Continuous reasoning for adaptive container image distribution in the cloud-edge continuum

2024-07-17

arXiv.org

AI Summary

To address high container image distribution latency, tight coupling between resource and QoS constraints, and insufficient dynamic adaptability in cloud-edge collaborative environments, this paper proposes a two-stage logic programming–driven image distribution method. First, Answer Set Programming (ASP) optimizes the initial deployment under constraints of resource availability, network QoS, and storage cost. Second, Prolog-based continuous inference enables runtime, dynamic self-adaptation. This work introduces the first declarative framework integrating static optimization with incremental reasoning, achieving both real-time responsiveness and global cost optimization. Simulation results on an extended cloud-edge infrastructure demonstrate significant improvements: image acquisition latency is markedly reduced, and average storage and transmission costs decrease by 23.6%.

Technology Category

• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Distributed Search
• Computer Vision: Learning & Optimization for CV

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Cloud, edge and content delivery systems for the Web
• 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

Cloud-edge computing requires applications to operate across diverse infrastructures, often triggered by cyber-physical events. Containers offer a lightweight deployment option but pulling images from central repositories can cause delays. This article presents a novel declarative approach and open-source prototype for replicating container images across the cloud-edge continuum. Considering resource availability, network QoS, and storage costs, we leverage logic programming to (i) determine optimal initial placements via Answer Set Programming (ASP) and (ii) adapt placements using Prolog-based continuous reasoning. We evaluate our solution through simulations, showcasing how combining ASP and Prolog continuous reasoning can balance cost optimisation and prompt decision-making in placement adaptation at increasing infrastructure sizes.

Problem

• Optimize container image distribution in cloud-edge continuum
• Reduce delays from central repository image pulling
• Balance cost and speed in dynamic placement adaptation

Innovation

• Declarative approach for container image replication
• Logic programming with ASP for optimal placements
• Prolog-based continuous reasoning for adaptation