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

22 Scopus citations


With the continuous expansion of the power Internet of Things and 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. This paper considers the joint optimization of computing offloading and service caching in edge computing-based smart grid, and formulates the problem as a Mixed-Integer Non-Linear Program, 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 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-complexity is very close to the optimal method, and performs much better than other benchmark methods.

Original languageEnglish
JournalIEEE Transactions on Cloud Computing
StateAccepted/In press - 2022


  • Cloud computing
  • Computing Offloading
  • Delays
  • Edge computing
  • Edge Computing
  • Game Theory
  • Optimization
  • Resource management
  • Service Caching
  • Smart Grid
  • Smart grids
  • Task analysis


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