Joint Service Placement and Computation Scheduling in Edge Clouds

Mobile edge computing enables users to run resource-intensive applications at the network edge equipped with small server clusters. The mobile services are heterogeneous and edge servers are generally resource-limited. Only a subset of services can be processed by an edge server in a time slot. In this paper, we study the joint service placement and computation scheduling (JSPCS) problem to optimize both service quality and operation cost. We formulate the optimization problem to maximize the worst utility among all the services, under the constraints of multiple types of resources, and the JSPCS problem is proved to be NP-hard. By linear relaxation, we provide a dual decomposition approach to decouple this hard problem into a sequence of tractable sub-problems. We propose the Lagrange duality based joint optimal service placement and computation scheduling (LD-JSPCS) algorithm to derive the optimal solution to JSPCS problem with linear relaxation. By iteratively solving a series of feasibility problems, we prove the proposed algorithm guarantees the convergence to the optimum. Theoretical analysis and extensive simulations are performed, validating the efficiency of LD-JSPCS in service provision with limited resources.