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Optimal Pricing for Offloaded Hard- and Soft-Deadline Tasks in Edge Computing

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  • Mithun Mukherjee, Nanjing University of Information Science & Technology
  • ,
  • Vikas Kumar, Bihar, Bharat Sanchar Nigam Limited
  • ,
  • Qi Zhang
  • Constandinos X. Mavromoustakis, University of Nicosia
  • ,
  • Rakesh Matam, Computer Science Engineering, Indian Institute of Information Technology Guwahati

In this paper, we study the deadline-aware task data offloading in edge-cloud computing systems. The hard-deadline tasks strictly demand to be processed within their delay deadline, whereas the deadline can be relaxed for the soft-deadline tasks. Generally, edge computing aims to shorten the transmission delay between the remote cloud and the end-user, however, at the cost of limited computing capability. Therefore, it is challenging to decide where to offload the hard- and soft-deadline tasks based on the average delay and the service price set by the edge and cloud servers. Both edge and cloud servers aim to maximize their revenue by selling the computational resources at the optimal price. Interestingly, a Wardrop equilibrium is reached, considering that each task is considered independently to be offloaded to a suitable location. The numerical results demonstrate that the proposed price- and deadline-sensitive task offloading policy reaches the equilibrium and finds the optimal location for processing while maximizing the revenue of both edge and cloud servers.

Original languageEnglish
JournalIEEE Transactions on Intelligent Transportation Systems
Pages (from-to)9829-9839
Number of pages11
Publication statusPublished - Jul 2022

Bibliographical note

Publisher Copyright:
© 2000-2011 IEEE.

    Research areas

  • Cloud computing, Computational modeling, Delays, Edge computing, Mobile edge computing, Pricing, Servers, Task analysis, deadline-aware task offloading, edge computing, offloading., pricing

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