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Pipelined Dynamic Scheduling of Big Data Streams
Content Provider | MDPI |
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Author | Souravlas, Stavros Anastasiadou, Sofia |
Copyright Year | 2020 |
Description | We are currently living in the big data era, in which it has become more necessary than ever to develop “smart” schedulers. It is common knowledge that the default Storm scheduler, as well as a large number of static schemes, has presented certain deficiencies. One of the most important of these deficiencies is the weakness in handling cases in which system changes occur. In such a scenario, some type of re-scheduling is necessary to keep the system working in the most efficient way. In this paper, we present a pipeline-based dynamic modular arithmetic-based scheduler (PMOD scheduler), which can be used to re-schedule the streams distributed among a set of nodes and their tasks, when the system parameters (number of tasks, executors or nodes) change. The PMOD scheduler organizes all the required operations in a pipeline scheme, thus reducing the overall processing time. |
Starting Page | 4796 |
e-ISSN | 20763417 |
DOI | 10.3390/app10144796 |
Journal | Applied Sciences |
Issue Number | 14 |
Volume Number | 10 |
Language | English |
Publisher | MDPI |
Publisher Date | 2020-07-13 |
Access Restriction | Open |
Subject Keyword | Applied Sciences Computation Theory and Mathematics Cloud Computing Big Data Task Re-scheduling Task Distribution Architecture Pipeline |
Content Type | Text |
Resource Type | Article |