CDAC Scientific Cloud: On Demand Provisioning of Resources for Scientific Applications
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CDAC Scientific Cloud: On Demand Provisioning of
Resources for Scientific Applications
A. Payal Saluja, B. Prahlada Rao B.B, C. Ankit Mittal and D. Rameez Ahmad
SSDH, Centre for Development of Advanced Computing
C-DAC Knowledge Park, Bangalore, Karnataka, India
resource utilization and allows scientists to scale up to solve
Abstract - Scientific applications have special requirements
larger science problems. It also enables the system software
of availability of a massive computational power for
to be configured as needed for individual application
performing large scale experiments and huge storage
requirements.For research groups, cloud computing will
capacity to storage terabyte or petabyte range of outputs.
provide convenient access to reliable, high performance
Scientific Cloud provides scientists computational, storage
clusters and storage, without the need to purchase and
and network resources with a inbuilt capability of utilizing
maintain sophisticated hardware. It has been said by Pete
the infrastructure. The scientific applications can be
Beckman, director of Argonne’s Leadership Computing
dynamically provisioned with the required cloud solutions
Facility that “Cloud computing has the potential to
that are tailored to the application needs. Centre for
accelerate discoveries and enhance collaborations in
Development of Advanced Computing (CDAC) under
everything from providing optimized computing
Department of IT, is the pioneer in HPC in India with ~70TF
environment for scientific applications to analyzing data
compute power. The authors of this paper have discussed the
from climate research, while conserving energy and lowering
need and benefits of scientific cloud. Authors have explained
operational costs”. However, there are various challenges of
the model, architecture and components of CDAC scientific
HPC on demand [29] like performance, power consumption
cloud. CDAC HPC resources can be provisioned on-demand
and collaborative work environments. In this approach
to the scientific research community and released when they
paper, we present the concept of scientific clouds, HPC as a
are not required. For Indian researchers and scientists,
service and its benefits to the scientific research community.
CDAC scientific cloud model will provide convenient access
The authors also propose a prototype for CDAC scientific
to reliable, high performance clusters and storage, without
cloud that will provide the following offerings
the need to purchase and maintain sophisticated hardware.
I. Infrastructure as a Service(IaaS)[18] by providing
traditional MPI enabled HPC with parallel file
Keywords: HPC, HPC as a Service, Map Reduce, Cloud
system like GlusterFS[19] and by provisioning
Vault
Hadoop[20] clusters with map reduce[21] with the
support of Hadoop distributed file
system(HDFS)[20].
1 Introduction II. Storage as a service (StaaS) [22] to provide petabytes
High Performance Computing (HPC) allows scientists of data storage to the scientific communities.
and engineers to solve complex science, engineering and
business problems using applications that require high The rest of the sections of this paper are organized as
bandwidth, low latency networking, very high compute and follows: Section 2 describes the concept of HPC as a Service,
storage capabilities. Scientists in the areas of high-energy the challenges of HPC on cloud and how cloud computing
physics [13], astronomy [14], climate modeling [15], chemo benefits the scientific community. Section 3 talks about the
informatics [16] and other scientific fields, require massive other scientific cloud projects and their objective and the
computing power to run experiments and huge data centers relevant work. Section 4 details about the CDAC scientific
to store data. Typically, scientists and engineers must wait cloud and its offerings. Section 5 details the proposed model
in long queues to access shared clusters or acquire expensive and architecture for the CDAC scientific cloud. Section 6
hardware systems. talks about the applications that will be enabled on CDAC
scientific cloud. Section 7 concludes and tells about the
Cloud computing [17] is a model for on-demand access to a future plan of the work.
shared pool of configurable computing resources (e.g.,
networks, servers, storage, applications, services, and
software) that can be easily provisioned as and when needed.
Cloud computing aggregates the resources to gain efficient2 HPC as a Service on Cloud • Reduction in overall Job execution time: Jobs will
be scheduled using intelligent data aware job
Bringing HPC facilities to cloud will provision the scheduling algorithms.
scientists and researchers with a crucial set of resources and
enable them to solve large-scale, data-intensive, advanced
computation problems on research topics across the Figure 1 depicts the layered architecture of scientific cloud.
