Generic cloud platform multi-objective optimization leveraging models@run.time

Cloud computing promises scalable hosting by offering an elastic management of virtual machines which run on top of hardware data centers. This elastic management as a cornerstone of PaaS (Platform As A Service) has to deal with trade-offs between conflicting requirements such as cost and quality of service. Solving such trade-offs is a challenging problem. Indeed, most of PaaS providers consider only one optimization axis or ad-hoc multi-objective resolution techniques using domain specific heuristics. This paper aims at proposing a generic approach to build cloud optimization by combining modeling and search based paradigms. Our approach is two-fold: 1) To reason about a cloud environment, we use a Models@run.time approach to have an abstraction layer of a cloud configuration that supports monitoring capabilities and represents cloud intrinsic parameters like cost, load information, etc. 2) We use a search-based algorithm to navigate through cloud candidate configuration solutions in order to solve the Cloud Multi-objective Optimization Problem (CMOP). We validate our approach based on a case study that we define with our cloud provider partner EBRC as representative of a dynamic management problem of heterogeneous distributed cloud nodes. We implement a prototype of our PaaS supervision framework using Kevoree, a Models@run.time platform. The prototype shows the efficiency of our approach in terms of finding possible cloud configurations in reasonable time. The prototype is flexible since it enables an easy reconfiguration of the cloud customer optimization objectives.