Joint Optimization of Computing Offloading and Service Caching in Edge Computing-Based Smart Grid

Huan Zhou, Zhenyu Zhang, Dawei Li, Zhou Su

Research output: Contribution to journalArticlepeer-review

30 Scopus citations


With the continuous expansion of the power Internet of Things (IoT) and the rapid increase in the number of Smart Devices (SDs), the data generated by SDs has exponentially increased. The traditional cloud-based smart grid cannot meet the low latency and high reliability requirements of emerging applications. By moving computing, data, and services from the centralized cloud to Edge Servers (ESs), edge computing exhibits excellent performance in communication delay and traffic reduction. Simultaneously, service caching also shows attractive advantages in handling the surge in data traffic. In this paper, we consider the joint optimization of computing offloading and service caching in edge computing-based smart grid, and formulate the problem as a Mixed-Integer NonLinear Program (MINLP), aiming to minimize the task cost of the system. The original problem is decomposed into an equivalent master problem and sub-problem, and a Collaborative Computing Offloading and Resource Allocation Method (CCORAM) is proposed to solve the optimization problem, which includes two low-complexity algorithms. Specifically, a gradient descent allocation algorithm is first proposed to determine the computing resource allocation strategy, and then a game theory-based algorithm is proposed to determine the computing strategy. Simulation results show that CCORAM with low time complexity is very close to the optimal method, and performs much better than other benchmark methods.

Original languageEnglish
Pages (from-to)1122-1132
Number of pages11
JournalIEEE Transactions on Cloud Computing
Issue number2
StatePublished - 1 Apr 2023


  • Computing offloading
  • edge computing
  • game theory
  • service caching
  • smart grid


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