Probabilistic-based workload forecasting and service redeployment for multi-tenant services Online publication date: Fri, 12-Feb-2016
by Shijun Liu; Zeyu Di; Lei Wu; Li Pan; Yuliang Shi
International Journal of High Performance Computing and Networking (IJHPCN), Vol. 9, No. 1/2, 2016
Abstract: This paper presents a two-stage service migration decision method which combines business workload forecasting with real-time load sensing, and thus adds business forecasting to previous load balancing approaches that rely solely upon real-time load sensing. The migration decision procedure and the detailed causal analysis algorithms based on Bayesian networks are also given. After the critical business indicators have been obtained from causal analysis, business fluctuation related with the critical indicators can be forecasted by using Markov chain method. And then, the migration decision can be made based on the forecasting results and the real-time load information together. We evaluate the migration decision method through three sets of experiments. We found that by migrating service on a shared multi-tenant service environment, the QoS requirement can be assured dynamically and the capability of workloads increases under same resource cost, which is helpful in optimised deploying for multi-tenant applications.
Online publication date: Fri, 12-Feb-2016
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of High Performance Computing and Networking (IJHPCN):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email firstname.lastname@example.org