Optimization of Sales Data Forecasting Computation Process Using Parallel Computing in Cloud Environment

The Holt-Winters Exponential Smoothing algorithm optimised using the Modified Improved Particle Swarm Optimization (MIPSO) algorithm is an algorithm that is able to provide good sales data forecasting results. However, there is a problem that when the iteration process is carried out using 1 computer, it takes a long time to finally get the test results. It is necessary to optimise the computational process to get more optimal and efficient results. This research will combine parallel computing technology and cloud computing technology to help speed up the computing process. The results of this research show that the more server used, the greater the reduction in execution time that occurs, because heavy computing tasks can be distributed more efficiently to many machines. This is evident from the comparison between single server and parallel server. Then the combination of more cores and servers produces the most optimal configuration in accelerating computation.