Cloud Computing Predictive Resource Management Framework Using Hidden Markov Model

Abstract

Volunteer and cloud computing are heterogeneous environments that aggregate the capabilities of their resources to solve large scale computationally-intensive problems and provide various services to users. Due to the dynamic nature of these environments, performance states of resources rapidly change, making elasticity characteristic and task allocation very challenging aspects. In order to implement a scalable elastic mechanism while utilizing the resources efficiently and maintaining the overall balance of these systems, real-time performance data need to be collected periodically. However, data collection may significantly increase the communication overhead in the cloud and volunteer network and consume from the limited processing power, energy and bandwidth of resources. Accordingly, this paper proposes solutions for balancing the load while reducing the communication overhead. A reactive and proactive resource auto-scaling task allocation algorithms are proposed. The proactive auto-scaling algorithm is based on the Hidden Markov Model (HMM). Performance evaluation using computer simulations show that the proposed algorithm achieves high prediction accuracy, enhances the overall system utilization and significantly decreases the communication overhead.