Optimal Posted Prices for Online Cloud Resource Allocation
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Abstract (arXiv)
We study online resource allocation in a cloud computing platform, through a posted pricing mechanism: The cloud provider publishes a unit price for each resource type, which may vary over time; upon arrival at the cloud system, a cloud user either takes the current prices, renting resources to execute its job, or refuses the prices without running its job there. We design pricing functions based on the current resource utilization ratios, in a wide array of demand-supply relationships and resource occupation durations, and prove worst-case competitive ratios of the pricing functions in terms of social welfare. In the basic case of a single-type, non-recycled resource (i.e., allocated resources are not later released for reuse), we prove that our pricing function design is optimal, in that any other pricing function can only lead to a worse competitive ratio. Insights obtained from the basic cases are then used to generalize the pricing functions to more realistic cloud systems with multiple types of resources, where a job occupies allocated resources for a number of time slots till completion, upon which time the resources are returned back to the cloud resource pool.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Apr 2017.
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Citing papers per year: 2021: 2; 2022: 0; 2023: 2; 2024: 3; 2025: 1.
8 papers.
- 2021Pareto-Optimal Learning-Augmented Algorithms for Online Conversion Problemscited by 19
- 2021Adversarial Deep Learning for Online Resource Allocationcited by 7
- 2024Near-Optimal Consistency-Robustness Trade-Offs for Learning-Augmented Online Knapsack Problemscited by 5
- 2023Time Fairness in Online Knapsack Problemscited by 3
- 2024LACS: Learning-Augmented Algorithms for Carbon-Aware Resource Scaling with Uncertain Demandcited by 2
- 2023A Competition-based Pricing Strategy in Cloud Markets using Regret Minimization Techniquescited by 0
- 2024Learning-Augmented Competitive Algorithms for Spatiotemporal Online Allocation with Deadline Constraintscited by 0
- 2025Online Multi-Class Selection with Group Fairness Guaranteecited by 0
- 2025Online Multi-Class Selection with Group Fairness Guarantee23 Oct 2025
- 2024Learning-Augmented Competitive Algorithms for Spatiotemporal Online Allocation with Deadline Constraints14 Aug 2024
- 2024Near-Optimal Consistency-Robustness Trade-Offs for Learning-Augmented Online Knapsack Problems26 Jun 2024
- 2024LACS: Learning-Augmented Algorithms for Carbon-Aware Resource Scaling with Uncertain Demand29 Mar 2024
- 2023A Competition-based Pricing Strategy in Cloud Markets using Regret Minimization Techniques20 Sep 2023
- 2023Time Fairness in Online Knapsack Problems22 May 2023
- 2021Adversarial Deep Learning for Online Resource Allocation19 Nov 2021
- 2021Pareto-Optimal Learning-Augmented Algorithms for Online Conversion Problems3 Sep 2021