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

Stefan Halfpap

Name at Birth: Stefan Klauck

Research Assistant, PhD Student

Phone:+49 (331) 5509-1302
Email:stefan.halfpap(at)hpi.de
Address:August-Bebel-Str. 88, 14482 Potsdam
Room:V-2.02
Links:dblp, Google Scholar, github, LinkedIn

 

Focus Area: Autonomous Database Management

Research

Efficient Scale-Out Using Query-Driven Workload Distribution and Fragment Allocation

Database replication is an approach for scaling throughput and ensuring high availability. Using workload knowledge, we are able to load-balance queries to replica nodes according to the data being accessed. However, balancing the load evenly while maximizing data reuse is a challenging allocation problem. To address large-size problems, we developed a novel decomposition-based heuristic using linear programming (LP). By using LP, we are flexible to extend our approach for versatile allocation problems, e.g., considering changing workloads and robustness against node failures.

 

Member of Horizon 2020 project

Supervised Master's Theses

  • Cost-efficiency and Performance Robustness in Serverless Join Processing
  • Evaluation of Index Selection Algorithms
  • Network Request Handling in Database Systems
  • Logging and Recovery on In-Memory Databases
  • Partial Database Replication: Comparing Allocation Algorithms for Read-Only Scale-Out

 

Publications

  • Budget-Conscious Fine-Gra... - Download
    1.
    Richly, K., Schlosser, R., Boissier, M.: Budget-Conscious Fine-Grained Configuration Optimization for Spatio-Temporal Applications. Proceedings of the VLDB Endowment. pp. 4079–4092 (2022).
     
  • Robust and Budget-Constra... - Download
    2.
    Boissier, M.: Robust and Budget-Constrained Encoding Configurations for In-Memory Database Systems. Proceedings of the VLDB Endowment. pp. 780–793 (2022).
     
  • Memory-Efficient Database... - Download
    3.
    Halfpap, S., Schlosser, R.: Memory-Efficient Database Fragment Allocation for Robust Load Balancing when Nodes Fail. 37th IEEE International Conference on Data Engineering, ICDE 2021, Chania, Greece, April 19-22, 2021. pp. 1811–1816 (2021).
     
  • 4.
    Schlosser, R., Halfpap, S.: Robust and Memory-Efficient Database Fragment Allocation for Large and Uncertain Database Workloads. 24th International Conference on Extending Database Technology (EDBT 2021). pp. 367–372 (2021).
     
  • 5.
    Halfpap, S., Schlosser, R.: Exploration of Dynamic Query-Based Load Balancing for Partially Replicated Database Systems with Node Failures. CIKM ’20: The 29th ACM International Conference on Information and Knowledge Management. pp. 3409–3412 (2020).
     
  • Magic mirror in my hand, ... - Download
    6.
    Kossmann, J., Halfpap, S., Jankrift, M., Schlosser, R.: Magic mirror in my hand, which is the best in the land? An Experimental Evaluation of Index Selection Algorithms. Proceedings of the VLDB Endowment. pp. 2382–2395 (2020).
     
  • A Decomposition Approach ... - Download
    7.
    Schlosser, R., Halfpap, S.: A Decomposition Approach for Risk-Averse Index Selection. 32nd International Conference on Scientific and Statistical Database Management (SSDBM 2020). pp. 16:1–16:4 (2020).
     
  • 8.
    Halfpap, S.: Efficient Scale-Out Using Query-Driven Workload Distribution and Fragment Allocation. Proceedings of the VLDB 2019 PhD Workshop co-located with the 45th International Conference on Very Large Databases (VLDB 2019) (2019).
     
  • Workload-Driven Fragment ... - Download
    9.
    Halfpap, S., Schlosser, R.: Workload-Driven Fragment Allocation for Partially Replicated Databases Using Linear Programming. IEEE 35th International Conference on Data Engineering (ICDE 2019). pp. 1746–1749 (2019).
     
  • A Comparison of Allocatio... - Download
    10.
    Halfpap, S., Schlosser, R.: A Comparison of Allocation Algorithms for Partially Replicated Databases. IEEE 35th International Conference on Data Engineering (ICDE 2019). pp. 2008–2011 (2019).
     
  • Budget-Conscious Fine-Gra... - Download
    1.
    Richly, K., Schlosser, R., Boissier, M.: Budget-Conscious Fine-Grained Configuration Optimization for Spatio-Temporal Applications. Proceedings of the VLDB Endowment. pp. 4079–4092 (2022).
     
  • Robust and Budget-Constra... - Download
    2.
    Boissier, M.: Robust and Budget-Constrained Encoding Configurations for In-Memory Database Systems. Proceedings of the VLDB Endowment. pp. 780–793 (2022).
     
  • Evaluating Lightweight In... - Download
    3.
    Heinzl, L., Hurdelhey, B., Boissier, M., Perscheid, M., Plattner, H.: Evaluating Lightweight Integer Compression Algorithms in Column-Oriented In-Memory DBMS. 12th International Workshop on Accelerating Analytics and Data Management Systems Using Modern Processor and Storage Architectures, ADMS@VLDB 2021, Copenhagen, Denmark, August 16, 2021 (2021).
     
