A POWER CONSTRAINED OPTIMIZATION ALGORITHM FOR ENERGY REDUCTION IN CLOUD COMPUTING

Abstract

Cloud computing is a new exemplar for data sharing and storing in cloud centers and it achieves the phenomenal growth for
remote accessing resources via networks. But, succeeding the power consumption controls, concurrently achieving the
performance oriented tasks are the most crucial issues for cloud services. For that, the system is implemented with three
important energy saving schemes for monitoring cloud services and also to reduce the server idle energy consumptions. In
existing, the optimization of energy is done in cloud servers only when the arrival rate is low. By using this EGCM the problems
of server wake up, and cloud system congestion is overcomed, but it cannot able to eliminate the unnecessary idle energy saving
when arrival rate increases and also it cannot allocate resources or switch the resources between idle and sleep status during the
execution of process.. Here, the main objective of the proposed work is to reduce idle energy consumption without sacrificing
performance and without violating (SLA) for that, the system introduces new examining methodology called Artificial
Association Bee [AAB]. The new method is used to solve the constrained optimized problem and support the cloud service
providers or server for energy saving and optimization. The new method reduces unwanted idle energy consumption by
switching idle to sleep modes in an iterative manner, when more tasks are performed in the cloud service execution process.
Such as the new AAB methodology provides an effective server performance for loading or sharing or providing the services to
the cloud clients, and also it achieves energy consumption, with a help of iterative modes. The Simulation results show that
efficient energy reduction is verified by applying energy saving schemes.