Using performance modelling and analysis for self-adaptive resources allocation systems: A case study

Abstract

Data centers need to have more and more flexible execution environments, allowing resources sharing between their different applications in order to meet performances requirements. In a cloud computing application for instance, the main objective is to maximize profits by an efficient resources use, to meet the clients Service Level Agreements (SLA) and reduce the energy cost of the data center. The main challenge of resource allocation is then to find the minimum amount of resources that an application needs to meet the desired Quality of Service. To answer these concerns, self-management capabilities have been proposed to efficiently automate the resource allocation process. Autonomic managers allow to adjust the scale of the targeted systems, based on a simple monitoring process and predefined scaling strategies. In this context, it becomes important to forecast the efficiency of such self-adaptive systems, so that to find the most appropriate resource configuration to be applied. To reach this objective, we present, in this paper, a modelling approach, allowing to predict the efficiency of self-adaptive systems relating resource allocation. We use, for this purpose, a Stochastic Petri Nets modelling. A set of experiments illustrates our approach starting from modelling to performance evaluation of the studied system.