A Fragmentation-Aware Adaptive Bilevel Search Framework for Service Mapping in Computing Power Networks

2025-07-10

AI Summary

To address low resource utilization and insufficient service satisfaction caused by service mapping in Computing Power Networks (CPNs), this paper proposes an Adaptive Bilevel Search (ABS) framework. ABS decouples service dependencies from resource couplings via graph partitioning, establishing a bilevel optimization architecture that synergistically integrates global exploration at the upper level with local refinement at the lower level. It further introduces a fragmentation-aware evaluation mechanism to enhance adaptability to dynamic, heterogeneous computing resources. Methodologically, ABS integrates graph partitioning theory, bilevel optimization modeling, and a distributed particle swarm optimization algorithm, supporting modular extensibility. Experimental results under complex dynamic scenarios demonstrate that ABS achieves up to a 73.2% improvement in resource utilization and a 60.2% increase in service acceptance rate over state-of-the-art approaches, significantly enhancing both mapping efficiency and service quality.

Technology Category

• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Mixed Discrete/Continuous Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• 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

Abstract

Computing Power Network (CPN) unifies wide-area computing resources through coordinated network control, while cloud-native abstractions enable flexible resource orchestration and on-demand service provisioning atop the elastic infrastructure CPN provides. However, current approaches fall short of fully integrating computing resources via network-enabled coordination as envisioned by CPN. In particular, optimally mapping services to an underlying infrastructure to maximize resource efficiency and service satisfaction remains challenging. To overcome this challenge, we formally define the service mapping problem in CPN, establish its theoretical intractability, and identify key challenges in practical optimization. We propose Adaptive Bilevel Search (ABS), a modular framework featuring (1) graph partitioning-based reformulation to capture variable coupling, (2) a bilevel optimization architecture for efficient global exploration with local optimality guarantees, and (3) fragmentation-aware evaluation for global performance guidance. Implemented using distributed particle swarm optimization, ABS is extensively evaluated across diverse CPN scenarios, consistently outperforming existing approaches. Notably, in complex scenarios, ABS achieves up to 73.2% higher computing resource utilization and a 60.2% higher service acceptance ratio compared to the best-performing baseline.

Problem

• Optimally mapping services to maximize resource efficiency
• Integrating computing resources via network-enabled coordination
• Overcoming fragmentation challenges in service mapping

Innovation

• Graph partitioning-based reformulation captures variable coupling
• Bilevel optimization ensures efficient global exploration
• Fragmentation-aware evaluation guides global performance