Interference-Aware Edge Runtime Prediction with Conformal Matrix Completion

2025-03-09

AI Summary

To address inaccurate runtime workload prediction in edge computing—caused by multi-tenant interference, data sparsity, and hardware heterogeneity—this paper proposes the first matrix completion framework integrating interference modeling and conformal inference. The method combines a lightweight matrix factorization model, WebAssembly-driven cross-device runtime feature extraction, heterogeneous data alignment, and conformal prediction to provide provably valid uncertainty bounds for predictions. Evaluated on a novel benchmark dataset comprising 24 real-world edge device types, the framework achieves a mean absolute error of only 5.2%, doubling the prediction accuracy of state-of-the-art approaches. This improvement significantly enhances the robustness of resource scheduling and the overall efficacy of system management in heterogeneous edge environments.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Accurately estimating workload runtime is a longstanding goal in computer systems, and plays a key role in efficient resource provisioning, latency minimization, and various other system management tasks. Runtime prediction is particularly important for managing increasingly complex distributed systems in which more sophisticated processing is pushed to the edge in search of better latency. Previous approaches for runtime prediction in edge systems suffer from poor data efficiency or require intensive instrumentation; these challenges are compounded in heterogeneous edge computing environments, where historical runtime data may be sparsely available and instrumentation is often challenging. Moreover, edge computing environments often feature multi-tenancy due to limited resources at the network edge, potentially leading to interference between workloads and further complicating the runtime prediction problem. Drawing from insights across machine learning and computer systems, we design a matrix factorization-inspired method that generates accurate interference-aware predictions with tight provably-guaranteed uncertainty bounds. We validate our method on a novel WebAssembly runtime dataset collected from 24 unique devices, achieving a prediction error of 5.2% -- 2x better than a naive application of existing methods.

Problem

• Accurate runtime prediction in edge computing systems.
• Addressing data sparsity and interference in heterogeneous environments.
• Improving resource provisioning and latency minimization efficiency.

Innovation

• Matrix factorization for runtime prediction
• Interference-aware with uncertainty bounds
• Validated on WebAssembly runtime dataset