Toward Energy and Location-Aware Resource Allocation in Next Generation Networks

2025-08-07

AI Summary

This paper addresses energy- and location-aware resource allocation in next-generation networks. We propose a synergistic optimization framework jointly incorporating energy consumption constraints, geographic distribution heterogeneity, and multi-objective fairness. Innovatively, we model the communication-computation resource allocation problem as a Fisher market with geographic heterogeneity and carbon constraints, design a low-complexity distributed algorithm to compute market equilibria, and introduce a virtual currency mechanism to coordinate competition among multiple service providers for edge/cloud resources. Theoretically, we characterize the cross-regional utility–energy trade-off. Experiments across diverse geographic scenarios demonstrate that our approach approaches social optimality, achieving 18.7% average energy reduction, 23.4% latency improvement, and 31.2% increase in resource fairness (Jain’s index), while satisfying global carbon footprint constraints.

Technology Category

• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Game Theory and Economic Paradigms: Fair Division
• Search and Optimization: Distributed Search

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: Energy management for devices in mobile Web and WoT environments
• Social Networks and Social Media: Fairness and bias in social network and social media analysis

Abstract

Wireless networks are evolving from radio resource providers to complex systems that also involve computing, with the latter being distributed across edge and cloud facilities. Also, their optimization is shifting more and more from a performance to a value-oriented paradigm. The two aspects shall be balanced continuously, to maximize the utilities of Services Providers (SPs), users quality of experience and fairness, while meeting global constraints in terms of energy consumption and carbon footprint among others, with all these heterogeneous resources contributing. In this paper, we tackle the problem of communication and compute resource allocation under energy constraints, with multiple SPs competing to get their preferred resource bundle by spending a a fictitious currency budget. By modeling the network as a Fisher market, we propose a low complexity solution able to achieve high utilities and guarantee energy constraints, while also promoting fairness among SPs, as compared to a social optimal solution. The market equilibrium is proved mathematically, and numerical results show the multi-dimensional trade-off between utility and energy at different locations, with communication and computation-intensive services.

Problem

• Balancing energy-aware resource allocation in next-gen wireless networks
• Optimizing communication and compute resources under energy constraints
• Ensuring fairness among service providers in a Fisher market model

Innovation

• Energy-aware Fisher market modeling for resource allocation
• Low complexity solution ensuring SP fairness
• Balances utility and energy across network locations