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ICPE 2027: International Conference on Performance Engineering

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投稿締切日:
2026-11-09 残り 13 日
通知日:
2027-01-25
開催日:
2027-05-24
開催地:
Gothenburg, Sweden
開催回数:
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ICORE: B   閲覧: 34199   フォロー: 7   参加: 3
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ICPE
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会伴インデックス (CP-I)

64.9 / 100
全 5,759 件中 第 447 位 · 上位 8%
学術的評価 (35%)
72
投稿の選択性 (20%) データなし — 中立の基準値 50 点として算入 —
開催回数 (20%)
79
コミュニティの注目度 (10%)
33
公開情報の充実度 (15%)
70

使用した入力: 収録ランク：ICORE B · 確認できる開催回数：18 · 会伴でフォローしている研究者：7 人 · 過去 24 か月にこのページを開いた研究者：5 人

公開情報で不足しているもの: 過去の採択率 (+4.5)
主催者は会議を認証申請したうえで、このページから直接追加できます。スコアは毎晩再計算されます。このスコアを上げるには

信頼度 80% — スコアのうち、中立の基準値ではなく実際に観測されたデータに基づく割合。 このスコアの算出方法 · ランキングを見る · アルゴリズム版 1.2 · 算出日 2026-10-26

論文募集

ICPE 2027 (International Conference on Performance Engineering) is a ICORE B conference held in Gothenburg, Sweden on 2027-05-24. The paper submission deadline is 2026-11-09. Acceptance notifications are sent on 2027-01-25.

Scope and Topics The ACM/SPEC International Conference on Performance Engineering (ICPE) is the leading international forum for presenting and discussing novel ideas, innovations, trends, and experiences in the field of performance engineering. Performance, energy efficiency, reliability, sustainability, and trustworthiness are central to the acceptance and long-term viability of modern computing systems. Digitalization brings computing technology closer to people and manages many aspects of their lives. In turn, human interaction, automation, and increasingly AI-enabled decision-making shape the behavior of computing systems. As a result, our systems become more complex, more adaptive, and more difficult to engineer, observe, and understand. ICPE brings together researchers and practitioners to report on open problems, state-of-the-art solutions, and in-progress research in performance engineering of software and systems, targeting performance and associated quality attributes such as efficiency, reliability, scalability, sustainability, and cost in all phases of the computing system lifecycle, from specification and development to run time and maintenance. Application Domains ICPE deals with performance and associated quality attributes across application domains. Examples include, but are not limited to: cyber-physical systems, IoT, industrial Internet, robotic systems, and autonomous systems communication networks, wireless systems, mobile systems, and distributed systems peer-to-peer environments and ad-hoc networks service-oriented architectures, microservices, web-based environments, cloud-native systems, and serverless applications big data systems, stream processing, graph processing, data analytics, and data science machine learning, artificial intelligence, LLM-based systems, AI infrastructure, and agentic AI applications HPC systems, cloud platforms, edge platforms, grid systems, fog computing, and accelerator-based platforms multimedia systems and applications resilient, adaptive, and self-managing systems and applications production software systems, observability platforms, and large-scale operational environments Types of Contributions ICPE 2027 invites contributions across the following tracks: Research track for novel research on the conference topics Industry track for research involving industry and production performance-engineering practice Industry Presentation track for presentations from industry practitioners showcasing their views and experiences in performance engineering (new) Journal-first track for contributions from recent journal publications Artifact track for sharing software, data, benchmarks, traces, and other reusable research artifacts Emerging research track for preliminary, exploratory, or vision contributions Poster and demo track for short interactive contributions Tutorials track for presentations by leading scientists and practitioners Workshops track for focused communities and specific aspects of performance engineering Data challenge track for addressing challenges presented by performance datasets Topics of Interest Topics of interest include, but are not limited to, the following. Measurement and Empirical Evaluation data collection techniques, simulation, measurement, instrumentation, profiling, tracing, telemetry, and observability controlled experiment design, data-driven experiments, diagnostics, and performance debugging data management and interchange, tool interoperability, and open performance datasets statistical analysis, exploration, visualization, visual data mining, and uncertainty-aware analysis measuring and evaluating performance and related attributes such as energy efficiency, carbon footprint, cost, stability, or resource usage measuring and evaluating dependability and related attributes such as security, resiliency, availability, privacy, or confidentiality measuring and evaluating algorithmic attributes such as learning efficiency, solution quality, model quality, and decision quality measurement and evaluation of AI/ML systems, LLM inference pipelines, and agentic workflows Modeling approaches and metrics for describing performance and related quality attributes modeling languages and formalisms, modeling methods and tools explanatory and predictive models, online models, causal models, and uncertainty-aware models model learning and extraction techniques model validation, calibration, transfer, and generalization across workloads and environments workload characterization, workload forecasting, and workload generation models for cost, energy, carbon, dependability, and performance tradeoffs Design and Development Processes requirements engineering, agile and experiment-driven techniques, DevOps, MLOps, and LLMOps performance-oriented design, software architectures, performance patterns, and performance antipatterns performance testing, regression detection, root cause analysis, and bottleneck identification humans in the loop, developer experience, operational usability, and ethical concerns performance engineering for AI-enabled, autonomous, and safety-critical systems Managing Systems at Runtime adaptive systems, monitoring, autotuning, elasticity, autoscaling, power management, and carbon-aware control virtualization and consolidation, resource scheduling, capacity management, and admission control anomaly detection, service-level objectives, service-level monitoring, incident response, and production observability runtime management of ML/AI inference, LLM serving, batching, routing, caching, and model selection performance and reliability of agentic systems, multi-agent systems, and human-AI operational workflows Platform-Related Optimizations parallel programming, multi-core systems, and many-core systems compiler optimizations, managed languages, and runtime optimization performance and efficiency of hardware accelerators, GPUs, TPUs, NPUs, and novel memory systems cloud, serverless, edge, container, microservice, Kubernetes-based platform optimizations, and lightweight virtualization technologies cost-performance, energy-performance, and latency-throughput tradeoffs in modern platforms Benchmarking design and standardization processes benchmark suites, investigative benchmarks, and benchmark governance benchmark synthesis, benchmark workload generation, and realistic production workloads benchmarking energy efficiency, carbon impact, resilience, stability, security, and related quality metrics benchmarks for AI/ML systems, LLM serving, agentic systems, observability pipelines, and cloud-native systems reproducibility, repeatability, and reusable benchmark artifacts
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