A Conflict-Aware Resource Management Framework for the Computing Continuum
Vlad Popescu-Vifor, Ilir Murturi, Praveen Kumar Donta, Schahram Dustdar

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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
