Resource management systems rely on a centralized approach to manage applications running on each resource. The centralized resource management system is not efficient and scalable for large-scale servers as the number of applications running on shared resources is increasing dramatically and the centralized manager may not have enough information about applications’ need.
This work proposes a decentralized auction-based resource management approach to reach an optimal strategy in a resource competition game. The applications learn through repeated interactions to select their optimal action for shared resources. Specifically, we investigate two case studies of cache competition game and main processor and coprocessor congestion game. We enforce costs for each resource and derive bidding strategy. Accurate evaluation of the proposed approach show that our distributed allocation is scalable and outperforms the static and traditional approaches.
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