A Cost-Optimized Data Parallel Task Scheduling in Multi-Core Resources Under Deadline and Budget Constraints

Abstract

Large-scale distributed systems have advantages of high processing speeds and large communication bandwidths over the network. The processing of huge real-world data through distributed computing system becomes obscure because the major concern in large-scale distributed systems is to guarantee the completion of data processing task to be done within a budget and time constraints. This paper proposes a cost-optimized data parallel task scheduling in multi-core resources to address the above issue. By running concurrent executions on a multi-core resource, the number of parallel executions could be increased correspondingly, thereby it is able to finish the task within the deadline. A model is developed here to optimize the operational cost of data parallel task by feasibly assigning load fractions to each multi-core resource. This work experimented with data parallel task. The outcome of the work gives better solutions in terms of processing task by deadline at optimised computational cost.