More on Pipelined Dynamic Scheduling of Big Data Streams
An important as well as challenging task in modern applications is the management and processing with very short delays of large data volumes. It is quite often, that the capabilities of individual machines are exceeded when trying to manage such large data volumes. In this regard, it is important t...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-12-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/11/1/61 |
| _version_ | 1797543730005147648 |
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| author | Stavros Souravlas Sofia Anastasiadou Stefanos Katsavounis |
| author_facet | Stavros Souravlas Sofia Anastasiadou Stefanos Katsavounis |
| author_sort | Stavros Souravlas |
| collection | DOAJ |
| description | An important as well as challenging task in modern applications is the management and processing with very short delays of large data volumes. It is quite often, that the capabilities of individual machines are exceeded when trying to manage such large data volumes. In this regard, it is important to develop efficient task scheduling algorithms, which reduce the stream processing costs. What makes the situation more difficult is the fact that the applications as well as the processing systems are prone to changes during runtime: processing nodes may be down, temporarily or permanently, more resources may be needed by an application, and so on. Therefore, it is necessary to develop dynamic schedulers, which can effectively deal with these changes during runtime. In this work, we provide a fast and fair task migration policy while maintaining load balancing and low latency times. The experimental results have shown that our scheme offers better load balancing and reduces the overall latency compared to the state of the art strategies, due to the stepwise communication and the pipeline based processing it employs. |
| first_indexed | 2024-03-10T13:49:49Z |
| format | Article |
| id | doaj.art-d9613d4e3a6b42f6abd0debc5dfab50e |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T13:49:49Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-d9613d4e3a6b42f6abd0debc5dfab50e2023-11-21T02:16:42ZengMDPI AGApplied Sciences2076-34172020-12-011116110.3390/app11010061More on Pipelined Dynamic Scheduling of Big Data StreamsStavros Souravlas0Sofia Anastasiadou1Stefanos Katsavounis2Department of Applied Informatics, University of Macedonia, 54616 Thessaloniki, GreeceDepartment of Early Childhood Education, Faculty of Education, University of Western Macedonia, 21, 53100 Florina, GreeceDepartment of Production and Management Engineering, Democritus University of Thrace, 67150 Xanthi, GreeceAn important as well as challenging task in modern applications is the management and processing with very short delays of large data volumes. It is quite often, that the capabilities of individual machines are exceeded when trying to manage such large data volumes. In this regard, it is important to develop efficient task scheduling algorithms, which reduce the stream processing costs. What makes the situation more difficult is the fact that the applications as well as the processing systems are prone to changes during runtime: processing nodes may be down, temporarily or permanently, more resources may be needed by an application, and so on. Therefore, it is necessary to develop dynamic schedulers, which can effectively deal with these changes during runtime. In this work, we provide a fast and fair task migration policy while maintaining load balancing and low latency times. The experimental results have shown that our scheme offers better load balancing and reduces the overall latency compared to the state of the art strategies, due to the stepwise communication and the pipeline based processing it employs.https://www.mdpi.com/2076-3417/11/1/61dynamic schedulingbig datastream processingtask migrations |
| spellingShingle | Stavros Souravlas Sofia Anastasiadou Stefanos Katsavounis More on Pipelined Dynamic Scheduling of Big Data Streams Applied Sciences dynamic scheduling big data stream processing task migrations |
| title | More on Pipelined Dynamic Scheduling of Big Data Streams |
| title_full | More on Pipelined Dynamic Scheduling of Big Data Streams |
| title_fullStr | More on Pipelined Dynamic Scheduling of Big Data Streams |
| title_full_unstemmed | More on Pipelined Dynamic Scheduling of Big Data Streams |
| title_short | More on Pipelined Dynamic Scheduling of Big Data Streams |
| title_sort | more on pipelined dynamic scheduling of big data streams |
| topic | dynamic scheduling big data stream processing task migrations |
| url | https://www.mdpi.com/2076-3417/11/1/61 |
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