with extensive management
    capabilities. Provided by a single vendor, this solution provides a highly scalable server and storage
    platform designed for adding capacity without downtime. The components within the platform
    provide automatic, dynamic load balancing, avoiding the need for manual performance tuning.
    All components have built-in remote management and integrate with industry-leading Hitachi
    Command Suite as well as with Microsoft management tools. All of this leads to a highly scalable
    system, removing constraints on Microsoft SQL Server with better than 99.999% availability,
    delivering constant uptime to clients now and into the future.

    This white paper's configuration information is intended for IT administrators, architects, DBAs, CIOs
    or CTOs with an interest or responsibility for planning a very large dataBase system using Microsoft
    SQL Server 2008 R2 or later.

      Note: Testing of this configuration was in a lab environment. Many things affect production
      environments beyond prediction or duplication in a lab environment. Follow the recommended
      practice of conducting proof-of-concept testing for acceptable results in a nonproduction,
      isolated test environment that otherwise matches your production environment before your
      production implementation of this solution.
Customer-Validated Converged Solution Supports Microsoft SQL Server Very Large Database Configuration
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    Solution Overview
    The very large dataBase (VLDB) converged solution configuration from Hitachi Data Systems pro-
    vides the scalable server, storage area network, and storage to keep pace with the growth of large
    enterprise-level databases. The combination of the converged Hitachi solution with Microsoft SQL
    Server 2008 R2 provides a high-performance, extremely reliable VLDB solution with the ability to
    grow over the long term as business needs expand.

    The solution reviewed in this white paper was developed to meet a customer use case for a service
    bureau that provides personal reports to individuals and corporations in real time, with a requirement
    for rapid response times. For this customer, large amounts of data are constantly being acquired
    and updated to service queries from their Internet portal using a primary Microsoft SQL 2008 online
    transaction processing (OLTP) database approximately 12TB in size and growing to an expected
    size of 20TB or larger in the next 12 to 24 months.

    This VLDB configuration from Hitachi Data Systems comprises the following infrastructure elements:

    ■■Compute          infrastructure — 1 Hitachi Compute Blade 2000 server supporting Microsoft SQL
       Server 2008 R2 with:
      ■■ 8   blades configured into 2 symmetric multiprocessor (SMP) servers providing compute scal-
          ability.
      ■■ Fibre   Channel dual-ported Brocade host bus adapters (HBAs) for redundant connectivity to
          the storage infrastructure.
      ■■ 64   cores and 768GB of memory per SMP.
    ■■Storage        infrastructure — 1 Hitachi Virtual Storage Platform:
      ■■ Dual    controllers for active-active link redundancy and added bandwidth.
      ■■ 512GB        total system cache.
      ■■ 75TB        initial useable disk capacity with growth to multiple petabytes.
    ■■SAN     infrastructure — 2 Brocade 5100 series Fibre Channel switches:
      ■■ Redundant        active-active paths from storage to the servers using Brocade 5100 switches.
      ■■ 16   x 8Gb/sec Fibre Channel ports per switch.
    ■■Network         infrastructure — redundant paths for robust connectivity.
    Figure 1 provides an overview of the VLDB configuration showing connections between the storage
    and servers.

    This customer configuration depicts a dual SMP configuration composed of 4 blades per SMP
    connected to a Hitachi Virtual Storage Platform capable of supporting a 32TB VLDB to each SMP.
    The design encompasses the use of various storage devices. For example solid state disks (SSDs)
    provide high bandwidth for heavily used database files, such as the SQL tempdb, and logs and
    lower-bandwidth devices, such as SAS drives, for lower-bandwidth activity.
Customer-Validated Converged Solution Supports Microsoft SQL Server Very Large Database Configuration
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    Figure 1. Very Large Database Customer Configuration Components and Connections
Customer-Validated Converged Solution Supports Microsoft SQL Server Very Large Database Configuration
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    Key Solution Components
    The following sections describe the key hardware and software components making up this
    solution.

    Hardware Components
    To sustain a scalable and reliable high-performance system to manage high volumes of data, the
    VLDB customer configuration is recommended.

    Table 1 lists the hardware components that this customer configuration uses.

        Table 1. The Very Large DataBase Architecture
        hardware components
          Component                     Description                     Firmware        Quantity
          Hitachi Compute Blade 2000    16 × 8 Gb/sec dual-port         A0170-B-5805    1
          chassis                       Brocade host bus adapters
                                        (HBAs)
                                        4 × 1 Gb LAN switch module
                                        2 x 8 Gb Fibre Channel
                                        switch modules
                                        2 × management modules
                                        8 × cooling fan modules
                                        4 × power supply modules
          Hitachi Compute Blade 2000,   2 × 8-core Intel Xeon X7560     EFI 03-20       8
          X57A1 blade                   2.26GHz
                                        192 GB memory
                                        1 x 4 port 1 Gb Ethernet
                                        mezzanine card
                                        1 x 4 port 8 Gb Fibre Channel
                                        mezzanine card
          Hitachi Virtual Storage       Multi-chassis18 x 8 Gb Fibre    70-03-04-0000   1
          Platform storage system       Channel ports used
                                        512 GB cache memory
                                        536x 300 GB, 10K RPM,
                                        SAS disks
                                        28x 400 GB SSDs
          Brocade 5100 switch           SAN switch with 16 x 8Gb        6.4.1b          2
                                        Fibre Channel ports
          Brocade 825 Fibre Channel     Dual-ported HBA                 v2-3-0-2 x64    16
          HBA                           8 Gb
                                        PCI Express 2.0
Customer-Validated Converged Solution Supports Microsoft SQL Server Very Large Database Configuration
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    Hitachi Compute Blade 2000

    Hitachi Compute Blade 2000 (CB 2000) is an enterprise-class, high-performance scalable server
    platform. Housed in a blade chassis, up to 8 dual-socket, 16-core blades can
    be accommodated. Hybrid I/O composed of internal I/O within a blade and
    PCI-e I/O in the chassis can be utilized for high bandwidth and scalability. All
    components are redundant and hot pluggable for high reliability to achieve at
    least five-9s (99.999%) availability. Built-in management features provide for
    ease of configuration and management in addition to remote management
    capability.

    Each blade has 2 processor sockets with each socket supporting up to 8 cores per processor,
    providing 16 cores per blade. A blade can have up to 384GB of memory to support compute-
    intensive applications. Blades contain 2 mezzanine I/O slots and are tied to 2 PCI-e slots, which can
    be shared, providing greater bandwidth to enable scaling for growth. In addition to the PCI-e slots,
    embedded switches within the blade enclosure provide connectivity for both Ethernet and Fibre
    Channel.

