QUICK: QoS-guaranteed Efficient Cloudlet Placement in Wireless Metropolitan Area Networks

TL;DR

This paper addresses the problem of efficiently deploying cloudlets in wireless metropolitan networks to minimize access delay and deployment costs, proposing novel algorithms that outperform existing solutions in efficiency and cost reduction.

Contribution

It introduces new heuristic and clustering algorithms for cloudlet placement that improve delay and cost metrics, including solutions for capacity constraints and a special case with fixed cloudlet capabilities.

Findings

Algorithms reduce average access delay by over 46%.

Deployment cost is decreased by approximately 50%.

Proposed methods outperform existing algorithms in efficiency and scalability.

Abstract

This article defines the QoS-guaranteed efficient cloudlet deploy problem in wireless metropolitan area network, which aims to minimize the average access delay of mobile users i.e. the average delay when service requests are successfully sent and being served by cloudlets. Meanwhile, we try to optimize total deploy cost represented by the total number of deployed cloudlets. For the first target, both un-designated capacity and constrained capacity cases are studied, and we have designed efficient heuristic and clustering algorithms respectively. We show our algorithms are more efficient than the existing algorithm. For the second target, we formulate an integer linear programming to minimize the number of used cloudlets with given average access delay requirement. A clustering algorithm is devised to guarantee the scalability. For a special case of the deploy cost optimization problem where all cloudlets' computing capabilities have been given, i.e., designated capacity, a minimal cloudlets efficient heuristic algorithm is further proposed. We finally evaluate the performance of proposed algorithms through extensive experimental simulations. Simulation results demonstrate the proposed algorithms are more than 46% efficient than existing algorithms on the average cloudlet access delay. Compared with existing algorithms, our proposed clustering and heuristic algorithms can reduce the number of deployed cloudlets by about 50% averagely.