A Stackelberg Game Framework with Drainability Guardrails for Pricing and Scaling in Multi-Tenant GPU Cloud Platforms

[2604.16802] A Stackelberg Game Framework with Drainability Guardrails for Pricing and Scaling in Multi-Tenant GPU Cloud Platforms Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computer Science and Game Theory arXiv:2604.16802 (cs) [Submitted on 18 Apr 2026 ( v1 ), last revised 15 Aug 2026 (this version, v2)] Title: A Stackelberg Game Framework with Drainability Guardrails for Pricing and Scaling in Multi-Tenant GPU Cloud Platforms Authors: Junji Yan , Asrin Efe Yorulmaz , Hanchen Zhou , Tamer Başar View a PDF of the paper titled A Stackelberg Game Framework with Drainability Guardrails for Pricing and Scaling in Multi-Tenant GPU Cloud Platforms, by Junji Yan and 3 other authors View PDF HTML (experimental) Abstract: Modern Graphics Processing Unit (GPU)-backed services must satisfy strict latency service-level objectives (SLOs) while controlling spare-capacity costs. In multi-tenant GPU cloud platforms, this trade-off is inherently dynamic because workload demand is endogenous; specifically, pricing shapes the submissions of heterogeneous tenants, which subsequently impact congestion and delay. We formulate the joint pricing-and-scaling problem as a large-population Stackelberg game problem, and we derive an explicit equilibrium demand map. The resulting closed-loop model reveals a structural failure mode in which delay-insensitive workloads sustain a residual demand floor, making the backlog undrainable under bounded price and service capacity. This observation motivates a computable drainability guardrail that certifies uniformly negative backlog drift in the residual-demand regime. For any fixed price-capacity pair satisfying the drainability guardrail, we establish global convergence to a unique operating point under a checkable step-size condition. Building on this fixed-pair analysis, we further develop an optimizer-agnostic action shield that provides a negative-drift certificate for shielded execution in the residual regime of the dynamic problem and show empirically that it improves safety and robustness for model-free reinforcement learning (RL) in this setting. Comments: 8 pages, 4 figures. Revised version incorporating reviewer feedback; added dynamic negative-drift guarantee, clarified assumptions, and updated experiments Subjects: Computer Science and Game Theory (cs.GT) ; Systems and Control (eess.SY); Optimization and Control (math.OC) Cite as: arXiv:2604.16802 [cs.GT] (or arXiv:2604.16802v2 [cs.GT] for this version) https://doi.org/10.48550/arXiv.2604.16802 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Junji Yan [ view email ] [v1] Sat, 18 Apr 2026 03:22:10 UTC (142 KB) [v2] Sat, 15 Aug 2026 05:41:50 UTC (141 KB) Full-text links: Access Paper: View a PDF of the paper titled A Stackelberg Game Framework with Drainability Guardrails for Pricing and Scaling in Multi-Tenant GPU Cloud Platforms, by Junji Yan and 3 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.GT < prev | next > new | recent | 2026-04 Change to browse by: cs cs.SY eess eess.SY math math.OC References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... 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