Exploiting Resource Overloading Using Utility Accrual Approach for Parallel Data Processing in Cloud

Parallel Data processing has emerged to be one of the killer applications for Infrastructure-as-a-Service (IaaS) clouds. One of an IaaS cloud’s key feature is the provisioning of compute resources on demand. The computer resources available in the cloud are highly dynamic and possibly heterogeneous. Nephele is the first data processing framework to explicitly exploit the dynamic resource allocation offered by today’s IaaS clouds for both, task scheduling and execution. Particular tasks of a processing job can be assigned to different types of virtual machines which are automatically instantiated and terminated during the job execution. However, Nephele is not considering resource overload or underutilization during the job execution. In this paper, a novel utility accrual scheduling algorithm is proposed for scheduling the real-time cloud computing services. The most unique characteristics of this approach is that, different from traditional utility accrual approach that works under one single Time Utility Function (TUF), which have two different TUF’s, a profit TUF and a penalty TUF – associated with each task at the same time, to model the real-time applications for cloud computing, that need not only to reward the early completions and also to penalize the missing abortions or deadline misses of realtime tasks. To improve the performance of cloud computing, the traditional Utility Accrual (UA) approach is deployed in both Non-Preemptive and Preemptive scheduling. The experimental results shows that the proposed algorithm can outperform the existing Nephele framework and also compare the performance between Non-Preemptive and Preemptive scheduling