A Hybrid Fitness-Utility Algorithm for Improved Service Chain Placement

Optimal placement of service chains (composed of multiple connected service components) on heterogeneous cloud/fog infrastructure is a challenging problem. Even when deploying a small set of service components, the search-space of all possible solutions is quite large. Furthermore, current approaches sacrifice either precision or scalability when presented with heterogeneous platform configurations. In this scenario, the goal is to support orchestration that optimally selects solutions that meet performance constraints, leverages differentiating platform features and delivers these solutions within reasonable run-time. This paper presents the novel Hybrid Fitness-Utility Algorithm that addresses these issues by utilizing concepts derived from evolutionary algorithms to dimensionally reduce complexity, and incorporates the utility of placing each individual service component on an infrastructure resource for optimized selection between potential solutions. Our results show that the algorithm succeeds in determining optimal service chain placement onto distributed infrastructure for a simulated cloud/fog scenario and for a live multi point-of-presence OpenStack-based testbed with over 90 percent confidence.