Real-Time Adaptive Energy-Scheduling Algorithm for Virtualized Cloud Computing

Abstract

Cloud computing becomes an ideal computing paradigm for scientific and commercial applications. The increased availability of the cloud models and allied developing models creates easier computing cloud environment. Energy consumption and effective energy management are the two important challenges in virtualized computing platforms. Energy consumption can be minimized by allocating computationally intensive tasks to a resource at a suitable frequency. An optimal Dynamic Voltage and Frequency Scaling (DVFS) based strategy of task allocation can minimize the overall consumption of energy and meet the required QoS. However, they do not control the internal and external switching to server frequencies, which causes the degradation of performance. In this paper, we propose the Real Time Adaptive Energy-Scheduling (RTAES) algorithm by manipulating the reconfiguring proficiency of Cloud Computing-Virtualized Data Centers (CCVDCs) for computationally intensive applications. The RTAES algorithm minimizes consumption of energy and time during computation, reconfiguration and communication. Our proposed model confirms the effectiveness of its implementation, scalability, power consumption and execution time with respect to other existing approaches.