An Analytical Cost-Optimal Cloudlet Placement Framework over Fiber-Wireless Networks with Quasi-Convex Latency Constraint

Abstract

To meet the low-latency constraints arising from future smart mobile devices, Internet-of-Things, and 5G applications, major interest is currently given to the integration of centralized cloud computing and distributed edge computing infrastructures to deliver higher performance and reliability to edge devices in accessing mobile cloud services. The three-tier network architecture arising from cloud, cloudlet, and edge-devices can handle miscellaneous latency requirements for both latency-sensitive and latency-tolerant applications more efficiently than conventional two-tier networks. In this paper, we primarily focus on the static cloudlet network planning problem and propose an analytical hybrid cost-optimization framework for optimal cloudlet placement. We formulate this problem as a convex optimization problem and solve by using Karush-Kuhn-Tucker (KKT) conditions, and show that this framework can be evaluated without any scalability issues observed with integer programming based frameworks for large datasets. Moreover, we derive user-friendly closed form expressions that provide a first-hand estimation of cloudlet deployment cost depending on a few important network parameters like split-ratio, population density, and network bandwidth. Finally, we also show that the optimal solution of this analytical framework can be considered as a tight lower bound of the optimal solutions of integer programming based frameworks and makes a better cloudlet installation cost estimation compared to other existing frameworks.