    Logical partitioning (LPAR) firmware-based resources within the blade chassis can be tied together
    or isolated to form various-sized virtual machines. Blades can also be partitioned into 2- or 4-blade
    SMP systems with I/O resources segregated into the SMP. A crossbar switch between the blades
    within an SMP is utilized for connecting the blades. Configuring blades into a 4-blade SMP provides
    a secure scalable compute resource with up to 8 sockets containing 64 cores supporting up to
    1.5TB of memory for server consolidation and highly scalable performance for high-performance
    computing environments.

    Components are redundant and hot swappable, reducing unplanned downtime and enabling
    five-9s availability. Power supplies, switches and fans are redundant and hot swappable. Blades
    can be set up to failover and be hot-replaced. Configurations can be created of N+M blades where
    N blades are backed up by M blades for failover, even across multiple Compute Blade 2000 chas-
    sis. Figure 2 provides a visualization of the layout of the CB 2000 architecture.
Customer-Validated Converged Solution Supports Microsoft SQL Server Very Large Database Configuration
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       Figure 2. illustrates the layout of a Compute Blade 2000 Layout

    The blade chassis is a 19-inch rack-compatible 10U-high unit. Management modules within the
    blade chassis provide both command line and GUI interfaces for configuring and managing the
    blades and chassis components remotely. The GUI management interface provides for easy con-
    figuration of the system. Interfaces are provided through the management modules for connecting
    remotely to the operating systems on the blades.

    Using CB 2000 for the VLDB configuration removes resource constraints on the Microsoft SQL
    Server. It also provides for processing and I/O scalability to meet current performance requirements
    for a VLDB and provides for future growth scaling.

    Hitachi Virtual Storage Platform

    Hitachi Virtual Storage Platform (VSP) offers an entirely new level of scalable
    enterprise storage capable of handling the most demanding workloads
    while maintaining great flexibility. The system offers high reliability (with a
    100% uptime guarantee available) while being able to dynamically scale
    bandwidth, processing and storage capacity independently. This provides
    a platform for hosting multiple scalable applications and for converging
    multiple existing systems.

    VSP is engineered to be scalable in 3 dimensions: Scale up for increased
    performance, adding just the processing, cache, ports, etc., that you need;
    scale out for increased internal capacity, with up to 1,280 large form factor
    drives, 2,048 small form factor drives, or combinations of the 2; scale deep
    to leverage industry-leading external virtualization technology and manage
    your existing storage through a common interface and management tools.
Customer-Validated Converged Solution Supports Microsoft SQL Server Very Large Database Configuration
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    Virtual Storage Platform Features

    PERFORMANCE

    ■■Predictable,    scalable performance through industry-leading engineering — every component of
       VSP is scalable independently so VSP grows as your demands grow:
      ■■ Up     to 32 processing cores.
      ■■ Up     to 1TB of shared global cache.
      ■■ Point-to-point    SAS-2 internal architecture with up to 192GB/sec expandable grid switch for
          internal data movement.
      ■■ 2.5-inch    and 3.5-inch SSD, SAS, nearline-SAS and SATA disk options.
      ■■ Up     to one hundred ninety-two 8Gb/sec Fibre Channel, IBM® FICON® and 10Gb/sec Fibre
          Channel over Ethernet (FCoE) host ports.
    ■■Hitachi    Dynamic Provisioning allows grouping of multiple RAID groups into a pool which can be
       dynamically grown or shrunk without disruption to production applications. Physical storage can
       be hot-added and appended to the pool to provide as-needed capacity and performance scal-
       ing. Dynamic provisioning utilizes a technique called "wide striping," which distributes the data
       across all the disks in a pool, delivering greater IOPS for performance scaling.
    ■■Dynamic     pools can be tailored for performance specific to different workloads, and expanded or
       shrunk on the fly while in production.

    COST EFFICIENCY

    ■■Green     Initiative: Best-in-class power, cooling, floorspace density, operating costs and data avail-
       ability mean a truly affordable solution for the long term, including a 46% smaller footprint and up
       to 48% less power and cooling than other solutions.
    ■■Thin   provisioning: Allocates capacity only as needed and includes an ability to reclaim any unuti-
       lized capacity.

    RELIABILITY

    ■■With   the industry's only 100% uptime guarantee, backed by a US$1million bond, delivering the
       highest reliability with fully redundant components, VSP provides the ability to scale capacity
       while applications are running in production.
    ■■Ability   to meet increasing demands by dynamically and nondisruptively adding internal proces-
       sors, cache connectivity, and capacity as well as scale externally attached storage as needed.
    ■■Global    support: 24/7 x 2-hour or 4-hour response.

    MANAGEABILITY

    ■■Single    integrated management suite for consolidating existing SAN and NAS systems.
    ■■Common       GUI dashboard and command line interface (CLI) for provisioning, replication, perfor-
       mance tuning and alerting.
    ■■Automatic     and dynamic internal load balancing means optimized performance and reduced
       management overhead.
Customer-Validated Converged Solution Supports Microsoft SQL Server Very Large Database Configuration
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     SYSTEM AND DATA SECURITY

     ■■Data-at-rest   encryption built into firmware with no impact on system performance.
     ■■Role-based     management system control for delegation of responsibilities and access to
       resources.
     ■■Cache     backup provided via SSD drives to enable the unit to withstand a total power outage with-
       out data loss.
     ■■Hi-Track®    Remote Monitoring system.
     ■■In-system    replication: Hitachi ShadowImage® Heterogeneous Replication volume copies and
       Hitachi Thin Image.
     ■■Logically   partition storage resources, including capacity, cache and ports for security and man-
       agement requirements.
     ■■Deliver   metered storage services and guaranteed quality of service (QoS) based on customizable
       application-specific requirements.

     VIRTUALIZATION

     ■■Industry-leading    virtualization of externally attached heterogeneous storage environments means
       you can manage all your storage via a single management tool.
     ■■Provides    the ability to nondisruptively and dynamically virtualize new or existing external stor-
       age systems without the need to perform large scale data migration; allows you to seamlessly
       migrate from older technologies to newer with just a single server reboot instead of hours or days
       of downtime waiting for data to migrate.
     ■■Leverage     existing storage assets while increasing utilization efficiency, improve performance to
       older storage systems, and extend new features such as VMware VAAI integration and dynamic
       tiering to existing storage systems.

     AUTOMATED TIERED STORAGE MANAGEMENT

     ■■Dynamic     tiering provides the ability to dynamically make more active data available on higher-
       performance disks (such as SSD drives) without operator intervention. At the same time, it
       moves less active data to lower performing drives, including modular and 3rd-party storage,
       without interrupting the application and delivering more effective use of storage resources.
     ■■Automated     policy-based mechanism transfers data volumes while matching application-driven
       requirements with the storage system characteristics for price, performance and availability.
     ■■Automated,     dynamic sub-LUN tiering enables page-level placement across internal and external
       storage tiers, including multivendor storage systems, based upon page utilization performance
       requirements.

