Using Virtualization and Job Folding for Batch Scheduling

In this paper we study the problem of batch scheduling within a homogeneous cluster. In this context, the problem is that the more processors the job requires the more difficult it is to find an idle slot to run it on. As a consequence the resources are often inefficiently used as some of them remain unallocated in the final schedule. To address this issue we propose a technique called job folding that uses virtualization to reduce the number of processors allocated to a parallel job and thus allows to execute it earlier. Our goal is to optimize the resource use. In this paper we propose several heuristics based on job folding and we compare their performance with classical on-line scheduling algorithms as FCFS or backfilling. The contributions of the paper are both on the design of the job folding algorithms and on their performance analysis.