Semantic-enabled CARE Resource Broker (SeCRB) for managing grid and cloud environment

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• 作者：Thamarai Selvi Somasundaram (1)
  Kannan Govindarajan (1)
  Usha Kiruthika (1)
  Rajkumar Buyya (2)
  
• 关键词：Cloud computing ; Grid computing ; High ; performance computing (HPC) ; Resource broker ; Semantic description ; Semantic discovery ; Service level agreement (SLA)
• 刊名：The Journal of Supercomputing
• 出版年：2014
• 出版时间：May 2014
• 年：2014
• 卷：68
• 期：2
• 页码：509-556
• 全文大小：
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• 作者单位：Thamarai Selvi Somasundaram (1)
  Kannan Govindarajan (1)
  Usha Kiruthika (1)
  Rajkumar Buyya (2)
  
  1. Anna University, Chennai, India
  2. Cloud Computing and Distributed Systems Lab, The University of Melbourne, Parkville, Australia
  
• ISSN：1573-0484

文摘

Grid computing is mainly helpful for executing high-performance computing applications. However, conventional grid resources sometimes fail to offer a dynamic application execution environment and this increases the rate at which the job requests of users are rejected. Integrating emerging virtualization technologies in grid and cloud computing facilitates the provision of dynamic virtual resources in the required execution environment. Resource brokers play a significant role in managing grid and cloud resources as well as identifying potential resources that satisfy users-application requests. This research paper proposes a semantic-enabled CARE Resource Broker (SeCRB) that provides a common framework to describe grid and cloud resources, and to discover them in an intelligent manner by considering software, hardware and quality of service (QoS) requirements. The proposed semantic resource discovery mechanism classifies the resources into three categories viz., exact, high-similarity subsume and high-similarity plug-in regions. To achieve the necessary user QoS requirements, we have included a service level agreement (SLA) negotiation mechanism that pairs users-QoS requirements with matching resources to guarantee the execution of applications, and to achieve the desired QoS of users. Finally, we have implemented the QoS-based resource scheduling mechanism that selects the resources from the SLA negotiation accepted list in an optimal manner. The proposed work is simulated and evaluated by submitting real-world bio-informatics and image processing application for various test cases. The result of the experiment shows that for jobs submitted to the resource broker, job rejection rate is reduced while job success and scheduling rates are increased, thus making the resource management system more efficient.