Hasso-Plattner-Institut
Prof. Dr. h.c. Hasso Plattner
  
 

Guenter Hesse

Research Assistant, PhD Candidate

Phone+49 (331) 5509 - 1381
Fax+49 (331) 97992 - 579
E-Mailguenter.hesse(at)hpi.de 
Room     V-2.02 (Campus II)

Profiles

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Research

Benchmarking Enterprise Data Stream Processing Architectures

Stream processing systems have gained popularity due to developments such as Internet of Things or Industry 4.0. Enterprises benefit from this technology by augmenting their business data with up-to-date streaming information in a stream processing architecture. The performance characteristics of such an architecture, e.g., latency or throughput, vary between data stream processing systems, system configurations, hardware setups, or further environmental differences. A common way of analyzing and evaluating a system's performance is benchmarking. Despite the increasing variety of stream processing frameworks, there is a lack of a satisfying benchmark. This is especially true for the enterprise context, i.e., when combining business or historical data with streaming data. My research investigates how a performance benchmark for streaming architectures should be designed and implemented.

Particularly, it presents a novel benchmark for enterprise streaming architectures. This includes the design of a benchmark process that ensures objective comparisons. As part of the benchmark, we developed a comprehensive toolkit with a focus on usability, which supports the benchmark execution and calculates benchmark results independently of the used stream processing system. The process design also comprises the definition of real-world benchmark queries that are validated in industry and which need to be implemented on the system under test. These queries cover the core functionalities of stream processing systems that are defined in literature. To validate the proposed benchmark, we will analyze selected systems, Apache Flink and Apache Spark Streaming being two of them.

Keywords: Data Stream Processing, Performance Benchmarking, Industry 4.0, and Internet of Things

Program Committee Membership

Publications

  • ESPBench: The Enterprise ... - Download
    1.
    Hesse, G., Matthies, C., Perscheid, M., Uflacker, M., Plattner, H.: ESPBench: The Enterprise Stream Processing Benchmark. ACM/SPEC Internation Conference on Performance Engineering (ICPE). pp. 201–212 (2021).
     
  • How Fast Can We Insert? A... - Download
    2.
    Hesse, G., Matthies, C., Uflacker, M.: How Fast Can We Insert? An Empirical Performance Evaluation of Apache Kafka. IEEE International Conference on Parallel and Distributed Systems (ICPADS). pp. 641–648 (2021).
     
  • Mining for Process Improv... - Download
    3.
    Matthies, C., Dobrigkeit, F., Hesse, G.: Mining for Process Improvements: Analyzing Software Repositories in Agile Retrospectives. IEEE/ACM International Conference on Software Engineering Workshops (ICSEW). pp. 189–190. ACM, New York, NY, USA (2020).
     
  • Towards Using Data to Inf... - Download
    4.
    Matthies, C., Hesse, G.: Towards Using Data to Inform Decisions in Agile Software Development: Views of Available Data. International Conference on Software Technologies (ICSOFT). pp. 552–559. SciTePress (2019).
     
  • Application of Data Strea... - Download
    5.
    Hesse, G., Sinzig, W., Matthies, C., Uflacker, M.: Application of Data Stream Processing Technologies in Industry 4.0: What is Missing?. International Conference on Data Science, Technology and Applications (DATA). pp. 304–310. SciTePress (2019).
     
  • Quantitative Impact Evalu... - Download
    6.
    Hesse, G., Matthies, C., Glass, K., Huegle, J., Uflacker, M.: Quantitative Impact Evaluation of an Abstraction Layer for Data Stream Processing Systems. IEEE International Conference on Distributed Computing Systems (ICDCS). pp. 1381–1392 (2019).
     
  • Adding Value by Combining... - Download
    7.
    Hesse, G., Matthies, C., Sinzig, W., Uflacker, M.: Adding Value by Combining Business and Sensor Data: An Industry 4.0 Use Case. In: Li, G., Yang, J., Gama, J., Natwichai, J., and Tong, Y. (eds.) International Conference on Database Systems for Advanced Applications (DASFAA). pp. 528–532. Springer International Publishing (2019).
     
  • An Additional Set of (Aut... - Download
    8.
    Matthies, C., Dobrigkeit, F., Hesse, G.: An Additional Set of (Automated) Eyes: Chatbots for Agile Retrospectives. IEEE/ACM International Workshop on Bots in Software Engineering (BotSE). pp. 34–37. IEEE (2019).
     
  • Beyond Surveys: Analyzing... - Download
    9.
    Matthies, C., Teusner, R., Hesse, G.: Beyond Surveys: Analyzing Software Development Artifacts to Assess Teaching Efforts. IEEE Frontiers in Education Conference (FIE). pp. 1–9 (2018).
     
  • Senska - Towards an Enter... - Download
    10.
    Hesse, G., Reissaus, B., Matthies, C., Lorenz, M., Kraus, M., Uflacker, M.: Senska - Towards an Enterprise Streaming Benchmark. TPC Technology Conference (TPCTC). pp. 25–40. , Cham (2018).
     
  • 11.
    Lorenz, M., Rudolph, J.-P., Hesse, G., Uflacker, M., Plattner, H.: Object-Relational Mapping Revisited - A Quantitative Study on the Impact of Database Technology on O/R Mapping Strategies. Hawaii International Conference on System Sciences (HICSS). pp. 4877–4886 (2017).
     
  • A New Application Benchma... - Download
    12.
    Hesse, G., Matthies, C., Reissaus, B., Uflacker, M.: A New Application Benchmark for Data Stream Processing Architectures in an Enterprise Context: Doctoral Symposium. ACM International Conference on Distributed and Event-based Systems (DEBS). pp. 359–362 (2017).
     
  • 13.
    Lorenz, M., Hesse, G., Rudolph, J.-P.: Object-relational Mapping Revised - Guideline Review and Consolidation. International Joint Conference on Software Technologies (ICSOFT). pp. 157–168 (2016).
     
  • Conceptual Survey on Data... - Download
    14.
    Hesse, G., Lorenz, M.: Conceptual Survey on Data Stream Processing Systems. IEEE International Conference on Parallel and Distributed Systems (ICPADS). 797–802 (2015).
     

Supervised Master's Theses

Design of a Benchmark Concept for Data Stream Management Systems in the Context of Smart Factories, Benjamin Reißaus, 2017