Call for Papers

Important Dates:

Full Papers: July 28, 2022 at 11:59 PM AoE

Notification: September 6, 2022 at 11:59 PM AoE

Final Papers Due: October 11, 2022 at 11:59 PM AoE

  • Paper Submission: The Full Version Of The Paper Should Be Submitted As A PDF File (15 pages in the LNCS format, not including references) Following The Submission Guidelines. The reviewing process is double-blind. Submission Site is https://bench2022.hotcrp.com/.
  • Publication: All accepted papers will be presented at the Bench 2022 conference, and will be published by Springer LNCS (Indexed by EI). Distinguished papers will be recommended to and published by the BenchCouncil Transactions on Benchmarks, Standards and Evaluation (TBench).
  • Awards: Regularly, the Bench conference will present the BenchCouncil Achievement Award ($3000), the BenchCouncil Rising Star Award ($1000), the BenchCouncil Best Paper Award ($1000), and the BenchCouncil Distinguished Doctoral Dissertation Award in Computer Architecture ($1000) and in other areas ($1000).

The Bench conference encompasses a wide range of topics in benchmarking, measurement, evaluation methods and tools. Bench’s multi-disciplinary emphasis provides an ideal environment for developers and researchers from the architecture, system, algorithm, and application communities to discuss practical and theoretical work covering workload characterization, benchmarks and tools, evaluation, measurement and optimization, and dataset generation.

We solicit papers describing original and previously unpublished work.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

Benchmark and standard specifications, implementations, and validations: Big Data, Artificial intelligence (AI), High performance computing (HPC), Machine learning, Warehouse-scale computing, Mobile robotics, Edge and fog computing, Internet of Things (IoT), Blockchain, Data management and storage, Financial, Education, Medical or other application domains.

Dataset Generation and Analysis: Research or industry data sets, 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; Evaluations of the rigor and quality of the experiments used to generate data and the completeness of the descriptions of the data; Tools generating large-scale data.

Workload characterization, quantitative measurement, design and evaluation studies: Characterization and evaluation 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 systems; 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.

Methodologies, abstractions, metrics, algorithms and tools: 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.