Auto-tuning of large-scale iterative operations on modern streaming platforms
2020 (English)In: CoNEXT 2020 - Proceedings of the 16th International Conference on Emerging Networking EXperiments and Technologies, Association for Computing Machinery (ACM), 2020, p. 554-555Conference paper, Published paper (Refereed)
Abstract [en]
As more analytical applications today require real-time processing over high volume data streams, finding an optimal implementation of traditional algorithms which possess iterative computations are gaining popularity and become crucial in most commercial contexts, particularly in edge processing and cloud applications. In this work, we propose an auto-tuning mechanism for enhancing the run-time performance of real-world iterative and cyclic stream processing applications (Multi-Join Operation as the study case) to correctly adjust the right performance bounds for workloads with different characteristics and data-sizes running on modern streaming data processing platform.
Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2020. p. 554-555
Keywords [en]
large scale datacenters, large-scale distributed processing platform, low-latency cyclic data-flow computational model, performance modeling of computer systems, real-time stream processing, Data streams, Analytical applications, Iterative computation, Iterative operation, Multi-join operations, Performance bounds, Realtime processing, Run-time performance, Streaming data processing, Iterative methods
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:kau:diva-83121DOI: 10.1145/3386367.3431680Scopus ID: 2-s2.0-85097611427ISBN: 9781450379489 (print)OAI: oai:DiVA.org:kau-83121DiVA, id: diva2:1530089
Conference
16th ACM Conference on Emerging Networking Experiment and Technologies, CoNEXT 2020, 1 December 2020 through 4 December 2020
2021-02-212021-02-212021-04-29Bibliographically approved