Workload Analysis for the Scope of User Demand Prediction Model Evaluations in Cloud Environments

• Published in: Proceedings of the 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing pp. 883 - 889
• Main Authors: Panneerselvam, John, Lu Liu, Antonopoulos, Nick, Yuan Bo
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2014
• Subjects: Analytical models; Computational modeling; Hidden Markov models; Markov processes; Mathematical model; modelling; pattern; prediction; Predictive models; Resource management; workloads
• DOI: 10.1109/UCC.2014.144

Alongside the healthy development of the Cloud-based technologies across various application deployments, their associated energy consumptions incurred by the excess usage of Information and Communication Technology (ICT) resources, is one of the serious concerns demanding effective solutions with immediate effect. Effective auto scaling of the Cloud resources in accordance to the incoming user demand and thereby reducing the idle resources is one optimum solution which not only reduces the excess energy consumptions but also helps maintaining the Quality of Service (QoS). Whilst achieving such tasks, estimating the user demand in advance with reliable level of accuracy has become an integral and vital component. With this in mind, this research work is aimed at analyzing the Cloud workloads and further evaluating the performances of two widely used prediction techniques such as Markov modelling and Bayesian modelling with 7 hours of Google cluster data. An important outcome of this research work is the categorization and characterization of the Cloud workloads which will assist leading into the user demand prediction parameter modelling.