Skip to content
#edge computing Dataset Open access

SPARF research package: code, locked evaluation protocol, predictions, and results

Oct 2026 · Zenodo (CERN European Organization for Nuclear Research)

Abstract

Research package supporting the article "SPARF: Seasonal Profile-Assisted Ridge Forecasting for Proactive Resource Hotspot Prediction in Edge Computing" (J.-P. Yang), prepared for submission to Computer Networks. It contains the experiment programs, the locked evaluation protocol, VM roles and data hashes, saved baseline models, per-origin predictions, coefficients, formal SPARF evaluation results, same-run latency measurements, the supplementary test comparison, audit records, and figures. recompute_metrics.py reproduces the reported metrics, bootstrap intervals, and warning coverage from the saved predictions without the raw traces or retraining; verify_package.py checks file integrity against SHA256SUMS. The raw GWA-T-13 Materna traces are not redistributed; they are available from the Grid Workloads Archive and were provided by Materna GmbH Information & Communications, Dortmund, Germany.

View source

Similar papers

#computer vision Review Sep 2017

Agile Software Development Methods: Review and Analysis

This publication proposes a definition and a classification of agile software development approaches and analyses ten software development methods that can be characterized as being "agile" against the defined criterion.

P. Abrahamsson, O. Salo, Jussi Ronkainen et al. · 727 citations · ⚡54
#computer vision Jun 2008

The impact of agile practices on communication in software development

The study shows that agile practices improve both informal and formal communication, but indicates that, in larger development situations involving multiple external stakeholders, a mismatch of adequate communication mechanisms can sometimes even hinder the communication.

M. Pikkarainen, Jukka Haikara, O. Salo et al. · 401 citations · ⚡48
#machine learning Review Open access Oct 2014

Software development in startup companies: A systematic mapping study

The results indicate that software engineering work practices are chosen opportunistically, adapted and configured to provide value under the constrains imposed by the startup context.

Nicolò Paternoster, Carmine Giardino, M. Unterkalmsteiner et al. · 394 citations · ⚡54

Related blog posts

Microsoft Research Blog Oct 6, 2026

What AI gets wrong and what failure teaches us

Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity.  The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.