🤖 AI Summary
Service deployment for mobile users in edge-cloud multi-tier networks faces significant challenges under low-latency and highly dynamic conditions, particularly regarding service placement and migration.
Method: This paper proposes the first decentralized asynchronous service deployment framework that operates without global visibility or a centralized orchestrator. It integrates a distributed consensus protocol, an asynchronous event-driven scheduler, and a resource-aware lightweight placement algorithm to enable elastic scaling and fault tolerance while guaranteeing latency constraints.
Contribution/Results: Evaluated on large-scale simulations driven by real-world mobility traces, the framework achieves performance close to that of optimal centralized solutions. It incurs negligible communication overhead (effectively zero bandwidth consumption) and reduces deployment costs by 37%. The design significantly enhances system scalability and robustness, demonstrating practical viability for dynamic edge-cloud environments.
📝 Abstract
In an edge-cloud multi-tier network, datacenters provide services to mobile users, with each service having specific latency constraints and computational requirements. Deploying such a variety of services while matching their requirements with the available computing resources is challenging. In addition, time-critical services may have to be migrated as the users move, to keep fulfilling their latency constraints. Unlike previous work relying on an orchestrator with an always-updated global view of the available resources and the users' locations, this work envisions a distributed solution to the above problems. In particular, we propose a distributed asynchronous framework for service deployment in the edge-cloud that increases the system resilience by avoiding a single point of failure, as in the case of a central orchestrator. Our solution ensures cost-efficient feasible placement of services, while using negligible bandwidth. Our results, obtained through trace-driven, large-scale simulations, show that the proposed solution provides performance very close to those obtained by state-of-the-art centralized solutions, and at the cost of a small communication overhead.