disciplinary spectrum. HPC as a service is an on-demand The lowest layer of the stack is the physical resources
provisioning of high-performance, scalable HPC (compute, storage and network) that will be connected
environment with high-density compute nodes and huge through a high speed link. The first software layer above the
storage on high performance interconnects like Infiniband physical hardware is the host operating system. Since
[4] and Myrinet [5]. HPC as a service is provisioned to meet scientific cloud will be catering HPC applications,
the HPC application demands, whether one server (Virtual performance of such applications on such infrastructure will
machine) or a large cluster (Virtual cluster). A Virtual be of prime importance. Hence, Type 1 or bare-metal
cluster is a collection of Virtual Machines configured to hypervisor should be preferred for virtualization that will
interact with each other as a traditional Linux cluster. run directly on the host's hardware to control the hardware
Scientific cloud or HPC as a Service enables greater systems and to manage the guest operating systems.
flexibility and eliminates the need for dedicated hardware
resources per applications and would help researchers cope
with exploding volumes of data that need to be analyzed to `SCIENTIIFC APPLICATIONS (bioinformatics,
Climate modeling)
yield meaningful results. It also simplifies usage models and
enables dynamic allocation per given task.
[6] Described a demonstration of a low-order coupled A User Interfaces ( APIs,Web Interface
atmosphere-ocean simulation running in parallel on an EC2 U Mobile Interface, Portals, Workflows &
T PSE ) Mana-
system. It highlights the significant way in which cloud H gement
SaaS PaaS IaaS
computing could impact traditional HPC computing (Clusters -
paradigms. The results show that the performance is below MPI & MR SLA &
& , storage) Policy
the level seen at dedicated, supercomputer centers, however,
performance is comparable with low-cost cluster systems. Mana-
Also it has been concluded that it is possible to envisage S Cloud Middleware Software Stack
gemen
E (Resource provisioning, scheduling, File t
cloud systems more closely targeted to HPC applications,
C system, monitoring)
that feature a specialized interconnect such as Myrinet or U Acco-
Infiniband. R
unting
Scientific Cloud benefits to the Scientists & research I
T
Operating System/Hypervisors
Community: Y
• Dynamic Provisioning of HPC Clusters: Access to Meteri
on-demand cloud resources enables automatic Compute Network Storage ng &
Billin
provisioning of additional resources from the HPC
g
service to process peak application workloads,
Ethernet and INFINIBAND
reducing the need to provision data center capacity (10 -20Gbps interconnect)
according to peak demand. Hence, scientists will
benefit from the ability to scale up and down the
computing infrastructure according to the
application requirements and the budget of users.
• Virtual ownership of resources : Virtual Figure 1 Scientific Cloud Architecture
ownership of cloud resources will reduce
uncertainty concerning access to those resources A guest operating system will run on another level above the
when you need to use them hypervisor. Hypervisor actually controls the host processor
and resources, allocating what are needed to each operating
• Ease of deployment and access: The use of virtual
system in turn and making sure that the guest operating
machine images offers the ability to package the
exact OS, libraries, patches, and application codes systems (called virtual machines) cannot disrupt each other.
The virtualized resources include the basic cloud computing
together for deployment. Scientists can have easy
services such as processing power, storage, and network. The
access to large distributed infrastructures and
completely customize their execution environment, Cloud middleware software stack is the key component that
handles resource provisioning and scheduling, volume
thus providing the perfect setup for their
management, system monitoring for all the higher-level
experiments.
components and services.Cloud management is a crucial component as it monitors and provides two major functionalities of Compute Grids and In-
manages all the cloud resources at physical and virtual level. Memory Data Grids
The various management components that will be part of
scientific cloud are : Resource Inventory search, Hardware 3.4 StratusLab
monitoring & Management , Storage maps and reports,
Alerts & notifications with automated rectification, Stratus Lab [31] is developing a complete, open-source
accounting and billing(to recover costs, capacity planning to cloud distribution that allows grid and non-grid resource
ensure that consumer demands will be met) , policy centers to offer and to exploit an Infrastructure as a Service
management & SLA(Service level Agreements- management cloud. It basically enhances the grid infrastructure with
to ensure that the terms of service agreed to by the provider virtualization and cloud technologies. It is particularly
and consumer are adhered to, and reporting for focused on enhancing distributed computing infrastructures
administrators). such as the European Grid Infrastructure (EGI).
Each of the above mentioned projects focuses either on
provisioning data centers on cloud or compute power on
3 Science Cloud Projects cloud. Amazon Web Services alone provisions the various
The following are some of the science cloud projects have services required for variety of HPC applications like
been executed in the direction to achieve HPC as a Service: Amazon Elastic Compute cloud EC2, Amazon Elastic Map
Reduce (EMR), Amazon Simple Storage Service (S3) [32].
