Server Placement and Task Allocation for Load Balancing in Edge-Computing Networks

Offloading tasks to cloud servers has increasingly been used to provide terminal users with powerful computation capabilities for a variety of services. Recently, edge computing, which offloads tasks from user devices to nearby edge servers, has been exploited to avoid the long latency associated with cloud computing. However, edge server placement and task allocation strongly affect the offloading process and the quality of a user's experience. Therefore, appropriately deploying the edge servers within a network and evenly allocating the workload to the servers are vital. This paper thus considers both the workload of edge servers and the distances involved in offloading tasks to these servers. To improve the user experience, edge server locations are carefully selected and the workload for the servers are allocated in a balanced manner. This scenario is formulated as a mixed-integer linear programming problem, and a novel solution that searches for the best server placement using simulated annealing while integrating task allocation using the Lagrangian duality theory with the sub-gradient method is proposed. Numerical simulations verify that the proposed algorithm can achieve better results than conventional heuristics.

Automated summary

Offloading tasks to edge servers has been utilized to reduce latency compared to cloud computing. Properly deploying edge servers and evenly distributing workload are crucial for enhancing user experience. This paper presents a novel approach using simulated annealing and Lagrangian duality theory for optimizing server placement and task allocation. Numerical simulations show improved results compared to conventional methods.