Publications of Boris Koldehofe
- Network Testing Utilizing ProgrammableNetworking Hardware. ( ), In IEEE Communications Magazine, IEEE, 2022.
- Enhancing Flexibility for Dynamic Time-Sensitive Network Configurations ( ), In Proceedings of the 3rd KuVS Fachgespräch on Network Softwarization, Universität Tübingen, 2022.
- On the Incremental Reconfiguration of Time-sensitive Networks at Runtime ( ), In Proceedings of the IFIP Networking Conference., IFIP, 2022.
- Travel light: state shedding for efficient operator migration ( ), In Proceedings of the 16th ACM International Conference on Distributed and Event-Based Systems (DEBS'22), ACM press, 2022.
- FA2: Fast, Accurate Autoscaling for Serving Deep Learning Inference with SLA Guarantees ( ), In Proceedings of the 28th IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS 2022), IEEE, 2022.
- PANDA: performance prediction for parallel and dynamic stream processing ( ), In Proceedings of the 16th ACM International Conference on Distributed and Event-Based Systems, ACM press, 2022.
- Window-based Parallel Operator Execution with In-Network Computing: Proceedings ( ), In Proceedings of the 16th ACM International Conference on Distributed and Event-based Systems (DEBS '22), ACM New York, NY, USA, 2022.
Data parallel processing is a key concept to increase the scalability and elasticity in event streaming systems. Often data parallelism is accomplished in a splitter-merger architecture where the splitter divides incoming streams into partitions and forwards them to parallel operator instances. The splitter performance is a limiting factor to the system throughput and the parallelization degree.This work studies how to leverage novel methods of in-network computing to accelerate the splitter functionality by implementing it as an in-network function. While dedicated hardware for in-network computing has a high potential to enhance the splitter performance, in-network programming models like the P4 language are also highly limited in their expressiveness to support corresponding parallelization models. We propose P4SS which supports overlapping and non-overlapping count-based windows for multiple independent data streams and parallelizes them to a dynamically configurable number of operator instances. We validate in the context of a prototypical implementation our splitting strategy and its scalability in terms of switch resource consumption.
Keywords: Data Parallelism, In-network Computing, Load Balancing, Complex Event Processing (CEP), P4 Language, Data Plane Programming
- TCEP: Transitions in Operator Placement to Adapt to Dynamic Network Environments. ( ), In In Journal of Computer and Systems Sciences (JCSS), Special Issue on Algorithmic Theory of Dynamic Networks and its Applications., Elsevier, volume 122, 2021.
- OpenBNG: Central office network functions on programmable data plane hardware ( ), In International Journal of Network Management, Wiley, volume 31, 2021.
- Leveraging Flexibility of Time-Sensitive Networks for dynamic Reconfigurability ( ), In Proceedings of IFIP Networking 2021, IFIP, 2021.
- Leveraging PIFO Queues for Scheduling in Time-Sensitive Networks ( ), In In the Proceedings of the IEEE International Symposium on Local and Metropolitan Area Networks (LANMAN 2021)., IEEE, 2021.
- P4-CoDel: Experiences on Programmable Data Plane Hardware ( ), In Proceedings of the IEEE International Conference on Communications (ICC 2021): Next-Generation Networking and Internet Symposium, IEEE, 2021.
- Towards QoE-Driven Optimization of Multi-Dimensional Content Streaming ( ), In Proceedings of the Conference on Networked Systems 2021 (NetSys 2021), European Association of Software Science and Technology, 2021.
- OpenBNG: Central office network functions on programmable data plane hardware ( ), In International Journal of Network Management, Wiley, 2020.
- Grußwort der Gastherausgeber zum Thema Fog Computing ( ), In Informatik Spektrum, Springer Science and Business Media LLC, volume 42, 2020.
- Operator as a Service: Stateful Serverless Complex Event Processing ( ), In Proceedings of the 2020 IEEE International Conference on Big Data, IEEE, 2020.
- Microbursts in Software and Hardware-based Traffic Load Generation ( ), In Proceedings of the IEEE/IFIP Network Operations and Management Symposium (NOMS), IEEE, 2020.
- Flexible Content-based Publish/Subscribe over Programmable Data Planes ( ), In Proceedings of the IEEE/IFIP Network Operations and Management Symposium (NOMS), IEEE, 2020.
- P4STA: High Performance Packet Timestamping with Programmable Packet Processors ( ), In Proceedings of the IEEE/IFIP Network Operations and Management Symposium (NOMS), IEEE, 2020.