  • 4.
    Klauck, S., Plauth, M., Knebel, S., Strobl, M., Santry, D., Eggert, L.: Eliminating the Bandwidth Bottleneck of Central Query Dispatching Through TCP Connection Hand-Over. Datenbanksysteme für Business, Technologie und Web (BTW), 18. Fachtagung des GI-Fachbereichs "Datenbanken und Informationssysteme" (DBIS). pp. 97–106 (2019).
     
  • Hyrise Re-engineered: An ... - Download
    5.
    Dreseler, M., Kossmann, J., Boissier, M., Klauck, S., Uflacker, M., Plattner, H.: Hyrise Re-engineered: An Extensible Database System for Research in Relational In-Memory Data Management. 22nd International Conference on Extending Database Technology (EDBT). pp. 313–324 (2019).
     
  • Giving Customers Control ... - Download
    6.
    Hiller, J., Kimmerlin, M., Plauth, M., Heikkila, S., Klauck, S., Lindfors, V., Eberhardt, F., Bursztynowski, D., Santos, J.L., Hohlfeld, O., Wehrle, K.: Giving Customers Control over Their Data: Integrating a Policy Language into the Cloud. 2018 IEEE International Conference on Cloud Engineering (IC2E) (2018).
     
  • 7.
    Klauck, S.: Scalability, Availability, and Elasticity through Database Replication in Hyrise-R. Proceedings of the 4th HPI Cloud Symposium “Operating the Cloud” 2016. pp. 1–10 (2017).
     
  • 8.
    Lindemann, J., Klauck, S., Schwalb, D.: A Scalable Query Dispatcher for Hyrise-R. Proceedings of the 3rd HPI Cloud Symposium “Operating the Cloud” 2015. pp. 25–32 (2016).
     
  • Interactive, Flexible, an... - Download
    9.
    Klauck, S., Butzmann, L., Müller, S., Faust, M., Schwalb, D., Uflacker, M., Sinzig, W., Plattner, H.: Interactive, Flexible, and Generic What-If Analyses Using In-Memory Column Stores. Database Systems for Advanced Applications. pp. 488–497 (2015).
     
  • 10.
    Butzmann, L., Klauck, S., Mueller, S., Uflacker, M., Plattner, H., Sinzig, W.: Generic Business Simulation Using an In-Memory Column Store. Datenbanksysteme für Business, Technologie und Web (BTW), 16. Fachtagung des GI-Fachbereichs "Datenbanken und Informationssysteme" (DBIS). pp. 633–643 (2015).
     
  • 11.
    Plattner, H., Mueller, S., Nica, A., Butzmann, L., Klauck, S.: Using Object-Awareness to Optimize Join Processing in the SAP HANA Aggregate Cache. Proceedings of the 18th International Conference on Extending Database Technology (EDBT), Brussels, Belgium (2015).
     
  • 12.
    Schwalb, D., Kossmann, J., Faust, M., Klauck, S., Uflacker, M., Plattner, H.: Hyrise-R: Scale-out and Hot-Standby through Lazy Master Replication for Enterprise Applications. Proceedings of the 3rd VLDB Workshop on In-Memory Data Mangement and Analytics (IMDM), in conjunction with VLDB 2015 Kohala Coast, Hawaii (2015).
     
  • 13.
    Mueller, S., Butzmann, L., Klauck, S., Plattner, H.: An Adaptive Aggregate Maintenance Approach for Mixed Workloads in Columnar In-Memory Databases. Proceedings of the Thirty-Seventh Australasian Computer Science Conference (ACSC ’14) - Volume 147. pp. 3–12 (2014).
     
  • 14.
    Mueller, S., Butzmann, L., Klauck, S., Plattner, H.: Materialized View Maintenance Leveraging In-Memory Data Structures. International Journal On Advances in Software, vol. 7, no. 3&4. (2014).
     
  • 15.
    Mueller, S., Butzmann, L., Klauck, S., Plattner, H.: Workload-Aware Aggregate Maintenance in Columnar In-Memory Databases. IEEE International Conference on Big Data (IEEE Big Data 2013), Silicon Valley, USA (2013).
     
  • 16.
    Mueller, S., Butzmann, L., Höwelmeyer, K., Klauck, S., Plattner, H.: Efficient View Maintenance for Enterprise Applications in Columnar In-Memory Databases. 17th IEEE International Enterprise Distributed Object Computing Conference (EDOC), Vancouver, Canada (2013).
     
  • 17.
    Zeier, A., Plattner, H., Butzmann, L., Klauck, S., Tinnefeld, C., Mueller, S.: Available-To-Promise on an In-Memory Column Store. Datenbanksysteme in Business, Technologie und Web (BTW 2011), 14. Fachtagung des GI-Fachbereichs "Datenbanken und Informationssysteme" (DBIS), Proceedings, Kaiserslautern, Germany (2011).