     REPLICATION

     ■■Integrated    heterogeneous replication, any-to-any long distance replication via a single interface
       with ability to logically group sets of volumes by individual application to ensure data consistency
       spanning across infrastructure.
     ■■Support     for enhanced capabilities such as 3 data center cascading or multi-target solutions
       ensures that any future business continuity requirements can be adequately met.
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     ■■Ability   to perform storage clustering for virtualized environments ensures automated transparent
        failover of applications.

     OPERATING SYSTEM INTEGRATION

     ■■Microsoft    Hyper-V support for live migration over distance.
     ■■Free   Microsoft System Center plug-ins for SCOM, SCCM, SCVMM.

     VLDBs typically grow at a high rate as new data is acquired and assimilated. VSP provides the
     ability to dynamically grow storage without disruption to keep pace with the new data. While data is
     being added, low response times are required to service database queries. The parallel paths, large
     cache, and high performance of the VSP platform provide the ability to respond to these queries
     while allowing the database to grow. The ability to add processors and cache allows VSP to keep
     pace with expanding processing needs.

     VSP also addresses VLDB backup by providing options where a database can be replicated in real
     time to another VSP system onsite or offsite, with data consistency, should a failover occur.

     Brocade 825 Fibre Channel HBA

     Brocade 825 Fibre Channel HBA is a dual-ported 8Gb PCI-e I/O card. The card provides high-
     performance data transfers and is manageable with Brocade Network Advisor.

     Brocade 5100 SAN Switch

     The Brocade 5100 SAN switch provides 40 x 8Gb/sec ports with the following characteristics:

     ■■Nonblocking      architecture with all ports active at 8Gb full-duplex with no oversubscription.
     ■■Integrated    routing capabilities to connect switches in different fabrics.
     ■■ISL   trunking for a single logical link of up to 64Gb/sec of balanced
        throughput.

     Software Components
     Table 2 outlines the software components used by this customer configuration.

       Table 2. Customer Configuration
       Software Components
        Software Components                                    Version
        Hitachi Storage Navigator                              Microcode Dependent
        Hitachi management and monitoring tools                Release dependent
        SQLIO disk subsystem benchmark tool                    4/4/2005
        Microsoft Windows Server                               2008 R2 SP1, Datacenter Edition (64-Bit)
        Microsoft SQL Server                                   2008 R2 SP1, Enterprise Edition (64-Bit)
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     Hitachi Storage Navigator Modular 2

     Hitachi Storage Navigator Modular 2 enables essential management and optimization of storage
     system functions on a single storage unit. Using Java agents, Storage Navigator Modular 2 runs on
     most browsers. A command line interface is also provided.

     Use Storage Navigator Modular 2 for the following:

     ■■RAID-level    configurations.
     ■■LUN    creation and expansion.
     ■■Online   microcode updates and other system maintenance functions.
     ■■Performance      metrics.

     For more information, see Hitachi Storage Navigator Modular 2 on www.HDS.com.

     Hitachi Command Suite Management and Monitoring Tools

     In addition to customizable dashboards, graphical reporting and service level objective manage-
     ment, the new Command Suite storage management tools allow simple wizard-based control of all
     Hitachi storage solutions, with many common tasks being completed in just a few clicks. The web-
     based architecture allows you to customize your view of the information with column selections,
     filters and dashboard modules. You can even schedule tasks for later execution, freeing you to do
     other work while the system keeps you apprised of the status.

     Command Suite 7 Module Features:

     Hitachi Device Manager provides centralized management of distributed Hitachi resources, with
     customizable views and dashboards for multisystem provisioning and management:

     ■■Simple   wizard-based management for common operations.
     ■■Intuitive   GUI with matching CLI functions improves
        efficiencies and flexibility for administrators.
     ■■Host   attachment and path management.
     ■■LUN    management and migrations.
     ■■Cache    partitioning and cache residency.
     ■■Replication    setup and management/monitoring.
     ■■Integrated    VMware and Microsoft Hyper-V capabilities.
     ■■Advanced      feature management.
     ■■SNMP     support.

                                          Hitachi Tuning Manager provides in-depth system monitor-
                                          ing and analysis with customizable dashboard views, dozens of
                                          flexible reporting options, alerts and custom reports. The reports
                                          are both real-time and historical, including forecasting, and they
                                          facilitate identifying, isolating and diagnosing storage bottlenecks.
                                          Tuning Manager is able to drill deeply into storage components.
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     Hitachi Command Director delivers graphical dashboard moni-
     toring of service level objectives and system performance with
     customizable alerting and alarm thresholds. Applications such as
     MS SQL are monitored for capacity, response time, IOPS, through-
     put, read hits and write pending percentages.

     Hitachi Performance Monitor provides visibility into resource uti-
     lization within VSP. Data can be displayed via GUI or in-depth data acquired from the command line.

     Hitachi IT Operations Analyzer (ITOA) monitors configuration, availability and performance of
     heterogeneous data center components, including servers, switches (both IP and Fibre Channel),
     and storage systems. It provides end-to-end visibility and proactive monitoring of these compo-
     nents, allowing the user to troubleshoot issues quickly, before they turn into larger problems. ITOA
     can monitor and alert on the application or database services running on the server and on per-
     formance through the server components including CPU, memory and network (both IP and Fibre
     Channel) utilization. Application plug-ins that are available for SQL Server allow specific SQL metrics
     monitoring. An intuitive correlation wizard displays problem areas with percentage of accuracy rat-
     ings to help you pinpoint problem areas quickly.

     SQLIO Disk Subsystem Benchmark Tool

     SQLIO is a tool provided by Microsoft to determine the I/O capacity of a given server/storage
     configuration. The load and number of simulated database files can be varied to provide a simula-
     tion of users applying stress to a simulated database. Various scenarios can be run, including read
     random, read sequential, write random and write sequential tests.

     SQLIO can be downloaded from Microsoft at:

     http://www.microsoft.com/en-us/download/details.aspx?id=20163

     Microsoft Windows Server 2008 R2 Datacenter

     Microsoft Windows 2008 Server R2 Datacenter is a multipurpose server operating system designed
     to increase the reliability and flexibility of your server or private cloud infrastructure.

     Additional highlights of Microsoft Windows Server 2008 R2 Datacenter include the following:

     ■■Develop,   deliver and manage rich user experiences and applications.
     ■■Provide   a highly secure network infrastructure.
     ■■Increase   technological efficiency and value within your organization.

     For more information, see "Product Information" on www.Microsoft.com.

