A Conflict-Aware Resource Management Framework for the Computing Continuum

TL;DR

This paper introduces a DRL-based framework for resolving resource conflicts in the computing continuum, enhancing efficiency and resilience in heterogeneous, decentralized environments.

Contribution

It presents a novel adaptive conflict resolution framework using deep reinforcement learning for resource orchestration across edge, fog, and cloud.

Findings

Achieves efficient resource reallocation in dynamic scenarios

Demonstrates architectural resilience and adaptability

Prototyped on a Kubernetes-based testbed

Abstract

The increasing device heterogeneity and decentralization requirements in the computing continuum (i.e., spanning edge, fog, and cloud) introduce new challenges in resource orchestration. In such environments, agents are often responsible for optimizing resource usage across deployed services. However, agent decisions can lead to persistent conflict loops, inefficient resource utilization, and degraded service performance. To overcome such challenges, we propose a novel framework for adaptive conflict resolution in resource-oriented orchestration using a Deep Reinforcement Learning (DRL) approach. The framework enables handling resource conflicts across deployments and integrates a DRL model trained to mediate such conflicts based on real-time performance feedback and historical state information. The framework has been prototyped and validated on a Kubernetes-based testbed, illustrating its methodological feasibility and architectural resilience. Preliminary results show that the framework achieves efficient resource reallocation and adaptive learning in dynamic scenarios, thus providing a scalable and resilient solution for conflict-aware orchestration in the computing continuum.