Adaptive Workload Prediction for Cloud-Based Server Infrastructures

Authors

  • Kritwara Rattanaopas Department of Computer Engineering, Faculty of Engineering, Prince of Songkla University, Hat Yai, Songkhla, Thailand.
  • Pichaya Tandayya Department of Computer Engineering, Faculty of Engineering, Prince of Songkla University, Hat Yai, Songkhla, Thailand.

Keywords:

Adaptive Workload Prediction, Cloud Infrastructure, Web, LMS, AR Model, Workload Characteristics, Elastic Architecture, Multi-Tier Architecture,

Abstract

Currently, data centers offer cloud computing platforms relying on virtualization technology and multi-tier architecture to handle an ever increasing scale and to provide elastic service. However, in order to achieve elasticity, efficient prediction is needed to control virtual machines. We present a new adaptive linear auto regressive technique for web server workload prediction with feedback loop control. We test the Adaptive-Feedback AR model with the Songkhla Rajabhat University’s academic web which has a similar daily pattern of workloads and the learning management system (LMS) web which has unpredictable workloads. For the 1-minute interval, the suitable result for controlling the AR orders is in the range of 2-8 and previous historical value is in range of 10-25. Our new prediction approach predicts both web workloads with a root mean square error (RMSE) below 0.6, of which quality is better, in terms of the prediction accuracy resulting in a better performance.

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Published

2017-06-01

How to Cite

Rattanaopas, K., & Tandayya, P. (2017). Adaptive Workload Prediction for Cloud-Based Server Infrastructures. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(2-4), 129–134. Retrieved from https://jtec.utem.edu.my/jtec/article/view/2374