     Microsoft SQL Server 2008 R2 — Microsoft SQL Server 2012

     Microsoft SQL Server 2008 R2 and Microsoft SQL Server 2012 provide a scalable, high-
     performance database engine for mission-critical applications that require the highest levels
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     of availability and security. SQL Server 2008 R2 and SQL Server 2012 also provide enhanced
     enterprise-class manageability for large database deployments like the one described in this
     customer configuration guide.

     With the Hitachi Virtual Storage Platform, Microsoft SQL Server 2008 R2 and Microsoft SQL Server
     2012 provide a scalable, high-performance database engine for any midrange to enterprise-level
     application.

     For more information about the features of SQL Server, see the "What's New" page of SQL Server
     Books Online or "Product Information" on www.Microsoft.com.

     Solution Design
     This section provides details on the design of a Microsoft SQL Server very large database imple-
     mented with Hitachi Compute Blades and Hitachi Virtual Storage Platform. The customer configura-
     tion is based on a SQL Server cluster implementation.

     Very Large Database Implementation for Microsoft SQL Server
     The VLDB customer configuration implementation for Microsoft SQL Server provides a base con-
     figuration from which to grow and scale OLTP and other types of high-transaction databases, from
     8TB to 32TB, to support growth over time.

     The referenced solution defines a performance- and cost-optimized design across all key compo-
     nents, including storage, server, application and configuration settings. This provides an optimal
     out-of-the-box balance between Microsoft SQL Server data processing capabilities with aggregate
     hardware throughput.

     ■■Purchase     minimum storage to satisfy storage requirements while providing for adding storage for
        growth and scaling as needed.
     ■■Provide   sufficient disk throughput for SQL Server to achieve a benchmarked maximum data
        processing rate.
     ■■Provide   ability to scale processing power as the workload increases.

     Storage Architecture
     The storage design of this customer configuration uses Hitachi Data Systems and Microsoft recom-
     mended practices for database storage design.

     The design uses Hitachi Dynamic Provisioning on the VSP system to allow scaling by growing
     storage capacity with no disruption in service to users. The configuration chosen makes use of
     2 controllers, which permits spreading the storage load between controllers providing additional
     performance scaling. The ability of VSP to symmetrically distribute the load internally is leveraged to
     permit load scaling.

     For additional performance, high-speed drives (SSDs) are used for SQL files that require high per-
     formance, such as tempdb and transaction logs. For other SQL files with lower performance needs,
     SAS drives are used.
15

     Figure 3 shows the storage configuration and port mapping for this customer configuration using
     the storage off controller 0. Controller 1 is configured in the same manner. Each controller comprises
     2 clusters of components such that if cluster 1 fails, cluster 2 will take over all operations and vice
     versa. The storage is connected to the server via 2 Fibre connections such that if a connection fails,
     traffic will be routed to another connection. The figure depicts the RAID groups, LUNs created from
     the RAID groups, and port connections.

     The storage design provides for 2 paths to every LUN. The paths are active-active to accommodate
     additional bandwidth. On failure of a path, the alternate path absorbs the I/O for the failed path.
     Table 4 shows the storage paths and LUN assignment on VSP.

        Figure 3. Customer Configuration: Storage and Port Mapping
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     Storage Configuration
     The storage is configured in pools built on top of RAID groups. Each Dynamic Provisioning or "DP"
     pool has the pool size set to exceed a capacity beyond that to which the database will ever grow
     (this size exceeds the size of storage currently on the system). When the pool is created, RAID
     groups are assigned, and LUNs are presented to the application from the pool, only enough real
     storage will be allocated to the pool to accommodate the current and near-term needs of the data-
     base (the real storage will be nowhere near the maximum size of the pool). Over time, as the data-
     base grows and approaches the size of the real storage it was given initially, additional disks will be
     installed in the system and added to the pool. The pool will nondisruptively and transparently absorb
     the additional capacity, allowing the database to grow without the application or users being aware.
     This is called thin provisioning: A large pool is created, only enough storage is provided to meet the
     current needs, and then, as storage needs grow, additional storage is dynamically added to the pool
     without disruption. This technique allows the database to scale over time while controlling costs.

     Underpinning the pool are RAID groups. A failure in a disk within a RAID group is protected by the
     RAID mechanism. Additionally, VSP has hot spares available and will spare out predictively when a
     drive begins to fail, thus avoiding most RAID rebuilds. The failed disk can then be hot-replaced with
     a new one.

     Multiple pools are allocated based on disk type. For high-performance pools, SSD drives are uti-
     lized. For files needing less performance, SAS drives are used to form the pool. LUNs are created
     and presented as needed from these pools. Figure 4 shows the RAID group to LUN mapping.

     An added benefit of using dynamic pools is the data is written in what is termed "wide striping."
     Wide stripping distributes the data across all the disks in a given pool. This increases the potential
     throughput by leveraging I/O from multiple drives at once. Figure 5 shows the disk distribution within
     each chassis.

        Figure 4. RAID Group to LUN Mapping
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        Figure 5. Disk Layout Within Hitachi Virtual Storage Platform Racks.

     RAID Configuration

     The RAID structure used provides for maximum protection of data as well as throughput. The RAID
     group to pool mapping is used to create LUNs for the following purpose:

     ■■Operating   system (OS) pool comprises RAID-5 (3D+1P) for 2 OS drives and 2 drives containing
        mount points for data LUNs.
     ■■Two   high-performance pools (SSD drives) of RAID-1+0 (2D+2P) for tempdbs and logs for each
        SMP.
     ■■Two   pools of RAID-1+0 (2D+2P) (SAS drives) for data, 1 for each SMP.

     The RAID group to pool mapping is provided in Table 3, which illustrates the RAID configuration,
     showing the mapping of RAID groups to pools.
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          Table 3. RAID Configuration showing the mapping
          of RAID groups to pools
           Purpose             RAID Group         Number of   Drive Capacity   Capacity         Number of
                               Configuration      Drives                                        LUs
           Operating System,   RAID-5 (3D+1P)     8           300GB            600GB            4
           Mount Point Disk
           Microsoft           RAID-1+0 (2D+2D)   12          300GB            2TB              2
           Transaction Logs
           for SMP1
           SQL Transaction     RAID-1+0 (2D+2D)   12          300GB            2TB              2
           Logs for SMP2
           PRI Data Pool       RAID-1+0 (2D+2D)   256         300GB            32TB             64
           SMP 1
           PRI Data Pool       RAID-1+0 (2D+2D)   256         300GB            32TB             64
           SMP 2
           Hot Spares SAS      N/A                8           300GB            N/A              N/A
           Hot Spares SSD      N/A                4           300GB            N/A              N/A

     Storage Load Distribution

     The VSP storage configuration utilized 2 storage controller chassis, with each chassis containing
     a controller. Each controller is comprised of multiple processors. The design allocates storage for
     each SMP in its own chassis to permit the resources within that chassis to be applied to the SMP
     containing a SQL instance.

