Title: A cluster workload forecasting strategy using a higher order statistics based ARMA model for IaaS cloud services

Authors: Zohra Amekraz; Moulay Youssef Hadi

Addresses: Laboratory of Modelization of Information and Communication Systems, Faculty of Sciences, Ibn Tofail University, Kénitra, BP 133, Morocco ' Laboratory of Modelization of Information and Communication Systems, Faculty of Sciences, Ibn Tofail University, Kénitra, BP 133, Morocco

Abstract: With cloud services becoming more popular among internet users, cloud providers are facing a challenge in allocating resources according to users' demand instantly due to the delay caused by the virtual machines' start up time. This problem can be solved using proactive allocation techniques that predict the workload in advance and make scaling decisions ahead of time. In this paper, we present an adaptive workload prediction method based on higher order statistics (HOS) and autoregressive moving average (ARMA) model. We use HOS to make a Gaussianity checking test of the cloud workload and decide the suitable identification method of the ARMA model to be used for forecasting. We evaluate our proposal with two real traces extracted from cluster workloads. The results show that the proposed method has an average of 34% higher accuracy than the baseline ARMA model and presents a low forecasting overhead (< 2 s).

Keywords: IaaS cloud services; workload prediction; cluster workload; autoregressive moving average; ARMA; higher order statistics; HOS.

DOI: 10.1504/IJNVO.2022.121844

International Journal of Networking and Virtual Organisations, 2022 Vol.26 No.1/2, pp.3 - 22

Received: 16 Sep 2020
Accepted: 22 Feb 2021
Published online: 07 Apr 2022 *

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