Dynamic Edge Server Placement for Computation Offloading in Vehicular Edge Computing

Abstract

Edge Computing is a distributed computing architecture where the computing process takes place near the user’s physical location or at the source where the data originates. By placing the edge servers (ES) closer to the user’s location, the services received are faster and more reliable while benefiting from edge computing. Various Internet of Vehicle (IoV) applications in smart cities including assisted and autonomous driving, real-time accidents monitoring require huge data processing and low-latency communication. Since the user’s devices are resource constrained, an effective approach to address this constraint is to offload their tasks to nearby ES. So, an adaptive placements (to respond continuously changing environment) of these edge computing devices play a major role in the performance of various IoV applications. So, an efficient placement of ES is considered a critical issue in vehicular edge computing (VEC). To address the efficient and cost aware dynamic ES placement problem (CADEP), we developed two greedy algorithms. First, is cost aware and vehicle density based deployment of ES (static) that ensures that each vehicle’s demand is covered by at least by one ES (coverage constraint), called Greedy_static. Second, is based on vehicle density and is dynamic as per changing environment, called Greedy_dynamic which updates ES locations periodically based on change in the environment. To minimize the relocation cost, we formulated an optimization problem and used Hungarian matching to find optimal cost. For various vehicle densities, we found that our algorithms outperform uniform strategies in terms of cost-effectiveness and ES utilization. Further, for dynamic relocation of ES, we have shown that the cost required to relocate ES randomly is more as compared to our proposed algorithm Greedy_dynamic.