| Autor: |
Soudabeh Hedayati, Neda Maleki, Tobias Olsson, Fredrik Ahlgren, Mahdi Seyednezhad, Kamal Berahmand |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Journal of Cloud Computing: Advances, Systems and Applications, Vol 12, Iss 1, Pp 1-30 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2192-113X |
| DOI: |
10.1186/s13677-023-00520-9 |
| Popis: |
Abstract Hadoop is a framework for storing and processing huge volumes of data on clusters. It uses Hadoop Distributed File System (HDFS) for storing data and uses MapReduce to process that data. MapReduce is a parallel computing framework for processing large amounts of data on clusters. Scheduling is one of the most critical aspects of MapReduce. Scheduling in MapReduce is critical because it can have a significant impact on the performance and efficiency of the overall system. The goal of scheduling is to improve performance, minimize response times, and utilize resources efficiently. A systematic study of the existing scheduling algorithms is provided in this paper. Also, we provide a new classification of such schedulers and a review of each category. In addition, scheduling algorithms have been examined in terms of their main ideas, main objectives, advantages, and disadvantages. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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