Optimal Design of Hierarchical Cloud-Fog&Edge Computing Networks with Caching

Abstract

This paper investigates the optimal design of a hierarchical cloud-fog&edge computing (FEC) network, which consists of three tiers, i.e., the cloud tier, the fog&edge tier, and the device tier. The device in the device tier processes its task via three computing modes, i.e., cache-assisted computing mode, cloud-assisted computing mode, and joint device-fog&edge computing mode. Specifically, the task corresponds to being completed via the content caching in the FEC tier, the computation offloading to the cloud tier, and the joint computing in the fog&edge and device tier, respectively. For such a system, an energy minimization problem is formulated by jointly optimizing the computing mode selection, the local computing ratio, the computation frequency, and the transmit power, while guaranteeing multiple system constraints, including the task completion deadline time, the achievable computation capability, and the achievable transmit power threshold. Since the problem is a mixed integer nonlinear programming problem, which is hard to solve with known standard methods, it is decomposed into three subproblems, and the optimal solution to each subproblem is derived. Then, an efficient optimal caching, cloud, and joint computing (CCJ) algorithm to solve the primary problem is proposed. Simulation results show that the system performance achieved by our proposed optimal design outperforms that achieved by the benchmark schemes. Moreover, the smaller the achievable transmit power threshold of the device, the more energy is saved. Besides, with the increment of the data size of the task, the lesser is the local computing ratio.

A Task Orchestration Strategy in a Cloud-Edge Environment Based on Intuitionistic Fuzzy Sets

“…The first category of algorithms is designed to minimize energy consumption, which is crucial in cloud-edge computing. Fan et al [13] and Chen et al [14] have made significant strides in this area. Fan et al developed an optimized hierarchical cloud-FEC network, while Chen et al focused on a genetic algorithm for energy-efficient offloading.…”

A Survey and Taxonomy on Task Offloading for Edge-Cloud Computing

“…Content may change prior to final publication. Fan et al [48] propose a method for optimizing the device energy consumption for task execution with deadline constraints in an edge-cloud environment considering content cache in edge servers for some tasks. The proposed method first offloads tasks with cached contents to the corresponding edge servers, and for other tasks, respectively assigns them to the cloud or joint device-edge incurring lower device energy.…”

Crowd-Based Cognitive Perception of the Physical World: Towards the Internet of Senses

“…Finally, it is worth stressing that both the Central Executive and the hyper-map should be intended as logical entities, whose implementations do not need to be centralized in a strict sense. In fact, as highlighted in Figure 3 , the principles of distributed computing and distributed database systems could be conveniently applied for their implementation, according to the classic Cloud paradigms [ 44 , 45 ]. Moreover, possible challenges and constraints in network connectivity, communication bandwidth and service latency can be successfully addressed by Edge and Fog computing infrastructures, in which data, computing capabilities, storage and applications are located somewhere between the sensing devices and the Cloud.…”