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In modern large-scale systems, fast distributed resource allocation and utility maximization are becoming increasingly important. Traditional solutions to such problems rely on primal/dual decomposition and gradient methods, whose convergence is sensitive to the choice of the stepsize and may not be sufficient to satisfy the requirement of large-scale real-time applications. The authors propose a new iterative approach to distributed resource allocation in coupled systems. Without complicating message-passing, the new approach is robust to parameter choices and expedites convergence by exploiting problem structures. They theoretically analyze the asynchronous algorithm convergence conditions and empirically evaluate its benefits in a case of cloud network resource reservation based on real-world data.
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