Auto-scaling method in hybrid cloud for scientific applications

Abstract

Scientists can ease to conduct large-scale scientific computational experiments over cloud environment according to an appearance of Science Clouds. Cloud computing enables applications to apply on-demand and scalable resources dynamically. It is necessary for Many Task Computing (MTC) to offer high performance resources in a long phase and certificate stable executions of applications even dramatic changes of vital status of physical resources. Auto-scaling on virtual machines provides integrated and efficient utilization of cloud resources. VM Auto-scaling schemes have been actively studied as effective resource management in order to utilize large-scale data center in a good shape. However, most of the existing auto-scaling methods just simply support CPU utilization and data transfer latency. It is needed to consider execution deadline or characteristics of an application. We propose an auto-scaling method, guaranteeing the execution of an application within deadline. It can handle two types of job patterns; Bag-of-Tasks jobs or workflow jobs. We simulate a variable index computation application in hybrid cloud environment. The results of the simulation show the method can dynamically allocate resources considering deadline.