Parallel Job Scheduling on a Dynamic Cloud Model with Variable Workload and Active Balancing

Abstract

Cloud Computing has been steadily evolving during the past few years with new types of services, based on cloud platforms, being introduced regularly. It becomes apparent that, as the technology and the available tools progress, clouds will be tasked to support increasingly heavier workloads. Efficient ways of scheduling are therefore required in order to effectively utilize the available computing capacity. This capacity, of course, comes at a cost which must be balanced with performance requirements. Thus, the resource leasing system plays an equally important role as the job scheduler. This paper studies a scalable Cloud Computing system modeled around the Amazon EC2 architecture, with a workload model that offers fluctuating traffic characteristics. Different job dispatching heuristics were used in combination with gang-scheduling and job migrations. Also a complex resource manager was incorporated to maintain cost-efficiency. The system was studied through the use of a discrete event simulator in order to assess the effect of different workload configurations and resource leasing schemes on the overall performance and cost of the modeled system. The simulation results, present the advantages and disadvantages of the applied configurations and highlight that different or hybrid strategies are required to achieve the best balance between performance and cost.