Multi-Objective Self-Adaptive Composite SaaS Using Feature Model

Abstract

Composite services of type SaaS run in dynamic distributed environments where the quality of service (QoS) properties of the constituent services may change during execution. To face such dynamism and volatility, adaptation of composite SaaS to the runtime changes is a vital requirement. Recent research focused on centralized environments which are impractical for dynamic composition that requires distributed settings. To address this challenge, this paper proposes a distributed approach for composite SaaS adaptation using feature selection through applying the master/slave pattern. Slaves locally monitor the distributed constituent services and send performance information to the master, which in its turn reconfigures the composite services to provide the expected QoS and monitors the overall performance. Since adapting a composite SaaS to be QoS-optimal depends on multiple criteria according to the selected features, e.g., performance and cost, we model the adaptation process as a multi-objective optimization problem and then propose a genetic algorithm to compute the Pareto-optimal set of solutions for this problem. Experimental results show that our approach is efficient in distributed and large-scale environments compared to the centralized approach.