Efficiency-Aware Workload Optimizations of Heterogeneous Cloud Computing for Capacity Planning in Financial Industry

Abstract

The broad implementation of cloud computing has brought a dramatic change to multiple industries, which derives from the development of the Internet-related technologies. This trend has enabled global enterprises to apply distributed computing techniques to reach many benefits. An effective risk management approach is required for service deliveries and a capacity planning is considered one of the convincing methods for financial industry. However, executing a capacity planning is still encountering a great challenge from bottlenecks of the Web server capacities. The unstable service demands often result in service delays, which embarrasses the competitivenesses of the enterprises. This paper addresses this issue and proposes an approach, named Efficiency-aware Cloud-based Workload Optimization (ECWO) Model, using greedy programming to predict server workloads of heterogeneous cloud computing in financial industry. The main algorithms used in the proposed model are Task Mapping Algorithm (TMA) and Efficiency-Aware Task Assignment (EATA) Algorithm. Our experimental evaluations have examined the performance of the proposed scheme.