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Fully Distributed Fog Load Balancing with Multi-Agent Reinforcement Learning

Maad Ebrahim, Abdelhakim Hafid

TL;DR

The paper addresses real-time IoT workloads by optimizing load balancing in Fog networks using fully distributed multi-agent reinforcement learning (MARL). Independent agents deployed at IoT APs learn local load-distribution policies over regional candidate Fog nodes, aided by lifelong transfer learning and interval-based Gossip observations to reflect realistic communication timing without inter-agent coordination. Key contributions include a scalable fully distributed MARL framework, region-based decomposition to reduce state/action complexity, and a realism-versus-performance analysis of interval-based observations. Results show that independently trained agents achieve faster convergence and lower waiting delays with fair resource utilization, while acknowledging a practical trade-off when observations are not real-time; the approach is positioned as deployment-ready for global-scale Fog environments.

Abstract

Real-time Internet of Things (IoT) applications require real-time support to handle the ever-growing demand for computing resources to process IoT workloads. Fog Computing provides high availability of such resources in a distributed manner. However, these resources must be efficiently managed to distribute unpredictable traffic demands among heterogeneous Fog resources. This paper proposes a fully distributed load-balancing solution with Multi-Agent Reinforcement Learning (MARL) that intelligently distributes IoT workloads to optimize the waiting time while providing fair resource utilization in the Fog network. These agents use transfer learning for life-long self-adaptation to dynamic changes in the environment. By leveraging distributed decision-making, MARL agents effectively minimize the waiting time compared to a single centralized agent solution and other baselines, enhancing end-to-end execution delay. Besides performance gain, a fully distributed solution allows for a global-scale implementation where agents can work independently in small collaboration regions, leveraging nearby local resources. Furthermore, we analyze the impact of a realistic frequency to observe the state of the environment, unlike the unrealistic common assumption in the literature of having observations readily available in real-time for every required action. The findings highlight the trade-off between realism and performance using an interval-based Gossip-based multi-casting protocol against assuming real-time observation availability for every generated workload.

Fully Distributed Fog Load Balancing with Multi-Agent Reinforcement Learning

TL;DR

The paper addresses real-time IoT workloads by optimizing load balancing in Fog networks using fully distributed multi-agent reinforcement learning (MARL). Independent agents deployed at IoT APs learn local load-distribution policies over regional candidate Fog nodes, aided by lifelong transfer learning and interval-based Gossip observations to reflect realistic communication timing without inter-agent coordination. Key contributions include a scalable fully distributed MARL framework, region-based decomposition to reduce state/action complexity, and a realism-versus-performance analysis of interval-based observations. Results show that independently trained agents achieve faster convergence and lower waiting delays with fair resource utilization, while acknowledging a practical trade-off when observations are not real-time; the approach is positioned as deployment-ready for global-scale Fog environments.

Abstract

Real-time Internet of Things (IoT) applications require real-time support to handle the ever-growing demand for computing resources to process IoT workloads. Fog Computing provides high availability of such resources in a distributed manner. However, these resources must be efficiently managed to distribute unpredictable traffic demands among heterogeneous Fog resources. This paper proposes a fully distributed load-balancing solution with Multi-Agent Reinforcement Learning (MARL) that intelligently distributes IoT workloads to optimize the waiting time while providing fair resource utilization in the Fog network. These agents use transfer learning for life-long self-adaptation to dynamic changes in the environment. By leveraging distributed decision-making, MARL agents effectively minimize the waiting time compared to a single centralized agent solution and other baselines, enhancing end-to-end execution delay. Besides performance gain, a fully distributed solution allows for a global-scale implementation where agents can work independently in small collaboration regions, leveraging nearby local resources. Furthermore, we analyze the impact of a realistic frequency to observe the state of the environment, unlike the unrealistic common assumption in the literature of having observations readily available in real-time for every required action. The findings highlight the trade-off between realism and performance using an interval-based Gossip-based multi-casting protocol against assuming real-time observation availability for every generated workload.
Paper Structure (7 sections, 1 theorem, 11 figures, 3 tables)

This paper contains 7 sections, 1 theorem, 11 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. The Fog System Model
  4. Distributed RL Load Balancing
  5. Performance Evaluation
  6. Performance Analysis
  7. Conclusion

Key Result

Lemma 4.1

Given two non-overlapping sets of candidate Fog nodes $\mathcal{F}_1 \cap \mathcal{F}_2 = \emptyset$ in two collaboration regions (see Fig. fig:overlapping). The resources in each region are optimized by a group of agents that blindly collaborate without knowing the actions of other agents in that r

Figures (11)

  • Figure 1: A simplified Fog network with overlapping collaboration regions.
  • Figure 2: Workflow of workload distribution.
  • Figure 3: Transmission of Fog queue information.
  • Figure 4: Overlapping vs. non-overlapping sets of candidate Fog nodes for two coordinating regions.
  • Figure 5: Lifelong learning for RL agents.
  • ...and 6 more figures

Theorems & Definitions (2)

  • Lemma 4.1
  • proof