Market-Driven Joint Trading Strategy for Computing Service and Electricity in Cloud-Edge Collaborative Systems

Abstract

The rapid growth of cloud-edge collaborative computing and its increasing electricity demand are forging strong interdependencies between computing and power markets. This paper proposes a market-driven joint trading strategy that coordinates computing services and electricity procurement within a cloud-edge collaborative system. To capture heterogeneous operational characteristics, we introduce a differentiated modeling paradigm, combining a task-queuing model for the cloud data center with an empirically-grounded nonlinear delay model for edge servers to characterize its performance saturation. These models are then embedded in a unified trading framework that explicitly characterizes the bidirectional interaction between computing and electricity markets, where the cloud acts as a price-making participant. The resulting large-scale, non-convex problem is transformed into a tractable mixed-integer second-order cone program by applying targeted convexification techniques—specifically, second-order cone relaxation for nonlinear delay constraints and special ordered sets of type 2 for bilinear market-clearing terms. Numerical case studies demonstrate that the differentiated modeling paradigm reduces the overall operational cost of the test system by 16.3%, including both electricity and delay costs. Furthermore, the solution methodology ensures high scalability and computational efficiency for large-scale instances while maintaining an optimality gap below 0.3%.