Experimental Analysis and Comparison of Load Prediction Algorithms in Cloud Data Center

Due to the increasing scale of cloud data center, the issue of energy consumption is becoming pretty significant. To tackle this problem, an extremely effective approach is increasing the utilization of resource in data center. Researchers have found that accurate load prediction can help allocator distribute resource reasonably, so as to increase the utilization. There are a lot of traditional prediction algorithms which have been applied to cloud data center, such as linear regression. However, with the development of technologies, a number of novel prediction algorithms are brought out, for example, neural network. This paper assesses and analyzes the performance of several different prediction algorithms applying on data sets from real world. We get some meaningful and interesting conclusions from comparison among these algorithms, which may offer references for system designers of cloud data center.