     Each controller has dual components with active-active paths between all components providing
     high throughput and full redundancy. For optimal performance, the routing of data within the control-
     ler is left to the processors in each controller, with the exception of the location of high-performance
     storage (SSDs) relative to lower-performance storage (SAS). It is recommended that the SSD drives
     be located as close to the controllers as possible, with the SAS drives located further away from the
     controller, as illustrated in Figure 5.

     The use of active-active paths allows for alternate path failover.

     Figure 6 depicts the data flow within a controller. Each controller contains 2 clusters or more pre-
     cisely, 2 subcontrollers; if one fails, the other can pick up the load. Within each cluster are duplicate
     components and paths; if a component fails the other will take over, allowing the cluster to continue.
     Both clusters communicate with each other and all paths through the clusters are used to provide
     additional bandwidth for dataflow. All data is duplicated as it flows through the clusters to ensure
     no data loss on a component failure. All components are hot-replaceable on failure. Not shown in
     the diagram are high-speed crossbar switches, which facilitate the flow of data within the controller.
     Also note that both clusters communicate with each other via an extension of the crossbar switch,
     in effect creating a single larger controller and providing for higher bandwidth and reliability.

     The data flow for a write (for example) is to move the data from the Fibre Channel ports to cache.
     The system will acknowledge to the server that the write is complete, permitting the server to con-
19

     tinue executing the application without waiting for the data to be written to the back-end disks. The
     data will then be written from cache through the disk controller modules to back-end storage. The
     use of cache permits a quick response to the I/O operation. Since the cache is backed by battery
     and SSD storage, all data is protected until it can be de-staged, or written, to back-end storage,
     even during a total power outage.

        Figure 6. Data Flow Within Hitachi Virtual Storage Platform from Fibre Channel Ports to
        Actual Storage
20

     SAN Architecture
     The SAN fabric is designed around a switched network utilizing Brocade switches for all data paths
     between VSP and the SMPs. The OS paths are routed through embedded Fibre switches in the blade
     enclosure to VSP.

     Each SMP is connected to VSP through a Brocade switch with no zoning. Eight Fibre connections are
     used between each SMP and Brocade switch, and between the Brocade switch and the VSP. Each
     fibre link is connected to a Brocade 825 HBA port in a PCI-e slot on the SMP, and each link is active.
     LUNs are arranged such that each LUN is visible from 2 links for both performance scaling and failover
     protection. On failure the 2nd link assumes all traffic for the LUN without causing disruption to the ap-
     plication or OS as there is no HBA failover necessary since both links are active.

     Table 4 lists the LUNs and paths for each LUN. Note that since the boot LUNs were not on the data
     paths and were not part of the benchmarking, they did not have duplicate paths for this test. The
     design placed the boot LUNs on a separate path to avoid conflicts with data. The amount of traffic for
     the boot LUNs is minimal and probably would not conflict with data traffic if the boot LUNs were on
     the data paths.

            Table 4. SAN Configuration with LUN purpose and
            path assignments.

                                                                                       Secondary Path /
             LUN                              Purpose                 Primary Path
                                                                                       Failover Path

             SMP1 - Data 0 through Data 7     SQL Database LUNs       2e               2a

             SMP1 - Data 8 through Data 15    SQL Database LUNs       4e               4a

             SMP1 - Data 16 through Data 23   SQL Database LUNs       2a               2e

             SMP1 - Data 24 - Data 31         SQL Database LUNs       4a               4e

             SMP1 - Data 32 - Data 39         SQL Database LUNs       1a               1e

             SMP1 - Data 40 - Data 47         SQL Database LUNs       3a               3e

             SMP1 - Data 48 - Data 55         SQL Database LUNs       1e               1a

             SMP1 - Data 56 - Data 63         SQL Database LUNs       3e               3a

             SMP2 - Data 0 through Data 7     SQL Database LUNs       2q               2l

             SMP2 - Data 8 through Data 15    SQL Database LUNs       4q               4l

             SMP2 - Data 16 through Data 23   SQL Database LUNs       2l               2q

             SMP2 - Data 24 - Data 31         SQL Database LUNs       4l               4q

             SMP2 - Data 32 - Data 39         SQL Database LUNs       1l               1q

             SMP2 - Data 40 - Data 47         SQL Database LUNs       3l               3q

             SMP2 - Data 48 - Data 55         SQL Database LUNs       1q               1l

             SMP2 - Data 56 - Data 63         SQL Database LUNs       3q               3l

             SMP1 Boot LUN                    Boot LUN                2f

             SMP2 Boot LUN                    Boot LUN                2r
21

     Multipathing Options and Settings

     Maintaining a constant connection between the compute server and the storage requires SAN mul-
     tipathing to provide the following:

     ■■Data     redundancy.
     ■■Path     failover mechanism in the event of a link failure.
     ■■Increased     throughput utilizing multiple paths between server and storage.

     To achieve SAN multipathing and provide LUN high availability, one of the following must be used:

     ■■Hitachi    Dynamic Link Manager (HDLM) Advanced.
     ■■Microsoft    MPIO.

     Microsoft MPIO was used for this test. Hitachi Dynamic Link Manager Advanced is another option
     providing multipath active-active and failover support.

     For performance, active-active paths were used in this configuration. All data and log LUNs have an
     active and secondary link. Both links are used for round-robin scheduling to allow rebalancing of the
     load for optimal bandwidth. On failure of a link, both MPIO and HDLM will maintain all traffic on the
     remaining link(s) without loss of data.

     HBA Queue Depth settings

     Setting the proper HBA queue depth is an important tuning parameter to achieve the maximum
     throughput for the solution. It controls the number of disk access requests that are in flight to
     storage.

     Higher queue depth settings are recommended for performance. In this configuration, Brocade has
     hard coded the queue depth to 32 for the HBAs.

     Network Architecture
     Each blade has 2 mezzanine slots on the blade able to contain an I/O mezzanine card. In the con-
     figuration for the customer configuration a 4-port 1Gb network card was installed in 1 mezzanine
     slot. Each blade has 2 onboard 1GB NICs providing a total of six 1Gb NIC ports from each blade.
     With a 4-blade SMP configuration, 24 x 1Gb NIC ports are available.

     The NIC ports between the 2 SMPs are routed within the blade chassis to 4 embedded Ethernet
     switches in the blade enclosure. Each switch contains four 1Gb ports and two 10Gb ports.
     Between the 4 switches are 16x 1Gb ports and eight 10 Gb ports.

     Of the 16 available ports, 2 ports within the switches were used to connect the 2 SMPs to the man-
     agement network. All other ports on the embedded switches are available for data transfer.