CDAC Scientific cloud is an effort to provide the services
3.1 Cumulus like compute and storage for the HPC community along with
the software technologies like map reduce, MPI , mobile
Cumulus [2] is a project to build a Scientific Cloud for a applications that will accelerate discoveries and enhance
Data Center. It is a storage cloud system that adapts existing collaborations in science.
storage implementations to provide efficient
upload/download interfaces compatible with S3.It provides 4 CDAC Scientific Cloud (CSC)
features such as quota support, fair sharing among clients,
and an easy to- use, easy-to-install approach for C-DAC [7] is the pioneer in HPC in India and its HPC
maintenance. The most important feature of Cumulus is its facilities on cloud can be linked by a 1 Gbps National
well-articulated back-end extensibility module. It allows Knowledge Network (NKN) [8], developed by NIC. The
storage providers to configure Cumulus with existing bandwidth offered by NKN will facilitate rapid transfer of
systems such as GPFS [9], PVFS [10], and HDFS [11], in data between geographically dispersed clouds and enable
order to provide the desired reliability, availability or scientists to use available computing resources regardless of
performance trade-offs. Cumulus is part of the open source location. In addition, CDAC Scientific cloud will provide
Nimbus toolkit [12]. Cumulus is implemented in the python data storage resources that will be used to address the
programming language as a REST service. The Cumulus challenge of analyzing the massive amounts of data being
API is a set of python objects that are responsible for produced by scientific applications and instruments. Storage
handling specific user requests. as a service is of particular importance to scientific research,
where volumes of data produced by one community can
3.2 OpenCirrus reach the scale of terabytes per day .CDAC will make the
Open Cirrus [3] tested is a collection of federated datacenters Scientific cloud storage available to science communities by
for open-source systems and services research. It is designed aggregating a set of storage servers .It will make use of
to support research into the design, provisioning, and advanced technologies to provide fast random access storage
management of services at a global, multi-datacenter scale. It to support more data-intensive problems. The test bed will be
is designed to encourage research into all aspects of service a mix of virtual clusters and storage options, traditional HPC
and datacenter management. cluster, Hadoop cluster, distributed and global disk storage,
archival storage. The system provides both a high-
3.3 GridGain bandwidth, low-latency InfiniBand network as well as a
commodity Gigabit Ethernet network. This configuration is
GridGain[30] is Java based open source middleware for real different from a typical cloud infrastructure but is more
time big data processing and analytics that scales up from suitable for the needs of scientific applications.
one server to thousands of machines. It enables the Using CDAC Scientific cloud instances, users can expedite
development of compute and data intensive High their HPC workloads on elastic resources as needed .Users
Performance Distributed Applications. Applications can choose from Cluster Compute or Cluster Hadoop
developed with Gridgain can scale up on any infrastructure - instances within a full-bisection high bandwidth network for
from a single Android device to a large cloud. Gridgain tightly-coupled and IO-intensive workloads or scale out
across thousands of cores for throughput-orientedapplications. This will let scientists focus on running their online and through mail about their login credentials and the
applications and crunching or analyzing the data generated IP address for the ssh access to the compute cluster. The
by applications without having to worry about time- allocation of the cluster and its nodes (master & worker
consuming set-up, management or tuning of clusters or nodes) will depend upon the CPU, memory, IO requirements
storage capacity upon which they sit. Users will be able to of the application. The applications that will need more of
run HPC applications on these instances including molecular data processing and less of communications will provided
modeling, genome sequencing & analysis, and numerical with the best suited map reduce cluster. The applications
modeling across many industries including Biopharma, Oil that are more compute and memory intensive will be
and Gas, Financial Services and Manufacturing. In addition, provisioned by the MPI enabled clusters with parallel IO
academic researchers will be able to perform research in facility.
physics, chemistry, biology, computer science, and materials
science.
Following will be the supported features of CDAC High Physical Compute
Resource pools Storage
Performance Computing as a service (HPCaaS): Nodes
Dynamic Provisioning of clusters :On demand Hyperviso
Infiniband
Provisioning MPI and Map reduce clusters to Hyperviso
support compute intensive and data intensive Hyperviso Image
Repository
applications
On-demand dynamic provisioning of storage
volumes: Dynamic provisioning of clusters and Parallel File System
User Virtual cluster (GlusterFS)
storage will be handled by the Cloud Resource Request from request
cloud portal
Broker (CRB) or cloud metaschedular.
