Server configuration optimization in mobile edge computing: A cost‐performance tradeoff perspective

Abstract

Before service providers build up an mobile edge computing (MEC) platform, an important issue that needs to be considered is the configuration of computing resources on edge servers. Since the computing resources on an edge server are limited compared with a cloud server and the service provider's deployment budget is limited, it would be unrealistic to equip all edge servers with abundant computing resources. In addition, the edge servers have different computation demands due to their different geographies. Therefore, this article investigates the problem of server configuration optimization in an MEC environment based on a given computation demand statistics of the selected deployment locations. Our strategy is to treat each edge server as an M/M/m queueing model, and then establish the performance and cost models for the system. Two optimization problems, including cost constrained performance optimization, and performance constrained cost optimization are formulated based on our models and solved by a series of fast numerical algorithms. We also conduct extensive numerical simulation examples to show the effectiveness of the proposed algorithms. MEC service providers can use our strategy to get the appropriate type of processor and obtain the optimal processor number for each edge server to achieve two different goals: (1) deliver the highest‐quality services with a given cost constraint; (2) minimize the investment cost with a service‐quality guarantee. Our research is of great significance for service providers to control the tradeoff between investment cost and service quality.

From zero to fog: Efficient engineering of fog‐based Internet of Things applications

“…Consequently, finding valid deployment options becomes exponentially harder with each added component and is infeasible for larger deployments. Tong et al 38 and, to some extent Heintz et al 36 take a similar approach to FogTorch, while 4‐7,35,39‐55 employ a more efficient heuristics approach to solve the formalized optimization problem. Naas et al 56 offer a heuristics‐based solution as well, but place data replicas rather than services, a challenge that is also addressed in References 57,58.…”

AuctionWhisk: Using an auction‐inspired approach for function placement in serverless fog platforms

“…On the one hand, there are static solutions using upfront best practices, 12,48-50 simulation, 45,[51][52][53][54][55][56][57][58][59][60] testbed evaluation, 38,61-67 a combination of those, 46,68 or formalized assignment problems. [69][70][71][72][73][74][75][76][77][78][79] On the other hand, dynamic approaches using centralized schedulers [80][81][82][83][84][85][86][87][88][89][90][91] or decentralized algorithms [92][93][94][95] have also been proposed. It is no surprise that auction-based approaches, which have seen broader interest in computer science, [28][29][30][31][32][33][34]…”

“…Consequently, this challenge has been a key research topic in the last few years. On the one hand, there are static solutions using upfront best practices, 12,48‐50 simulation, 45,51‐60 testbed evaluation, 38,61‐67 a combination of those, 46,68 or formalized assignment problems 69‐79 . On the other hand, dynamic approaches using centralized schedulers 80‐91 or decentralized algorithms 92‐95 have also been proposed.…”

Special issue: Elastic computing from edge to the cloud environments

“…The second paper in this special issue titled “Server Configuration Optimisation in Mobile Edge Computing: A Cost‐Performance Tradeoff Perspective” 4 studies the problem of server configuration optimization in Mobile edge computing environments. The authors use M/M/m queuing models and establish the performance and cost models for the system.…”