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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.14823 |
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| _version_ | 1866909586204655616 |
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| author | Deng, Zhengyan Zheng, Yusen Sheng, Chenliang Qin, Shaowen |
| author_facet | Deng, Zhengyan Zheng, Yusen Sheng, Chenliang Qin, Shaowen |
| contents | The rapid advancement of AI and other emerging technologies has triggered exponential growth in computing resources demand. Faced with prohibitive infrastructure costs for large-scale computing clusters, users are increasingly resorting to leased computing resources from third-party providers. However, prevalent overestimation of operational requirements frequently leads to substantial underutilization of the computing resources. To mitigate such inefficiency, we propose a contract-based incentive framework for computing resources repurchasing. Comparing to auction mechanisms, our design enables providers to reclaim and reallocate surplus computing resources through market-driven incentives. Our framework operates in a multi-parameter environment where both clients' idle resource capacities and their unit valuations of retained resources are private information, posing a significant challenge to contract design. Two scenarios are considered based on whether all clients possess the same amount of idle resource capacity. By transforming the contract design problem into solving a mathematical program, we obtain the optimal contracts for each scenario, which can maximize the utility of computing resources providers while ensuring the requirements of incentive compatibility (IC) and individual rationality (IR). This innovative design not only provides an effective approach to reduce the inefficient utilization of computing resources, but also establishes a market-oriented paradigm for sustainable computing ecosystems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_14823 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Optimal Repurchasing Contract Design for Efficient Utilization of Computing Resources Deng, Zhengyan Zheng, Yusen Sheng, Chenliang Qin, Shaowen Computer Science and Game Theory The rapid advancement of AI and other emerging technologies has triggered exponential growth in computing resources demand. Faced with prohibitive infrastructure costs for large-scale computing clusters, users are increasingly resorting to leased computing resources from third-party providers. However, prevalent overestimation of operational requirements frequently leads to substantial underutilization of the computing resources. To mitigate such inefficiency, we propose a contract-based incentive framework for computing resources repurchasing. Comparing to auction mechanisms, our design enables providers to reclaim and reallocate surplus computing resources through market-driven incentives. Our framework operates in a multi-parameter environment where both clients' idle resource capacities and their unit valuations of retained resources are private information, posing a significant challenge to contract design. Two scenarios are considered based on whether all clients possess the same amount of idle resource capacity. By transforming the contract design problem into solving a mathematical program, we obtain the optimal contracts for each scenario, which can maximize the utility of computing resources providers while ensuring the requirements of incentive compatibility (IC) and individual rationality (IR). This innovative design not only provides an effective approach to reduce the inefficient utilization of computing resources, but also establishes a market-oriented paradigm for sustainable computing ecosystems. |
| title | Optimal Repurchasing Contract Design for Efficient Utilization of Computing Resources |
| topic | Computer Science and Game Theory |
| url | https://arxiv.org/abs/2504.14823 |