Distributed Resource Selection for Self-Organising Cloud-Edge Systems

2025-10-09

AI Summary

In cloud-edge collaborative environments, high system dynamics and large scale render centralized resource scheduling prone to bottlenecks. Method: This paper proposes a decentralized, distributed resource selection mechanism integrating multi-agent coordination, context-awareness, and a lightweight consensus protocol, enabling self-organized decision-making based solely on local state information—thereby eliminating costly global coordination. Contribution/Results: Compared to conventional centralized heuristic approaches, the proposed mechanism achieves up to 30× faster resource allocation in large-scale dynamic scenarios while maintaining near-optimal solution quality. Experimental evaluation demonstrates significant improvements in system scalability, robustness, and real-time adaptability. The approach establishes a low-latency, highly elastic foundation for self-organizing orchestration of complex distributed applications.

Technology Category

• Multiagent Systems: Coordination and Collaboration
• Search and Optimization: Distributed Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

This paper presents a distributed resource selection mechanism for diverse cloud-edge environments, enabling dynamic and context-aware allocation of resources to meet the demands of complex distributed applications. By distributing the decision-making process, our approach ensures efficiency, scalability, and resilience in highly dynamic cloud-edge environments where centralised coordination becomes a bottleneck. The proposed mechanism aims to function as a core component of a broader, distributed, and self-organising orchestration system that facilitates the intelligent placement and adaptation of applications in real-time. This work leverages a consensus-based mechanism utilising local knowledge and inter-agent collaboration to achieve efficient results without relying on a central controller, thus paving the way for distributed orchestration. Our results indicate that computation time is the key factor influencing allocation decisions. Our approach consistently delivers rapid allocations without compromising optimality or incurring additional cost, achieving timely results at scale where exhaustive search is infeasible and centralised heuristics run up to 30 times slower.

Problem

• Distributed resource selection for cloud-edge systems
• Dynamic context-aware allocation for distributed applications
• Efficient scalable resilient cloud-edge coordination

Innovation

• Distributed resource selection mechanism for cloud-edge systems
• Consensus-based mechanism without central controller
• Rapid allocations without compromising optimality or cost