Improved differential search algorithm based dynamic resource allocation approach for cloud application

Abstract

The performance of a service-based system (SBS) in a cloud environment may not satisfy service-level agreement (SLA) constraints when the system load changes. To improve the profits of resource providers and satisfy the global SLA, it is necessary to dynamically allocate proper resource for SBS based on the forecasted system load. By analyzing the complex workflow of the SBS, this paper proposes improved differential search algorithm-based dynamic resource allocation approach which adopts an active mechanism to respond to the change of system load so as to ensure the timely response to change. The dynamic resource allocation model based on costs optimization and SLA constraint is then proposed. The improved differential search algorithm is designed to solve the dynamic resource allocation model. This paper proposes a load forecasting approach based on deep belief networks (DBNs) in order to accurately forecast the load to support dynamic resource allocation. Experimental results show that the approach performs well in terms of the quality of the solution compared with other related approaches.