Reliability is Blind: Collective Incentives for Decentralized Computing Marketplaces without Individual Behavior Information

2025-03-24

AI Summary

Decentralized cloud computing markets face challenges in ensuring service reliability (SLA compliance) due to the absence of individual behavioral observability and inherent information asymmetry. Method: This paper proposes a collective incentive mechanism that operates without monitoring individual agents. Its core innovation is “blind collective punishment”: upon task failure, all participants incur uniform penalties; risk propagation is formally modeled via bankruptcy theory, enabling system-level behavioral self-adaptation. The design integrates game-theoretic analysis and distributed mechanism design, validated through large-scale asset-pool simulations. Contribution/Results: The mechanism robustly improves task success rates, autonomously filters out fault-prone nodes, and drives market-wide reliability to converge above a pre-specified SLA threshold. Crucially, it breaks from conventional incentive paradigms reliant on individual observability—achieving endogenous SLA compliance even under untrustworthy or inaccessible behavioral information.

Technology Category

• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Mechanism Design
• Search and Optimization: Distributed Search

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Social and technical mechanisms of refusal for web technologies and applications
• Security and Privacy: Data transparency and provenance

Abstract

In decentralized cloud computing marketplaces, ensuring fair and efficient interactions among asset providers and end-users is crucial. A key concern is meeting agreed-upon service-level objectives like the service's reliability. In this decentralized context, traditional mechanisms often fail to address the complexity of task failures, due to limited available and trustworthy insights into these independent actors' individual behavior. This paper proposes a collective incentive mechanism that blindly punishes all involved parties when a task fails. Based on ruin theory, we show that Collective Incentives improve behavior in the marketplace by creating a disincentive for faults and misbehavior even when the parties at fault are unknown, in turn leading to a more robust marketplace. Simulations for small and large pools of marketplace assets show that Collective Incentives enable to meet or exceed a reliability target, i.e., the success-rate of tasks run using marketplace assets, by eventually discarding failure-prone assets while preserving reliable ones.

Problem

• Ensuring fair decentralized cloud computing interactions
• Addressing task failures without individual behavior data
• Improving marketplace reliability via collective incentives

Innovation

• Collective incentives for decentralized computing marketplaces
• Blind punishment mechanism based on ruin theory
• Improves reliability by discarding failure-prone assets