Security : Simple , Secure and quick access to HPC
Security Cloud resource broker Infiniband /
clusters module And scheduler Ethernet
Provisioning of customized libraries, softwares Interconnec
t
workflows ,etc on HPC clusters as per the
applications requirement Users will be provided Ssh access
with an option of selecting the specific MPI versions to cluster
or compiler versions to suffice the application
requirements
Performance: To reduce the hypervisor overhead
type-1 kind of hypervisor will be used. The
Virtual Cluster
distributed locations will be connected with 1Gbps MPI/ Map Reduce
link and within the site nodes will be connected
with infiniband interconnect to reduce the
latencies.VM allocation to form a cluster will be Figure 2 Infrastructure as a Service (IaaS)
done by the cloud scheduler based on nearness to
storage nodes to minimize the data movement on
cloud. 4.2 Storage As a Service (StaaS)
Following are the services that will be provisioned on the
CDAC Scientific Cloud A service of supplying data storage capacity over Internet is
Storage as a Service. In context of scientific cloud, StaaS
provisions petabytes of data storage to the scientific
4.1 Infrastructure as a Service (IaaS) communities. CDAC’s Cloud Vault based on OpenStack
C-DAC has its HPC facilities at various CDAC Swift Object Storage software will provide scientists and
locations like Bangalore, Pune, Chennai, and Hyderabad researchers partners with a convenient and affordable way to
with approximately 70TF. Figure 2 depicts the prototype store, share, and archive data, including extremely large data
model for dynamic provisioning of the computational sets. CDAC Cloud Vault is an object based storage system
resources when requested by the user. Users will be able to and multiple interface methods make the Cloud Vault easy to
access the CDAC scientific cloud services through cloud use for the average user. It also provides a flexible,
portal. First time users will have to register with their configurable, and expandable solution to meet the needs of
required details and also the details about the kind of more demanding applications. In this, files (also known as
applications they want to run on the cluster. Based on the objects) are written to multiple physical storage arrays
type of the application mentioned by the user resources will simultaneously, ensuring at least two verified copies exist on
be allocated by the cloud broker and the cluster instance will different servers at all times. Figure 3 depicts the flow of the
be created on the fly. Immediately user will be intimated Storage as a service (StaaS).The user registers himself byproviding the required details and the required amount of Cloud Vault will also be accessed by mobiles using mobile
storage. After the users request gets validated and approved, application for the basic file operations like list, upload,
user is sent the access details of the storage through email. download, and synchronize. There will also be a facility to
The various interfaces through which user can access Cloud auto synchronize users mobile with his cloud vault files so
Vault are as follows: that he can keep his mobile backup on cloud vault.
4.2.1 Web Interface
Web interface will allow access to the cloud vault files 4.2.5 APIs
through browser. User will be able to list, create containers, Files of any size can be stored in the Cloud Vault, from small
Upload/Download files, and Delete files using this interface. personal document collections to multi-terabyte backup sets
There will not be any need of installation of any clients to routed directly to the cloud using Rack space or S3 API in
access cloud files. applications.
4.2.2 Desktop GUI Application 5 CSC Architecture and Components
Cloud Vault files will be accessible using open source Figure 4 depicts the components of CDAC scientific cloud.
desktop application called cyberduck. It is an FTP-like stand The various components of CDAC scientific cloud are as
alone GUI application for accessing files. It supports file/ follows:
directory listing, upload, download, synchronize, editing, etc.
Cyberduck is a open source desktop application available for
MAC and Windows system 5.1 Hypervisor
1 Registration A hypervisor, also known as a virtual machine
Request manager/monitor (VMM), is computer hardware platform
virtualization software that allows several operating systems
Cloud Vult to share a single hardware host. The hypervisor controls the
web
Interface host processor and resources so that systems/virtual
machines are unable to disrupt each other. As virtualization
2 credentials adds overheads to the cluster performance, we choose to use
are sent via
email 3 Login with type-1 or bare-metal hypervisors for virtualization. Type-1
credentials
hypervisors run directly on the host's hardware to control the
hardware and to manage guest operating systems. Some of
CDAC the examples of type-1 hypervisors are Citrix XenServer
4 Access Cloud
vault Files CloudVault [24], VMware ESX/ESXi [25], and Microsoft Hyper-V
Storage
hypervisor. CDAC scientific cloud will be using Xen
5 Access GUI desktop
Files application to access
hypervisor for the same.
