Información de la conferencia
SIGMETRICS 2026: International Conference on Measurement and Modeling of Computer Systems
https://www.sigmetrics.org/sigmetrics2026/
Día de Entrega:
2026-01-06
Fecha de Notificación:
2026-03-13
Fecha de Conferencia:
2026-06-08
Ubicación:
Ann Arbor, Michigan, USA
Años:
52
CCF: b   CORE: a*   QUALIS: a1   Vistas: 207496   Seguidores: 173   Asistentes: 16

Solicitud de Artículos
Overview

The 2026 ACM SIGMETRICS conference will be held at the University of Michigan, Ann Arbor, USA from June 8-12, 2026.

We solicit papers on the design, modeling, measurement, and performance analysis of computer systems. A good submission typically focuses on a well-motivated problem in computer systems, proposes a principled solution using modeling, measurement, and performance analysis, and empirically validates the solution through simulations and/or implementation. The research may target systems in a broad range of domains, including networking, high-performance computing, machine learning, IoT, cloud, edge, multimedia, mobile, and cyber-physical systems. The work may target diverse system metrics such as latency, throughput, reliability, cost, accuracy, fairness, energy, and carbon footprint.

The conference presents papers that have been accepted to appear in the Proceedings of the ACM on Measurement and Analysis of Computing Systems (POMACS). There are multiple deadlines to submit your work in time for a presentation at the conference (see below). A panel of reviewers will consider all accepted and eligible papers for the Best Paper Award and the Kenneth C. Sevcik Outstanding Student Paper Award. Additionally, a selection of the accepted papers, as identified by a panel of reviewers, may be nominated for publication as Research Highlights in the Communications of the ACM (CACM).

Topics

We solicit papers describing original and previously unpublished research. Specific topics of interest include, but are not limited to, the following.

Machine Learning and AI

    Machine learning and AI: Theory
    Machine learning and AI: Empirical studies and applications
    Machine learning for systems
    Systems for machine learning

Measurement & Applied Modeling

    Anomaly detection, problem diagnosis, and troubleshooting
    System measurement, performance monitoring, and forecasting
    Experimental design, statistical analysis, simulation
    Workload characterization and benchmarking

Systems

    Computer and communication networks: protocols and algorithms
    Computer architecture, hardware accelerators, multi-core processors, memory systems and storage networks
    Cloud computing, fog and edge computing systems
    Energy-efficient and carbon-aware computing systems
    Mobile and personal computing systems
    Operating systems, file systems, and databases
    Real-time and fault-tolerant systems
    Security and privacy of computing and networked systems
    Software systems and services, and enterprise applications
    Wireless, mobile, ad-hoc and sensor networks, IoT applications

Theory

    Analytical modeling techniques and model validation
    Capacity planning, resource allocation, and scheduling
    Control theory
    Game theory, network economics, and platform design
    Graph algorithms and analysis
    Queueing theory and applications

Emerging Topics and Application Areas

    Blockchain and crypto-currency
    Cyber-physical systems, including smart grids
    High-performance computing
    Quantum computing and communication
    Reproducibility and replicability (tools and methodology)
    Social networks, multimedia systems, Web services
    Sustainability and power management
Última Actualización Por Dou Sun en 2025-09-13
Coeficiente de Aceptación
AñoEnviadosAceptadosAceptados(%)
2021841416.7%
20202864415.4%
20193175015.8%
20182705420%
20172032713.3%
20162082813.5%
20152393213.4%
20142374016.9%
20131962613.3%
20122033115.3%
20111772916.4%
20101842915.8%
20091802715%
20082013617.9%
20071702917.1%
20062173013.8%
20052373113.1%
20042653312.5%
20032222611.7%
20021702313.5%
20012333012.9%
20001652817%
1999921819.6%
19981362518.4%
19971302519.2%
19961102421.8%
19951232722%
19941102522.7%
19921082018.5%
19901022322.5%
19891362216.2%
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