Replica Placement in Cloud through Simple Stochastic Model Predictive Control

This paper presents a model and an algorithm for optimal service placement (OSP) of a set of N-tier software systems, subject to dynamic changes in the workload, Service Level Agreements (SLA), and administrator preferences. The objective function models the resources' cost, the service level agreements and the trashing cost. The optimization algorithm is predictive: its allocation or reallocation decisions are based not only on the current metrics but also on predicted evolution of the system. The solution of the optimization, in each step, is a set some service replicas to be added or removed from the available hosts. These deployment changes are optimal with regards to overall objectives defined over time. In addition, the optimization considers the restrictions imposed on the number of possible service migrations at each time interval. We present experimental results that show the effectiveness of our approach.