POSEIDON : Efficient Function Placement at the Edge using Deep Reinforcement Learning

TL;DR

POSEIDON employs deep reinforcement learning to optimize function placement at the edge, effectively reducing latency and resource use in highly dynamic environments.

Contribution

This paper introduces POSEIDON, a novel DRL-based approach using PPO for efficient function placement in edge computing, addressing dynamic workload challenges.

Findings

Reduces execution time significantly

Lowers network delay effectively

Consumes fewer resources

Abstract

Edge computing allows for reduced latency and operational costs compared to centralized cloud systems. In this context, serverless functions are emerging as a lightweight and effective paradigm for managing computational tasks on edge infrastructures. However, the placement of such functions in constrained edge nodes remains an open challenge. On one hand, it is key to minimize network delays and optimize resource consumption; on the other hand, decisions must be made in a timely manner due to the highly dynamic nature of edge environments. In this paper, we propose POSEIDON, a solution based on Deep Reinforcement Learning for the efficient placement of functions at the edge. POSEIDON leverages Proximal Policy Optimization (PPO) to place functions across a distributed network of nodes under highly dynamic workloads. A comprehensive empirical evaluation demonstrates that POSEIDON significantly reduces execution time, network delay, and resource consumption compared to state-of-the-art methods.