     A separate port in the blade management module in the blade enclosure was used to connect the
     blade chassis to the management network. Through the management port on the blade chassis,
     remote management connections are provided to:

     ■■All   embedded switches in the blade chassis (both Ethernet as well as Fibre Channel switches).
     ■■The    blade chassis for management of the chassis components.
22

     ■■Both   SMPs for management of the blades.
     ■■Access   RDP sessions to the server OS consoles via the SMP management port on the blade
        chassis.

     On VSP, a management port was used to connect VSP to the management network. The manage-
     ment port on each Brocade switch was also connected to the management network.

     Figure 7 shows the network connectivity for the compute and storage systems, including the
     management network and management computer, as well as the connection of the SMPs to the
     private LAN.
23

       Figure 7. Private LAN and Management Network Connectivity

     Compute Blade Configuration
     The customer configuration uses:

     ■■Eight   blades (X57A1) configured in 2 four-node SMPs with a crossbar switch connecting the
       blades in each SMP. Each blade contains:
       ■■ One   4-port 1Gb Ethernet mezzanine card.
       ■■ One   4-port 8Gb Fibre Channel HBA mezzanine card.
24

     ■■Each    blade is configured with 192GB RAM providing 768GB RAM per SMP.
     ■■Two     embedded Ethernet switches in the blade enclosure each with four 1Gb ports + two 10Gb
        ports.
     ■■Two     6-port 8Gb Fibre Channel embedded switches in the blade enclosure.
     ■■Eight   Brocade 825 dual-ported 8Gb PCI-e HBAs per SMP (using 1 port per HBA).
     ■■SAN     boot utilizing embedded Fibre Channel mezzanine HBAs, connecting through embedded
        Fibre Channel switches in the blade enclosure.
     ■■Utilizing   Microsoft Windows Server 2008 R2 Datacenter Edition as the OS on both SMPs.

     All modules, including fans and power supplies, can be configured redundantly and hot-swapped.
     This maximizes system uptime. Using the logical partitioning feature, both SMPs are configured
     with 4 blades. No zoning is set up on either the Fibre Channel embedded switches or the Ethernet
     embedded switches.

     All modules, including fans and power supplies, can be configured redundantly and hot-swapped.
     This maximizes system uptime.

     Figure 8 illustrates the configuration of Hitachi Compute Blade 2000 used in this customer configu-
     ration.

        Figure 8. Blade Configuration and I/O Components of the Blade Enclosure

     Engineering Validation
     This describes the methodology and test results used to validate this customer configuration.

     The customer configuration was utilized to demonstrate the scalability of Hitachi Compute Blade
     2000, Hitachi Virtual Storage Platform and Microsoft SQL 2008 R2 with a very large database. Vari-
     ous scenarios were run to characterize the configuration under different loads and varying sizes of
     the simulated database. The results highlighted the following:
25

     ■■This    is not a bottleneck.
     ■■All   components reach optimal performance.

     Test Methodology
     The test methodology used on this customer configuration did the following:

     ■■Provided    a baseline performance benchmark for the solution.
     ■■Determined        the overall solution capability at peak operations.

     Server Testing Scenarios

     The testing methodology was designed to drive various levels of stress over various sizes of files
     representing the database. This action characterizes and validates the capability of the customer
     configuration to scale with both performance and capacity. The performance characterization was
     designed to provide stress on the server, SAN and storage.

     The files were distributed across sixty-four 500GB volumes. Each volume contained a 423GB mdf
     file created by SQLIO. The choice of 423GB was to remain close to 80% utilization of each vol-
     ume. As the data on a volume exceeds 80% of the volume capacity, the performance begins to be
     impacted.

     To validate different sizes of files representing the database, various combinations of volumes were
     used for SMP1, as shown in Table 5. The storage for SMP2 was laid out in the same manner.

       Table 5. DATA VOLUME CONFIGURATION
                                                                                      Ports Used on Hitachi
             Number of Volumes        Capacity in Megabytes            LUNs
                                                                                     Virtual Storage Platform
                    16                          8                  Data0 to Data15        2a, 2e, 4a, 4e

                    32                         16                  Data0 to Data31        2a, 2e, 4a, 4e

                                                                                          2a, 2e, 4a, 4e
                    48                         24                  Data0 to Data47
                                                                                          1a, 1e, 3a,e3

                                                                                          2a, 2e, 4a, 4e
                    64                         32                  Data0 to Data63
                                                                                          1a, 1e, 3a,e3

     The test scenarios included the following type of runs:

     ■■Read     Sequential.
     ■■Read     Random.
     ■■Write    Sequential.
     ■■Write    Random.

     Each test scenario was run with block sizes of 8k, 16k, 32k and 64k, with each run lasting 2
     minutes.

     Each scenario was run against each set of volumes (the "Number of Volumes" column in Table 5
     with a set being 16, 32, 48 or 64 volumes) to simulate a database with the capacity shown in the
26

     table. For each volume in the set of volumes in a given test, a SQLIO process was initiated to ex-
     ercise the appropriate test against that volume. For example, for 16 volumes, 16 SQLIO processes
     were initiated: 1 process against each volume. For each process, runs were made with the number
     of threads set to 1, 2, 4 and 8 to incrementally increase stress against each storage configuration of
     16, 32, 48 and 64 volumes.

     Test Results
     Table 6 shows the results for the Read Sequential test on SMP1. The test started at 16 volumes
     (8TB) with 16 processes and 8 threads per process. The test increased the number of volumes by
     16 for each row to 64 volumes with 64 processes and 8 threads per process.

       Table 6. READ SEQUENTIAL TEST RESULTS
                                            Number of
                            Simulated                                                                          Processor
                                            Processes                                          Average
          Number of         Database                          Throughput                                       Utilization:
                                              Times                              IOPS         Latency in
           Volumes             Size                          (megabytes)                                      Percentage of
                                            Number of                                       (milliseconds)
                           (terabytes)                                                                            CPU
                                             Threads
              16               6.7              128              2,296          36,738           112*                5
              32               13.5             256              2,624          41,987           195*                8
              48               20.3             384              4,515          72,243           179*                88
              64               27.0             512              4,906          78,507           217*               100

       * The high average latencies are due to the inability to constrain the parameters in SQLIO. IOMeter was used to gather
       more realistic latency information. The use of IOMeter and the latency are discussed in latency measured by SQLIO and
       IOMeter.

     Table 7 shows the results for the Write Random test on SMP1 to give a feel for performance where
     more than one type of I/O is performed. The test started at 16 volumes (8 TB) with 16 processes
     and 8 threads per process. The test increased the number of volumes by 16 for each row to 64
     volumes with 64 processes and 8 threads per process as was done for the read random test.

