BenchCouncil Transactions on Benchmarks, Standards and Evaluations

Open access

ISSN: 2772-4859

BenchCouncil Transactions on Benchmarks, Standards and Evaluations

Open access

BenchCouncil Transactions on Benchmarks, Standards and Evaluations (TBench) publishes position articles that open new research areas; research articles that address new problems; methodologies; tools;...

BenchCouncil Transactions on Benchmarks, Standards and Evaluations (TBench) publishes position articles that open new research areas; research articles that address new problems; methodologies; tools; survey articles that build up comprehensive knowledge; and comment articles that argue published articles. Submissions should deal with benchmarks, standards and evaluation research areas. Particular areas of interest include, but are not limited to:

  1. Generalized benchmark science and engineering, for example:
    • Measurement standards
    • standardized data sets with defined properties
    • Representative workloads
    • Representative data sets
    • Best practices

  2. Benchmarks and standard specifications, implementations and validations of:
    • Big data
    • AI
    • HPC
    • Machine learning
    • Big scientific data
    • Data centers
    • Cloud
    • Warehouse-scale computing
    • Mobile robotics
    • Edge and fog computing
    • IoT
    • Blockchain
    • Data management and storage
    • Financial domains
    • Education domains
    • Medical domains
    • Other application domains

  3. Data sets
    • Detailed descriptions of research or industry datasets, including the methods used to collect the data and technical analyses supporting the quality of the measurements.
    • 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.
    • Articles evaluating the rigor and quality of the experiments used to generate the data and the completeness of the data description.
    • Tools that generate large-scale data while preserving their original characteristics.

  4. Workload characterization, quantitative measurement, design and evaluation studies of:
    • Computer and communication networks, protocols and algorithms
    • Wireless, mobile, ad-hoc and sensor networks, and IoT applications
    • Computer architectures, hardware accelerators, multi-core processors, memory systems and storage networks
    • High-performance computing
    • 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 and multimedia systems, web services
    • Cyber-physical systems, including the smart grid

  5. Methodologies, metrics, abstractions, algorithms and tools for:
    • Analytical modelling 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

  6. Measurement and evaluation:
    • Evaluation methodology 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 modelling (e.g., workloads, scaling behavior and 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 and sharing)
    • Reappraisals of previous empirical measurements and measurement-based conclusions
    • Descriptions of challenges and future directions that the measurement and evaluation community should pursue

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