SCAREY: Location-Aware Service Lifecycle Management

2025-05-16

AI Summary

To address the joint optimization challenge of low latency, low cost, and low carbon emissions in service lifecycle management across the edge/fog/cloud continuum, this paper proposes a user-location-aware dynamic state machine framework that unifies service discovery, provisioning, deployment, and monitoring. Innovatively, geographic information is embedded into the state machine to enable on-demand switching of service discoverability; furthermore, a network-aware adaptive deployment algorithm is designed to jointly optimize latency, operational cost, and carbon emissions. Experimental evaluation demonstrates that the proposed approach improves service discovery and acquisition speed by 73%, reduces operational costs by 45%, and cuts energy consumption and CO₂ emissions by over 57%. The framework delivers a scalable, deployable, full-stack management paradigm for sustainable edge intelligence.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Learning on the Edge & Model Compression
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Scheduling services within the computing continuum is complex due to the dynamic interplay of the Edge, Fog, and Cloud resources, each offering distinct computational and networking advantages. This paper introduces SCAREY, a user location-aided service lifecycle management framework based on state machines. SCAREY addresses critical service discovery, provisioning, placement, and monitoring challenges by providing unified dynamic state machine-based lifecycle management, allowing instances to transition between discoverable and non-discoverable states based on demand. It incorporates a scalable service deployment algorithm to adjust the number of instances and employs network measurements to optimize service placement, ensuring minimal latency and enhancing sustainability. Real-world evaluations demonstrate a 73% improvement in service discovery and acquisition times, 45% cheaper operating costs and over 57% less power consumption and lower CO2 emissions compared to existing related methods.

Problem

• Manages service lifecycle across Edge, Fog, and Cloud dynamically
• Optimizes service placement for minimal latency and sustainability
• Improves discovery speed and reduces costs and power consumption

Innovation

• Location-aided state machine lifecycle management
• Scalable service deployment algorithm
• Network measurements for optimized placement