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

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  • Mithun Mukherjee, Guangdong University of Petrochemical Technology
  • ,
  • Suman Kumar, IGNTU
  • ,
  • Constandinos X. Mavromoustakis, University of Nicosia
  • ,
  • George Mastorakis, Crete Greece 72100
  • ,
  • Rakesh Matam, Computer Science Engineering, Indian Institute of Information Technology Guwahati
  • ,
  • Vikas Kumar, Bihar, Bharat Sanchar Nigam Limited
  • ,
  • Qi Zhang

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.

OriginalsprogEngelsk
Artikelnummer8918450
TidsskriftIEEE Transactions on Industrial Informatics
Vol/bind16
Nummer9
Sider (fra-til)6050-6058
Antal sider9
ISSN1551-3203
DOI
StatusUdgivet - 2020

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