Resource requirement prediction using clone detection technique
In order to maintain the QoS requirements of jobs running on a large distributed system, like Cloud and Grid environments, resource requirements of jobs should be predicted prior to their submission, and on the basis of this prediction, appropriate resources can be selected for their execution. However, because of the dynamic and heterogeneous nature of the modern distributed systems, estimation of resource requirements is a challenging task. This paper presents a feedback-based job modeling scheme based on clone detection technique. In this scheme, the execution data for each job which runs in the environment is stored in Execution History. A newly submitted job is analyzed to find its clones from the execution history and on the basis of the data stored in the execution history, the resource requirement of the new job is predicted. Different levels of clones are discussed in this paper and a metric-based clone detection technique is presented. An automatic resource requirement prediction scheme for jobs is proposed. The paper also evaluates a preliminary implementation of the scheme and discusses the results of using the scheme for some test codes.
job modeling resource management code cloning software metrics