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BenchCouncil Transactions on Benchmarks, Standards and Evaluations

BenchCouncil Transactions on Benchmarks, Standards, and Evaluations (TBench) publishes a range of papers, comments on previously published papers, and survey articles that deal with the benchmarks, standards, and evaluations research areas of current importance to our readers. Particular areas of interest include, but are not limited to:

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

  2. Open access 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.
    • Evaluating the rigour and quality of the experiments used to generate the data and the completeness of the description of the data.
    • Tools generating large-scale data while preserving their original characteristics.

  3. 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
    • 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, multimedia systems, Web services
    • Cyber-physical systems, including the smart grid

  4. 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, simulation

  5. Measurement and evaluation
    • Evaluation methodology and metric
    • 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 the measurement and evaluation community should pursue

View full aims and scope


Editors-in-Chief: Jianfeng Zhan, Tony Hey
View full editorial board


Imprint: KeAi
ISSN: 2772-4859
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