A Predictive and Availability-Aware Job Scheduling Algorithm for Resource Management in Cloud

To propose an efficient scheduling algorithm in a large distributed heterogeneousenvironment like cloud, resource (CPU cycles, memory) requirement of jobs must be predicted priorto the execution. An execution history can be maintained to store execution profile of all jobs executedearlier on the given set of resources. A feedback guided job modelling scheme is proposed earlier [1] todetect similarity between newly submitted job and previously executed jobs on that resource set. Basedon the similarity the new jobs are categorized as either an exact clone or near-miss clone or miss-cloneto the history jobs. However, in [2], it is shown that the actual resource consumption, and predictedresource requirement may differ to a great extent, especially for the near-miss-clone and miss-clone jobs.Furthermore, efficient resource scheduling based on the similarity of new jobs has not been addressedin [2]. Some studies show that even if the resource requirements of jobs are predicted accurately, it isnearly impossible to predict the actual execution time on a given resource, and actual execution time isonly available after the completion of the job [3]. Ignoring uncertain facts at the time of scheduling maylead to unsuccessful completion of jobs, especially, where resources are available for the limited periodof time, like in the case of cloud. In this work, we propose an efficient scheduling approach that selectsa resource for a job based on two critical criteria. Firstly, the selected resource is evaluated to ensurea faster completion time. Secondly, the availability of the resource until the completion of the assignedjobs is ensured. In addition, this work proposes optimization of these two criteria during the resourceselection process. Finally, we compare the efficiency of our scheduling algorithm with some well-knownjob scheduling algorithms.