Joint offloading and service selection via matching and auction theory for multi-task dependent computation-intensive applications

Abstract: The increasing complexity of next-generation services demands efficient orchestration across the edge-to-cloud continuum to balance computational intensity, latency constraints, and resource availability. These services are typically decomposed into interdependent sub-tasks, requiring careful synchronization to meet stringent completion time requirements. The challenge is further amplified in heterogeneous and resource-constrained edge environments, where multiple providers dynamically compete for sub-task execution. This paper introduces a game-theoretic stochastic framework that optimizes system welfare from both users' and providers' perspectives, ensuring efficient task allocation across distributed computing resources. We propose a Cumulative Distribution Function (CDF)-driven game, where edge nodes serve as intermediaries between users and cloud/edge service providers. The framework is structured as a two-level mechanism: (i) a matching game governing the user-to-edge node association, and (ii) a nested Vickrey-Clarke-Groves auction selecting the optimal provider, based on a CDF-driven assessment of service completion times. To enhance feasibility in decentralized edge computing environments, provider bids are represented as uniform CDFs, establishing a dominance relation that mitigates strategic manipulation. We theoretically analyze cheating strategies, showing that truthful bidding is a rational provider behavior and that the resulting user–edge matching satisfies a suitable stability notion. Extensive simulations compare the proposed approach against a full-knowledge-based allocation, conventional game-theoretic models, and a heuristic recently proposed in the literature, evaluating the price of anarchy, system welfare, and outage probability. The results demonstrate the effectiveness of our framework in achieving resilient, cost-efficient, and low-latency orchestration across the edge-to-cloud continuum in heterogeneous edge deployments.

Perform. Evaluation, 172:102556:1-102556:16, 2026

Performance ModelsGame TheoryStochastic WorkflowsEdge AITheory



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