A Dynamic Multi-Objective Optimization Framework for Selecting Distributed Deployments in a Heterogeneous Environment

In distributed systems, where several deployments of a specific service exist, it is a crucial task to select and combine concrete deployments to build an executable workflow. Non-functional properties such as performance and availability are taken into account in such selection processes that are designed to reach certain objectives while meeting constraints. In this paper, a concrete data-intensive application scenario from a High-Energy Physics experiment comprising a deployment selection challenge is introduced. A generic model for distributed systems is presented based on which a formal model representing the individual components of the system is derived. The optimization problem is approached both from the angle of the user and the angle of the system provider. Moreover the dynamic aspects of the underlying system are taken into account. This results in a dynamic multi-objective optimization problem for which an explicit memory-based genetic algorithm is proposed.