An Adaptive Techniques for energy and performance efficient in cloud data ceneters.

Abstract

In order to reduce energy consumption, VM live migration has four steps: overloading detection, under loading detection, VM migrate selection and VM placement. Our work is mainly about under loading detection. By switching the hosts into sleep mode which are defined as under loading host, we can reduce a huge amount of energy consumption. Thus, detecting theUnder loading host is the key approach to reduce energy consumption. The Minimum Utilization algorithm(MU) is a simple under loading detection algorithm, which defines the host with minimum CPU utilization as the under loading host. a dynamic annexed balance technique to solve the issue of static load balancing and estimate the capacity of load balancing. The Cloud Load Balancing (CLB) was majorly concentrated on both computer loading as well as server processing. By using CLB, a server would unable to handle excessive computational requirements. An algorithm was applied to both virtual web servers and physical servers. The results showed that cloud server performance based on the architecture balanced the loading performance when users logged in at the same time. The algorithm was simple and very efficient compared to the existing methods. The method suffered from the time complexity which was a major drawback. The previous work on dynamic power management may be classified into two as with or without power optimization and the first case may be further subdivided into two depending on whether or not optimization is joint over the servers and network power consumption. Classification may also include other parameters such as workload and server heterogeneity awareness and VM migration.