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

Mengjie Liu, Yihua Li, Fangyi Mou, Zhiqing Tang, Jiong Lou, Jianxiong Guo, Weijia Jia

TL;DR

This work forms the container migration problem in meta computing, taking into account layer dependencies to reduce migration costs and overall task duration by considering four delays, and introduces a reinforcement learning algorithm based on policy gradients to minimize total latency.

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.

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

TL;DR

This work forms the container migration problem in meta computing, taking into account layer dependencies to reduce migration costs and overall task duration by considering four delays, and introduces a reinforcement learning algorithm based on policy gradients to minimize total latency.

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.
Paper Structure (16 sections, 25 equations, 7 figures, 1 table, 1 algorithm)

This paper contains 16 sections, 25 equations, 7 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Service Migration
  4. Layer-aware Scheduling
  5. System Model
  6. System Model
  7. Cost
  8. Problem Formulation and Analysis
  9. Our Algorithms
  10. Algorithm Settings
  11. Expert Demonstrations
  12. Migration Policy
  13. Evaluation
  14. Experimental Settings
  15. Experimental Results
  16. ...and 1 more sections

Figures (7)

  • Figure 1: Example of container migration in meta computing.
  • Figure 2: Overview of the layer-aware proactive and passive container migration algorithm in meta computing.
  • Figure 3: Performance with different number of nodes
  • Figure 4: Total latency
  • Figure 5: Performance with different number of tasks
  • ...and 2 more figures