AI-enabled mobile multimedia service instance placement scheme in mobile edge computing

Abstract

Leveraging cloud infrastructure to the mobile edge computing helps the mobile users to get real time multimedia services in Fifth Generation (5G) network system. To ensure higher Quality-of-Experience (QoE), faster migration of mobile multimedia service instances is required to cope up with user mobility. By deploying the mobile multimedia service instances proactively in multiple edge nodes (ENs) helps the users to get higher QoE. However, excessive deployment of service replicas might increase the cost of the overall network. To establish trade-off between these two conflicting objectives, we have formulated the problem as a Multi-objective Integer Linear Programming (MILP) by integrating the users’ path prediction model. This problem is proven to be an NP-hard one for large networks, thus we develop an artificial intelligence (AI) based meta-heuristic Binary Particle Swarm Optimization (BPSO) algorithm to achieve near-optimal solution within polynomial time. The performance analysis results show the significant performance improvement in terms of QoE and user satisfaction as compared to other state-of-the-art works.