TY - GEN
T1 - A distributed cloud resource management framework for High-Performance Computing (HPC) applications
AU - Govindarajan, Kannan
AU - Kumar, Vivekanandan Suresh
AU - Somasundaram, Thamarai Selvi
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/6/16
Y1 - 2017/6/16
N2 - The High-Performance Computing (HPC) users require the high-end compute, storage, and, network execution environment in a dynamic manner for testing HPC applications. The High-Performance Computing Cloud (HPCC) provides a kind of execution environment in an on-demand manner. Generally, the cloud resource management system or cloud resource broker manages the compute, storage, and network resources in HPCC. However, it faces the challenges of scalability, interoperability, and achieving guaranteed Quality of Service (QoS). Hence, the proposed research work addresses the above-said issues by employing the distributed cloud resource management framework. The proposed system can be able to handle a large number of user application requests and manage the multiple cloud resources in an interoperable manner. The proposed system is evaluated by submitting a large number of real-world HPC applications. The performance metrics such as response time, a number of successfully handled requests, and user satisfaction are measured to evaluate the performance of proposed system.
AB - The High-Performance Computing (HPC) users require the high-end compute, storage, and, network execution environment in a dynamic manner for testing HPC applications. The High-Performance Computing Cloud (HPCC) provides a kind of execution environment in an on-demand manner. Generally, the cloud resource management system or cloud resource broker manages the compute, storage, and network resources in HPCC. However, it faces the challenges of scalability, interoperability, and achieving guaranteed Quality of Service (QoS). Hence, the proposed research work addresses the above-said issues by employing the distributed cloud resource management framework. The proposed system can be able to handle a large number of user application requests and manage the multiple cloud resources in an interoperable manner. The proposed system is evaluated by submitting a large number of real-world HPC applications. The performance metrics such as response time, a number of successfully handled requests, and user satisfaction are measured to evaluate the performance of proposed system.
KW - Cloud Computing
KW - Distributed Hash Table (DHT)
KW - Distributed Resource Management
KW - High-Performance Computing
KW - Semantic Description and Discovery
UR - http://www.scopus.com/inward/record.url?scp=85025127077&partnerID=8YFLogxK
U2 - 10.1109/ICoAC.2017.7951735
DO - 10.1109/ICoAC.2017.7951735
M3 - Published Conference contribution
AN - SCOPUS:85025127077
T3 - 2016 8th International Conference on Advanced Computing, ICoAC 2016
SP - 1
EP - 6
BT - 2016 8th International Conference on Advanced Computing, ICoAC 2016
T2 - 8th International Conference on Advanced Computing, ICoAC 2016
Y2 - 19 January 2017 through 21 January 2017
ER -