An adaptive framework for QoS‐aware service selection optimization

The optimization of quality‐of‐service (QoS) aware service selection problems is a crucial issue in both grids and distributed service‐oriented systems. When several implementations per service exist, one has to be selected for each workflow step. This paper aims to address these issues.

The authors proposed several heuristics with specific focus on blackboard and genetic algorithms. Their applicability and performance has already been assessed for static systems. In order to cover real‐world scenarios, the approaches are required to deal with dynamics of distributed systems.

The proposed algorithms prove their feasibility in terms of scalability and runtime performance, taking into account their adaptability to system changes.

In this paper, the authors propose a representation of the dynamic aspects of distributed systems and enhance their algorithms to efficiently capture them.

By combining both algorithms, the authors envision a global approach to QoS‐aware service selection applicable to static and dynamic systems.

The authors prove the feasibility of their hybrid approach by deploying the algorithms in a cloud environment (Google App Engine), that allows simulating and evaluating different system configurations.