Optimal Workload Allocation in Fog-Cloud Computing Towards Balanced Delay and Power Consumption

TLDR

Mobile users demand localized services, but retrieving data from remote clouds is inefficient, prompting the development of fog (edge) computing that deploys local facilities to provide low‑latency, high‑rate access between users and the cloud. This study investigates the tradeoff between power consumption and transmission delay in fog‑cloud computing. The authors formulate a workload‑allocation problem that minimizes power under a delay constraint and solve it approximately by decomposing the primal problem into three solvable subproblems for the fog, cloud, and communication subsystems. Simulations show that modestly increasing computation resources to save bandwidth and reduce latency enables fog computing to significantly improve cloud performance.

Abstract

Mobile users typically have high demand on localized and location-based information services. To always retrieve the localized data from the remote cloud, however, tends to be inefficient, which motivates fog computing. The fog computing, also known as edge computing, extends cloud computing by deploying localized computing facilities at the premise of users, which prestores cloud data and distributes to mobile users with fast-rate local connections. As such, fog computing introduces an intermediate fog layer between mobile users and cloud, and complements cloud computing toward low-latency high-rate services to mobile users. In this fundamental framework, it is important to study the interplay and cooperation between the edge (fog) and the core (cloud). In this paper, the tradeoff between power consumption and transmission delay in the fog-cloud computing system is investigated. We formulate a workload allocation problem which suggests the optimal workload allocations between fog and cloud toward the minimal power consumption with the constrained service delay. The problem is then tackled using an approximate approach by decomposing the primal problem into three subproblems of corresponding subsystems, which can be, respectively, solved. Finally, based on simulations and numerical results, we show that by sacrificing modest computation resources to save communication bandwidth and reduce transmission latency, fog computing can significantly improve the performance of cloud computing.