TY - JOUR

T1 - Centralized and Distributed Architectures for Energy and Delay Efficient Fog Network-Based Edge Computing Services

AU - Bozorgchenani, Arash

AU - Tarchi, Daniele

AU - Emanuele Corazza, Giovanni

PY - 2019/3/1

Y1 - 2019/3/1

N2 - Edge computing techniques allow to exploit the devices at the network borders for computing efforts in order to reduce centralized cloud requests. A fog network is a feasible solution for implementing edge computing services. Within this scenario, the deployed fog nodes (FNs) are able to offload different portions of a single task to the available nodes to be processed at the network edge. However, to partially offload, FNs consume an extra amount of energy for transmission and reception of the tasks while saving energy by not processing the whole task on their own. Moreover, offloading requires an extra transmission and reception time to the task processing time. In this paper, the focus is on a partial offloading approach where the tradeoff between FN energy consumption and task processing delay is considered when estimating the portion to be offloaded to the available devices at the edge of the network by comparing a centralized and a distributed architecture. Simulation results demonstrate the effectiveness of the proposed estimation solutions in terms of FN energy consumption, average task delay, and network lifetime.

AB - Edge computing techniques allow to exploit the devices at the network borders for computing efforts in order to reduce centralized cloud requests. A fog network is a feasible solution for implementing edge computing services. Within this scenario, the deployed fog nodes (FNs) are able to offload different portions of a single task to the available nodes to be processed at the network edge. However, to partially offload, FNs consume an extra amount of energy for transmission and reception of the tasks while saving energy by not processing the whole task on their own. Moreover, offloading requires an extra transmission and reception time to the task processing time. In this paper, the focus is on a partial offloading approach where the tradeoff between FN energy consumption and task processing delay is considered when estimating the portion to be offloaded to the available devices at the edge of the network by comparing a centralized and a distributed architecture. Simulation results demonstrate the effectiveness of the proposed estimation solutions in terms of FN energy consumption, average task delay, and network lifetime.

U2 - 10.1109/TGCN.2018.2885443

DO - 10.1109/TGCN.2018.2885443

M3 - Journal article

VL - 3

SP - 250

EP - 263

JO - IEEE Transactions on Green Communications and Networking

JF - IEEE Transactions on Green Communications and Networking

SN - 2473-2400

IS - 1

ER -