Low-Latency Layer-Aware Proactive and Passive Container Migration in Meta Computing

TL;DR

This paper proposes low-latency, layer-aware container migration strategies in meta computing, utilizing reinforcement learning to optimize proactive and passive migrations and significantly reduce total latency.

Contribution

It introduces a novel formulation of container migration considering layer dependencies and develops a reinforcement learning approach with expert demonstrations for improved decision-making.

Findings

Reinforcement learning algorithm reduces total migration latency.

Layer dependency consideration decreases migration costs.

Proposed strategies outperform baseline algorithms in experiments.

Abstract

Meta computing is a new computing paradigm that aims to efficiently utilize all network computing resources to provide fault-tolerant, personalized services with strong security and privacy guarantees. It also seeks to virtualize the Internet as many meta computers. In meta computing, tasks can be assigned to containers at edge nodes for processing, based on container images with multiple layers. The dynamic and resource-constrained nature of meta computing environments requires an optimal container migration strategy for mobile users to minimize latency. However, the problem of container migration in meta computing has not been thoroughly explored. To address this gap, we present low-latency, layer-aware container migration strategies that consider both proactive and passive migration. Specifically: 1) We formulate the container migration problem in meta computing, taking into account layer dependencies to reduce migration costs and overall task duration by considering four delays. 2) We introduce a reinforcement learning algorithm based on policy gradients to minimize total latency by identifying layer dependencies for action selection, making decisions for both proactive and passive migration. Expert demonstrations are introduced to enhance exploitation. 3) Experiments using real data trajectories show that the algorithm outperforms baseline algorithms, achieving lower total latency.