       Table 7. results of the Write Random Test
                                            Number of
                            Simulated                                                                            Processor
                                            Processes                                           Average
          Number of         Database                         Throughput                                          Utilization:
                                              Times                             IOPS           Latency in
           Volumes             Size                         (megabytes)                                          Percentage
                                            Number of                                        (milliseconds)
                           (terabytes)                                                                             of CPU
                                             Threads
              16               6.7              128             1,301          20,819             197*                2
              32              13.5              256             1,471          23,535             362*                3
              48              20.3              384             2,299          36,789             340*                4
              64              27.0              512             2,322          37,164             445*                5

       * The high average latencies are due to the inability to constrain the parameters in SQLIO. IOMeter was used to gather
       more realistic latency information. The use of IOMeter and the latency are discussed in latency measured by SQLIO and
       IOMeter.
27

     Table 8 compares the Read Sequential runs with SMP1 and SMP2. The test started at 16 volumes
     (8TB) with 16 processes and 8 threads per process. The test increased the number of volumes by
     16 for each row to 64 volumes with 64 processes and 8 threads per process.

       Table 8. Hitachi Virtual Storage Platform
       Performance With Both Controllers Under
       Stress from 2 SMPS
                                                     Number of
                                       Simulated                                                                   Processor
                        Number                       Processes                                     Average
                                       Database                      Throughput                                    Utilization:
            SMP            of                          Times                           IOPS         Latency
                                          Size                      (megabytes)                                    Percentage
                        Volumes                      Number of                                   (milliseconds)
                                      (terabytes)                                                                    of CPU
                                                      Threads
           SMP1             16            6.7            128            2,296         36,738           112*              5
           SMP2             16            6.7            128            2,373         37,962           108*              5
           SMP1             32           13.5            256            2,624         41,987           195*              8
           SMP2             32           13.5            256            2,641         42,254           193*             12
           SMP1             48           20.3            384            4,515         72,243           179*             88
           SMP2             48           20.3            384            4,100         65,610           192*             100
           SMP1             64           27.0            512            4,906         78,507           217*             100
           SMP2             64           27.0            512            4,166         66,661           253*             100

       * The high average latencies are due to the inability to constrain the parameters in SQLIO. IOMeter was used to gather
       more realistic latency information. The use of IOMeter and the latency are discussed in latency measured by SQLIO and
       IOMeter.

     Of interest is the effect of hyper-threading on performance. An SMP has 64 cores, with each core
     capable of hyper-threading, giving a total of 128 processors per SMP. Table 9 compares the Read
     Sequential data from SMP1 for hyper-threading "on" and hyper-threading "off."

       Table 9. performance for the Read Sequential TesT
                                                    Number of
                                      Simulated                                                                    Processor
          Hyper-        Number                      Processes                                      Average
                                      Database                      Throughput                                     Utilization:
         Threading         of                         Times                           IOPS          Latency
                                         Size                      (megabytes)                                     Percentage
         Off or On      Volumes                     Number of                                    (milliseconds)
                                     (terabytes)                                                                     of CPU
                                                     Threads
             Off           16            6.7            128            2,296         36,738           112*               5
             On            16            6.7            128            2,286         36,582           111*               2

             Off           32           13.5            256            2,624         41,987           195*               8
             On            32           13.5            256            2,602         41,626           197*               4
             Off           48           20.3            384            4,515         72,243           179*              88
             On            48           20.3            384            4,521         72,339           192*             100
             Off           64           27.0            512            4,906         78,507           217*             100
             On            64           27.0            512            4,466         71,467           235*             100

       * The high average latencies are due to the inability to constrain the parameters in SQLIO. IOMeter was used to gather
       more realistic latency information. The use of IOMeter and the latency are discussed in latency measured by SQLIO
       and IOMeter.
28

     Latency Measured by SQLIO and IOMeter
     Multiple tools exist to provide stress to characterize and test various aspects of a system such
     as the one outlined in this document. Each tool has its strengths and weaknesses. While all tools
     can provide characterizations, the resultant data must be viewed in context. To evaluate the most
     accurate application characterization, the actual application should be run in a production-realistic
     environment.

     To provide a characterization for the configuration used in this paper, the primary tool used was
     SQLIO. SQLIO I/O is designed to provide extensive stress to the storage system in a given con-
     figuration. SQLIO can be tuned to stress storage with reads and writes in a sequential or random
     manner. SQLIO cannot be tuned to provide reads and writes with both sequential and random data.
     SQLIO cannot be fine tuned to reflect a random workload.

     IOMeter is a tool also used to characterize I/O subsystems and has the capability to be tuned more
     closely to reflect a given workload. The load generated by IOMeter can be modeled to reflect both
     reads and writes issued in a more random order, if desired.

     In characterizing the system used here to reflect the customer's configuration, it was found that
     SQLIO was able to put high stress on the system but was not able to be throttled. This resulted in
     good throughput but poor latency numbers.

     To try to provide more realistic latency numbers, IOMeter was used to create a comparable work-
     load on the system used for this paper. In general, an OLTP workload will be composed of 67%
     read and 33% write. To determine the latency for the configuration, a workload profile was created
     with this mix of operations.

     To generate the desired workload, IOMeter was run on the same configuration as was used to char-
     acterize the customer workload for this paper. Sixty-four LUNs were used with each LUN 500GB in
     size. The file used to test on each LUN used 80% of each disk. To generate the workload, a worker
     (a thread) was created for each LUN. The worker was configured to vary the load with 67% reads
     and 33% writes. Multiple parameters were varied in an attempt to demonstrate how the workload
     would affect the performance and latency within the configuration.

     Transaction databases are characterized as using 8k, 16k and 64k buffers. To portray the latency
     more closely, tuned test profiles were created with 4k, 16k, 64k and 128k buffers. The tests used to
     present the data below were all read sequential. They provide a comparison to the read sequential
     tests from SQLIO (as presented for the configuration described in this paper) to be able to gauge
     the differences in latency with a more realistic workload.

     The configuration used comprised a 4-blade SMP system with 64 cores. The storage was a Fibre
     Channel storage system with 64 LUNs composed of 256 spindles configured as RAID-1+0 (2D
     + 2D). One worker was assigned to each LUN. Each worker was a thread. Sixty-four workers (or
     threads) were assigned to a manager in IOMeter. A 2nd manager was created with 64 workers
     (threads), each worker assigned 1 of the 64 LUNs (hence each LUN had 2 workers, 1 from each
     manager). This configuration thus had 128 threads to generate load against the 64 LUNs.

     Table 10 reflects the latency based on the runs made with IOMeter. Various buffer sizes and out-
     standing I/O values were utilized to show the scaling and latency progression. The workload repre-
     sents 128 active threads on 64 processors.
29

              The profiles are 100% read sequential. Data was created using 2 managers, each with 64 workers.
              A total of 64 LUNS were used (the same configuration as was used by SQLIO in this paper).
              Workers were assigned 1 LUN each, while each LUN was shared by a worker from each manager
              reading data from it.

