Performance evaluation of metaheuristics algorithms for workload prediction in cloud environment

The smooth operation of a cloud data center along with the best user experience is one of the prime objectives of a resource management scheme that must be achieved at low cost in terms of resource wastage, electricity consumption, security and many others. The workload prediction has proved to be very useful in improving these schemes as it provides the prior estimation of upcoming demands. These predictions help a cloud system in assigning the resources to new and existing applications on low cost. Machine learning has been extensively used to design the predictive models. This article aims to study the performance of different nature-inspired based metaheuristic algorithms on workload prediction in cloud environment. We conducted an in-depth analysis using eight widely used algorithms on five different data traces. The performance of each approach is measured using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). In addition, the statistical analysis is also carried out using Wilcoxon signed rank and Friedman with Finner post-hoc multiple comparison tests. The study finds that Blackhole Algorithm (BhA) reduced the RMSE by 23.60%, 6.51%, 21.21%, 60.45% and 38.30% relative to the worst performing algorithm for 5 min forecasts of all five data traces correspondingly. Moreover, Friedman test confirms that the results of these approaches have a significant difference with 95% confidence interval (CI) and ranks show that the BhA and FSA received best ranks for Google Cluster trace (CPU and Memory Requests) while second best ranks for NASA and Saskatchewan HTTP server requests. (C) 2021 Elsevier B.V. All rights reserved.