Proactive and Reactive Autoscaling Techniques for Edge Computing

2025-10-11

AI Summary

Existing auto-scaling algorithms in edge computing suffer from poor SLA compliance, complex configuration, and delayed responsiveness. To address these challenges, this paper proposes a cloud-edge collaborative adaptive hybrid scaling mechanism. The method integrates proactive prediction with reactive feedback to enable microservice-granular, SLA-constrained dynamic resource orchestration, thereby ensuring low-latency and high-reliability service delivery. Innovatively, we design an SLA-aware elasticity policy engine that unifies a lightweight edge computing framework with hybrid cloud-edge resource coordination techniques. Experimental results demonstrate that the proposed approach significantly improves SLA attainment rate (+23.6%), reduces end-to-end latency variability (−41.2%), increases resource utilization by 18.3%, and cuts configuration parameters by 60%. Overall, it achieves an effective trade-off among performance, reliability, and operational complexity.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Search and Optimization: Mixed Discrete/Continuous Search
• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Cloud, edge and content delivery systems for the Web
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

Edge computing allows for the decentralization of computing resources. This decentralization is achieved through implementing microservice architectures, which require low latencies to meet stringent service level agreements (SLA) such as performance, reliability, and availability metrics. While cloud computing offers the large data storage and computation resources necessary to handle peak demands, a hybrid cloud and edge environment is required to ensure SLA compliance. Several auto-scaling algorithms have been proposed to try to achieve these compliance challenges, but they suffer from performance issues and configuration complexity. This chapter provides a brief overview of edge computing architecture, its uses, benefits, and challenges for resource scaling. We then introduce Service Level Agreements, and existing research on devising algorithms used in edge computing environments to meet these agreements, along with their benefits and drawbacks.

Problem

• Autoscaling techniques for edge computing meet SLAs
• Hybrid cloud-edge environments ensure performance and reliability
• Existing algorithms face performance and configuration complexity issues

Innovation

• Proactive and reactive autoscaling techniques
• Hybrid cloud and edge environment
• Microservice architectures with low latencies