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Task Data Offloading and Resource Allocation in Fog Computing with Multi-Task Delay Guarantee

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  • Mithun Mukherjee, Guangdong University of Petrochemical Technology
  • ,
  • Suman Kumar, IGNTU
  • ,
  • Qi Zhang
  • R. Matam, Indian Institute of Information Technolog
  • ,
  • Constandinos X. Mavromoustakis, University of Nicosia
  • ,
  • Yunrong Lv, Guangdong University of Petrochemical Technology
  • ,
  • George Mastorakis, Hellenic Mediterranean University

With the emergence of delay-sensitive task completion, computational offloading becomes increasingly desirable due to the end-users limitations in performing computation-intense applications. Interestingly, fog computing enables computational offloading for the end-users towards delay-sensitive task provisioning. In this paper, we study the computational offloading for the multiple tasks with various delay requirements for the end-users, initiated one task at a time in end-user side. In our scenario, the end-user offloads the task data to its primary fog node. However, due to the limited computing resources in fog nodes compared to the remote cloud server, it becomes a challenging issue to entirely process the task data at the primary fog node within the delay deadline imposed by the applications initialized by the end-users. In fact, the primary fog node is mainly responsible for deciding the amount of task data to be offloaded to the secondary fog node and/or remote cloud. Moreover, the computational resource allocation in term of CPU cycles to process each bit of the task data at fog node and transmission resource allocation between a fog node to the remote cloud are also important factors to be considered. We have formulated the above problem as a Quadratically Constraint Quadratic Programming (QCQP) and provided a solution. Our extensive simulation results demonstrate the effectiveness of the proposed offloading scheme under different delay deadlines and traffic intensity levels.

Original languageEnglish
JournalIEEE Access
Pages (from-to)152911-152918
Number of pages8
Publication statusPublished - 2019

    Research areas

  • 5G and beyond, CLOUD, Cloud computing, Computational modeling, DYNAMIC RESOURCE, Delays, Edge computing, Resource management, Servers, Task analysis, computation offloading, fog computing, mobile edge computing, offloading decision, resource allocation

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ID: 167405811