Qi Zhang

TY - JOUR

T1 - Latency-driven Parallel Task Data Offloading in Fog Computing Networks for Industrial Applications

AU - Mukherjee, Mithun

AU - Kumar, Suman

AU - Mavromoustakis, Constandinos X.

AU - Mastorakis, George

AU - Matam, Rakesh

AU - Kumar, Vikas

AU - Zhang, Qi

PY - 2020

Y1 - 2020

N2 - Fog computing leverages the computational resources at the network edge to meet the increasing demand for latency-sensitive applications in large-scale industries. In this article, we study the computation offloading in a fog computing network, where the end users, most of the time, offload part of their tasks to a fog node. Nevertheless, limited by the computational and storage resources, the fog node further simultaneously offloads the task data to the neighboring fog nodes and/or the remote cloud server to obtain the additional computing resources. However, meanwhile, the offloaded tasks from the neighboring node incur burden to the fog node. Moreover, the task offloading to the remote cloud server can suffer from limited communication resources. Thus, to jointly optimize the amount of tasks offloaded to the neighboring fog nodes and communication resource allocation for the offloaded tasks to the remote cloud, we formulate a latency-driven task data offloading problem considering the transmission delay from fog to the cloud and service rate that includes the local processing time and waiting time at each fog node. The optimization problem is formulated as a quadratically constraint quadratic programming. We solve the problem by semidefinite relaxation. The simulation results demonstrate that the proposed strategy is effective and scalable under various simulation settings.

AB - Fog computing leverages the computational resources at the network edge to meet the increasing demand for latency-sensitive applications in large-scale industries. In this article, we study the computation offloading in a fog computing network, where the end users, most of the time, offload part of their tasks to a fog node. Nevertheless, limited by the computational and storage resources, the fog node further simultaneously offloads the task data to the neighboring fog nodes and/or the remote cloud server to obtain the additional computing resources. However, meanwhile, the offloaded tasks from the neighboring node incur burden to the fog node. Moreover, the task offloading to the remote cloud server can suffer from limited communication resources. Thus, to jointly optimize the amount of tasks offloaded to the neighboring fog nodes and communication resource allocation for the offloaded tasks to the remote cloud, we formulate a latency-driven task data offloading problem considering the transmission delay from fog to the cloud and service rate that includes the local processing time and waiting time at each fog node. The optimization problem is formulated as a quadratically constraint quadratic programming. We solve the problem by semidefinite relaxation. The simulation results demonstrate that the proposed strategy is effective and scalable under various simulation settings.

KW - Computation offloading

KW - Industrial IoT

KW - fog computing

KW - latency sensitive

KW - mobile edge computing

KW - offloading decision

KW - resource allocation

U2 - 10.1109/TII.2019.2957129

DO - 10.1109/TII.2019.2957129

M3 - Journal article

VL - 16

SP - 6050

EP - 6058

JO - IEEE Transactions on Industrial Informatics

JF - IEEE Transactions on Industrial Informatics

SN - 1551-3203

IS - 9

M1 - 8918450

ER -