Dynamic resource scaling in cloud using neural network and black hole algorithm

Cloud computing has gained much attention in recent years. In spite of several advantages, cloud computing involves a number of issues such as dynamic resource scaling and power consumption. These factors lead a cloud system to be inefficient and costly. Workload prediction is one of the factors by which the efficiency of a cloud can be improved and operational cost would be reduced. In this paper, we present a workload prediction model using neural network and black hole algorithm. The experiments were performed on the benchmark data sets of HTTP traces from NASA, Calgary and Saskatchewan web servers. We achieved an improvement on mean squared error upto 134 times over back propagation.