     TABLE 10. IOMETER OF VARIOUS READ PROFILES SHOWS LATENCY
     UNDER DIFFERENT CONDITONS
                    Outstanding
     Block Size                                                                           Transfer Delay
                      I/Os per               Transfer      Average             CPU                         Burst Length (I/
     (kilobytes)                                                                          (milliseconds)
                       Target     IOPS         Rate         Latency         Utilization                     Os) (IOMeter
      (IOMeter                                                                               (IOMeter
                     (IOMeter              (megabytes)   (milliseconds)   (percentage)                       Parameter)
     Parameter)                                                                             Parameter)
                    Parameter)
         4              1         81015       331            1.57              58               0                 1
         16             1         98006       1605            1.3              51               0                 1
         32             1         110760      3629           1.15              36               0                 1
         64             1         82446       5403           1.55              6                0                 1
        128             1         42121       5520           3.03              4                0                 1
         4              4         69385       284            7.37              94               0                 1
         16             4         69728       1142           7.33              92               0                 1
         32             4         68882       2257           7.42              93               0                 1
         64             4         84980       5569           6.02              9                0                 1
        128             4         42673       5593           11.99             4                0                 1
         4              8         69920       286            14.63             98               0                 1
         16             8         69601       1140           14.7              98               0                 1
         32             8         68694       2250           14.89             98               0                 1
         64             8         81582       5346           12.54             25               0                 1
        128             8         42550       5577           24.05             4                0                 1

              Table 11 shows latency with 64 active threads on 32 processors to give a perspective of how the
              configuration scales, based on load and processors.
30

                   TABLE 11. IOMETER PARAMETERS SHOWING LATENCY:
                   64 THREADS, 32 PROCESSORS
                                 Outstanding
                   Block Size                                                                                           Transfer Delay      Burst
                                   I/Os per                   Transfer              Average                CPU
                   (kilobytes)                                                                                          (milliseconds)    Length (I/
                                    Target        IOPS          Rate                 Latency            Utilization
                    (IOMeter                                                                                               (IOMeter      Os) (IOMeter
                                  (IOMeter                  (megabytes)           (milliseconds)      (percentage)
                   Parameter)                                                                                             Parameter)     Parameter)
                                 Parameter)
                       4                1        228447           930                 0.27                 33                 0               1
                       16               1        172008           2810                0.37                 24                 0               1
                       64               1        55917            3660                1.14                 10                 0               1
                      128               1        28348            3710                2.25                 5                  0               1
                       4                4        189924           770                 1.34                 87                 0               1
                       16               4        193272           3600                 1.3                 46                 0               1
                       64               4        56706            3710                4.51                 8                  0               1
                      128               4        28445            3720                8.99                 5                  0               1
                       4                8        207193           840                 2.47                 76                 0               1
                       16               8        207163           3390                2.47                 47                 0               1
                       64               8        57168            3740                8.95                 9                  0               1
                      128               8        28560            3740                17.92                5                  0               1

                            To validate the data from SQLIO and IOMeter are valid and consistent comparable runs were
                            compared.

                            Table 12 provides the results. In both cases, 128 threads were used to generate work against 64
                            LUNs. The data for transfer rate, average latency and CPU utilization were close.

                            Based on the testing methodology, it was found that it was easier to throttle workload and generate
                            more realistic workload using IOMeter.

                            Table 12 provides a comparison of the data acquired from SQLIO and IOMeter. The data was
                            created for 64 LUNS with 128 threads to create a load on 64 processors. The data is relatively
                            consistent, showing that the 2 tools are generating consistent results based on the input parameters
                            selected.

     TABLE 12. COMPARISON OF SQLIO AND IOMETER DATA
                   Outstanding
     Block Size                                           Transfer        Transfer        Average         Average             CPU             CPU
                     I/Os per
     (kilobytes)                     IOPS       IOPS        Rate            Rate          Latency         Latency          Utilization     Utilization
                      Target
      (IOMeter                     IOMeter     SQLIO      IOMeter          SQLIO          IOMeter          SQLIO            IOMeter         SQLIO
                    (IOMeter
     Parameter)                                          (gigabyte)      (gigabyte)     (millisecond)   (millisecond)    (percentage)    (percentage)
                   Parameter)
         8              32          69868      91013        572             711               58.63         54.89             100             98
         16             32          69668      83561       1141            1305               58.24         58.70             100            100
         32             32          69023      79833       2261            2494               59.31         55.20             100            100
         64             32          69988      80862       4586            5054               58.45         54.31             84             100
31

     Conclusion
     The very large database customer configuration from Hitachi Data Systems provides an integrated,
     highly reliable and highly scalable Microsoft SQL Server solution, removing many of the constraints
     imposed by other solutions. This architecture delivered by a single vendor with a history of providing
     highly reliable mission-critical implementations, brings together the design experience and proven
     technologies to keep businesses up and running.

     The Hitachi Compute Blade 2000 provides a highly scalable compute platform. The ability to hot-
     swap and upgrade components without downtime permits capacity scaling while providing continu-
     ous service to clients. The servers within Compute Blade 2000 can be joined together to provide
     larger single-server platforms and address compute needs as the database grows. The drivers
     integrated into the OS and platform provide for dynamic load balancing, reducing the need to manu-
     ally tune the system.

     Hitachi Virtual Storage Platform provides for extensive storage capacity growth and performance
     scaling without downtime. The design of the platform distributes load through multiple balanced
     paths, enabling dynamic load-balancing plus failover capability. VSP's abilities to hot-add new
     capacity and hot-replace failed components enable it to deliver the industry's only 100% uptime
     availability.

     The remote management capabilities built into all components of this system, coupled with Hitachi
     Command Suite and integration with industry-standard Microsoft tools, deliver ease of installation
     and ongoing management while reducing cost and complexity.

     The VLDB customer configuration from Hitachi Data Systems provides a highly scalable and reliable
     solution with long-term investment protection from a company that specializes in global enterprise
     mission-critical business solutions.
32

     Appendix A — Contributors
     The configuration reviewed in this white paper was validated at the Microsoft Enterprise Engineering
     Center on the Microsoft main campus with the cooperation of Microsoft. The architecture is based
     on a Microsoft and Hitachi global account with a large web presence. The account required a large
     backing storage of historical data for immediate queries and a constant flow of new data continu-
     ously updating the historical data.

     The information included in this document represents the expertise, feedback and suggestions of a
     number of skilled practitioners. The authors recognize and sincerely thank the following contributors
     and reviewers of this document:

                         Ralph Lobato — Hitachi Data Systems, Sponsor

                         John Wildes — Hitachi Data Systems

                         Rick Andersen — Hitachi Data Systems

                         Nathan Tran — Hitachi Data Systems

                         David Punnett — Hitachi Consulting
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