Through CDAC Cloud Vault
Desktop GUI
4 Access Files
5.2 Cloud middleware
Through mobile Cloud Middleware or Cloud OS: Cloud middleware is the
software stack for provisioning the large networks of virtual
machines on demand. It also handles scalability &
Cloud Vault
reliability of the resources provided to the users. There are
mobile Interface various open source & commercial cloud middleware
available like Nimbus [12], Open Nebula [26], and vCentre
Figure 3 Storage as a Service (StaaS) [27], Eucalyptus [28].
4.2.3 Command Line 5.3 Cloud resource broker
Cloud Resource Broker and Meta scheduler: Cloud resource
Command line access will allow the access to cloud vault broker is a common gateway to provision access to the HPC
files with the UNIX shell. Client installation of the scripts resources like compute clusters, storage on cloud .It is an
needs to be done on the user machine or laptop. intelligent scheduler that will provision the best pool of
available resources to the users by using policy based
4.2.4 Mobile Interface decision. The various components that will build up a cloud
resource broker are as follows:5.3.1 Resource Discovery 5.4 Cloud Management and Monitoring
Resource discovery of the available resources based on the
kind of user application that can be Compute intensive or Cloud Infrastructure monitoring & management tool is the
Data intensive or Memory Intensive control point for the virtual environment in cloud. This tool
will provide a single point access for administrators to
monitor & manage the resources of cloud. The following
HDFS / Glusterfs Storage for Hadoop features that will be supported :
Cloud Resource Inventory search: inventory including
Management virtual machines, hosts, data stores, and networks at
Hadoop Hadoop & Monitoring
Data
Node
Hadoop
Data
Data
Node tool
the administrators fingertips from anywhere
Node
Hardware monitoring & Management
ETHERNET / InfiniBand Storage maps and reports: Provides storage usage,
INTERCONNECT connectivity and configuration. Customizable
Map Reduce MPI enabled topology views give you visibility into storage
Virtual Cluster Virtual Cluster infrastructure and assist in diagnosis and
Cloud
troubleshooting of storage issues.
Vault
Portal Alerts & notifications with automated rectification
Gluster Utilisation, Performance & Energy Consumption
Mount
over Trends
Infiniband
Accounting and billing (to recover costs, capacity
planning to ensure that consumer demands will be
met), Policy management & SLA.
Cloud Broker or Automated dynamic
Meta Scheduler provisioning scripts
Virtual Machines
Cloud Middleware
5.5 Cloud Portal
Virtual Virtual Cloud vault
Storage As 5.5.1 Portal for IaaS Provisioning and Problem Solving
Machines Machines
Cloud Cloud A Service
Cloud Environments (PSE)
Middleware Middleware Middleware
Openstack Openstack Openstack The scientific cloud portal will be the access point for
Nova Nova SWIFT the users for requesting & accessing the on demand HPC
Hypervisor Hypervisor clusters. There will also be customized PSEs for
bioinformatics & climate modeling domains that will
provide the complete environment and workflow for the
domain specific applications.
Ethernet 5.5.2 Portal for Storage as a Service
In house development Storage
The portal for storage as a service will an access point
Node Storage
Storage Node
for the cloud storage through which user can register
Open source tool Node
himself and ask for the required amount of storage .Also
user will be allowed to request for expanding the
allocated storage on the fly
Figure 4 Components of CDAC Scientific Cloud (CSC)
5.3.2 Policy based resource selection 6 Target Applications on CDAC
Resource selection and provisioning will be done Scientific Cloud
considering the various aspects like Load balancing,
resources utilization, power aware. On-demand cloud computing can add new dimension
to HPC, in which virtualized resources can be sequestered,
5.3.3 Data aware Job scheduling in a form customized to target a specific application
requirement, at any point of time. [6] Described the
Data aware scheduling enables computation to be done
feasibility of running Coupled Atmosphere-Ocean Climate
nearest to the location of the data .In this case, the cloud
Models on an EC2 computing cloud and found that the
resource broker will talk to the cloud file system components
performance is below the level seen at dedicated clusters.
to find out the nearest storage nodes where data resides
However, cloud systems that feature a specializedinterconnect such as Myrinet or Infiniband and support MPI [9] http://www.darwinproject.mit.edu/wiki/images/2/2e/Gpfs
or Map reduce are more closely targeted to HPC _overview.pdf
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require the full MPI model for high performance parallel Robert Chansler,
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monsoon rainfall in advance of a season. It involves the
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KDash_HPC-USA-final.pdf
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l
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