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A Dynamic Combinatorial Double Auction Model for Cloud Resource Allocation
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AI
DOI：10.1109/TCC.2022.3231249.png)
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Abstract
En 中文
For the cloud market, we proposed a Dynamic Combinatorial Double Auction (DCDA) model to improve the social welfare and resource utilization. In the model, cloud-agents represent cloud service providers, and user-agents represent cloud users. They bid for various combinations of resources in a dynamic environment. To overcome the computational complexity of combinatorial auctions, we employed a greedy approximation method to solve the winner determination problem together with a truthful payment scheme. The proposed model is proven to be approximately efficient, incentive compatible, individually rational, and budget-balanced. Considering both parties' interests and the relative scarcity of cloud resources, this model also ensures fairness and balances resource allocation.
Keywords:
Cloud computing
combinatorial double auction
truthful mechanism
greedy heuristics
winner determination problem
resource allocation
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