Abstract
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.
Highlights
With the increasing popularity of smartphones and Internetof-Things (IoT) devices, many new applications that require significant computation and prompt responses have been developed
Edge server placement and task allocation are tailored by considering both the workload balance among the edge servers and the transmission distance for the offloaded tasks
Balancing the workload among the edge servers and reducing the transmission distance for offloaded tasks is equivalent to reducing the computational and communication latency, respectively. This is formulated as a mixed-integer linear programming problem
Summary
With the increasing popularity of smartphones and Internetof-Things (IoT) devices, many new applications that require significant computation and prompt responses have been developed. Edge server placement and task allocation are tailored by considering both the workload balance among the edge servers and the transmission distance for the offloaded tasks. Balancing the workload among the edge servers and reducing the transmission distance for offloaded tasks is equivalent to reducing the computational and communication latency, respectively. This is formulated as a mixed-integer linear programming problem. Simulations are conducted to evaluate the performance of the proposed scheme, with the results indicating that the ESP algorithm exhibits great flexibility in balancing the load among the edge servers and the transmission distance. This paper proposes a load balancing model that considers both the computational load on the edge servers and the transmission distance for task offloading in edgecomputing networks.
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