Budgeted Edge Service Selection in Mobile Edge Computing Environment

Abstract

Mobile edge computing allows end-users to retrieve required services from edge servers with low network latency. However, when massive service instances with the same or interchangeable functionalities are available at the network edge, how to select appropriate service instances for use to minimize the overall latency perceived is a critical problem. This is not trivial due to that: 1) usually multiple service instances are involved to collectively fulfil an end-user's ever-increasing complicated need; 2) the overall cost incurred should not exceed end-user's service recruiting budget. In this article, we formally define the budgeted edge service selection (BESS) problem. Then, we model the BESS problem as a constrained optimization problem and theoretically prove its <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {NP}$</tex-math></inline-formula> -hardness. Next, to solve the BESS problem effectively, we propose two approaches named BESS-O and BESS-A, respectively. Specifically, BESS-O is an optimal approach based on nonlinear integer programming. It can find out optimal solutions to BESS problems. Moreover, BESS-A is a greedy approach. It can find out approximate BESS solutions with higher efficiency, which is more useful for dealing with large-scale BESS problems. Finally, we conduct comprehensive experiments to evaluate our approaches against five representative approaches. Experimental results demonstrate that given a specific service usage budget, our approaches proposed in this article can achieve the highest completion ratio and the lowest service delay.