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Geo-Distributed Workload Scheduling of Cloud Data Centers through Consensus-Based Asynchronous Decentralized Algorithm
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DOI：10.1109/tcc.2026.3721771.png)
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Abstract
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The rapid growth of cloud computing demand has fueled the expansion of cloud data centers (CDCs). To improve resource utilization and operational efficiency, effective coordination of geographically distributed CDCs is becoming increasingly important. This paper proposes a consensus-based asynchronous decentralized optimization method for geo-distributed workload scheduling across CDCs. Specifically, computing requests are categorized into three classes according to their latency tolerance, based on which a decentralized scheduling architecture is developed. Then, the geo-distributed workload scheduling problem is formulated as an integer linear program (ILP), and further transformed into a decentralized optimization model by introducing consensus variables to represent inter-regional workload transfers. To address heterogeneous communication delays and straggler effects, an asynchronous consensus algorithm is developed. Two novel mechanisms, known as the minimum dependency set and maximum lagging level, are leveraged to balance update responsiveness and information freshness. Numerical studies on exemplary cloud computing systems demonstrate that the proposed scheduling method reduces overall cost by 15.5% compared with the local-only computing paradigm. Besides, our asynchronous decentralized algorithm has reduced the wall-clock time by 53.6% and 47.8% under fixed- and stochastic-delay straggler scenarios, while achieving solution quality close to that of the centralized benchmark.
Keywords:
Cloud data centers
geo-distributed workload scheduling
decentralized optimization
asynchronous consensus algorithm
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