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BenchCouncil: International Open Benchmark Council

 

Aims and Scopes

It publishes a range of papers, including letters, research articles, survey articles, benchmark articles, standard articles, data articles, tool articles, practice articles, and comments on previously published papers. Particular areas of interest include, but are not limited to:

  • Benchmark and standard specifications, implementations, and validations of:
    • Big Data
    • Artificial intelligence (AI)
    • High performance computing (HPC)
    • Machine learning
    • Big scientific data
    • Datacenters
    • Cloud
    • Warehouse-scale computing
    • Mobile robotics
    • Edge and fog computing
    • Internet of Things (IoT)
    • Blockchain
    • Data management and storage
    • Science domains
    • Medical domains
    • Financial domains
    • Education domains
    • Other application domains
  • Open access data sets:
    • Detailed descriptions of research or industry data sets, including the methods used to collect the data and technical analyses supporting the measurements' quality.
    • Analyses or meta-analyses of existing data and original articles on systems, technologies, and techniques that advance data sharing and reuse to support reproducible research.
    • Evaluations of the rigor and quality of the experiments used to generate data and the completeness of the data's descriptions.
    • Tools generating large-scale data while preserving their original characteristics.
  • Workload characterization, quantitative measurement, design and evaluation studies of:
    • Computer and communication networks, protocols, and algorithms
    • Wireless, mobile, ad-hoc and sensor networks, IoT applications
    • Computer architectures, hardware accelerators, multi-core processors, memory systems, and storage networks
    • HPC
    • Operating systems, file systems, and databases
    • Virtualization, data centers, distributed and cloud computing, fog, and edge computing
    • Mobile and personal computing systems
    • Energy-efficient computing systems
    • Real-time and fault-tolerant systems
    • Security and privacy of computing and networked systems
    • Software systems and services, and enterprise applications
    • Social networks, multimedia systems, web services
    • Cyber-physical systems, including the smart grid
  • Methodologies, abstractions, metrics, algorithms and tools for:
    • Analytical modeling techniques and model validation
    • Workload characterization and benchmarking
    • Performance, scalability, power, and reliability analysis
    • Sustainability analysis and power management
    • System measurement, performance monitoring, and forecasting
    • Anomaly detection, problem diagnosis, and troubleshooting
    • Capacity planning, resource allocation, run time management, and scheduling
    • Experimental design, statistical analysis, and simulation
  • Measurement and evaluation:
    • Evaluation methodologies and metrics
    • Testbed methodologies and systems
    • Instrumentation, sampling, tracing, and profiling of large-scale, real-world applications and systems
    • Collection and analysis of measurement data that yield new insights
    • Measurement-based modeling (e.g., workloads, scaling behavior, assessment of performance bottlenecks)
    • Methods and tools to monitor and visualize measurement and evaluation data
    • Systems and algorithms that build on measurement-based findings
    • Advances in data collection, analysis, and storage (e.g., anonymization, querying, sharing)
    • Reappraisal of previous empirical measurements and measurement-based conclusions
    • Descriptions of challenges and future directions that the measurement and evaluation community should pursue
  • Optimization methodologies and tools