Papers for

edge system operators

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Dynamic scaling improves latency and resource use in edge stream processing

A Dynamic Vertical Scaling Strategy for Distributed Stream Processing Applications in Edge Computing

Abstract: Distributed Stream Processing applications at the edge must reconcile low latency and high throughput with limited and heterogeneous resources. This paper presents a dynamic vertical scaling strategy based on Proximal Policy Optimization, formulated as a Partially Observable Markov Decision Process. The policy jointly adjusts task allocations and prioritizes compliance with a p95 end-to-end latency Service Level Objective. In EdgeStreamPy simulation experiments with two application profiles, two workloads, and ten paired placements per combination, the selected policies preserved throughput, obtained mean violation rates from 0.03% to 0.30%, below VRebalance in every scenario, and used less CPU in three of four combinations. The comparison covers complete controller configurations with different decision frequencies.

Fri 11 SeptDistributed, Parallel, and Cluster Computing

The gist

Distributed stream processing at the edge faces tough choices because devices have limited and different resources but need to process data quickly and smoothly. The authors developed a smart method using machine learning to decide how to best allocate tasks and resources dynamically, aiming to keep delays low most of the time. Their tests in simulations showed this method kept the data flowing well, violated latency goals very rarely, and often used less CPU compared to previous approaches. This helps edge computing systems work better under changing conditions.

Open → 2609.12975v1