Dynamic resource allocation of computer clusters with probabilistic workloads

Real-time resource scheduling is an important factor for improving the performance of cluster computing. In many distributed and parallel processing systems, particularly real-time systems, it is desirable and more efficient for jobs to finish as close to a target time as possible. This work models the execution time for such a stochastic environment and proposes a dynamic algorithm for optimizing the job completion times by dynamically allocating resources to jobs that are behind schedule and taking resources from jobs that are ahead of schedule. We validate our analytical model with simulations that represent the real computing environment. The results of our simulations show that our alternative is the best estimate to predict the time remaining by using earlier data. Emphasis is placed on where variance enters the system and how well it can be controlled. Also our dynamic algorithm involves modifying the architecture to help reduce the peak number of servers used to execute a job and thus optimize the computation cost.