Collaborative Cloud Resource Management and Task Consolidation Using JAYA Variants Article Swipe
YOU?
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· 2024
· Open Access
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· DOI: https://doi.org/10.1109/tnsm.2024.3443285
In Cloud-based computing, job scheduling and load balancing are vital to ensure on-demand dynamic resource provisioning. However, reducing the scheduling parameters may affect datacenter performance due to the fluctuating on-demand requests. To deal with the aforementioned challenges, this research proposes a job scheduling algorithm, which is an improved version of a swarm intelligence algorithm. Two approaches, namely linear weight JAYA (LWJAYA) and chaotic JAYA (CJAYA), are implemented to improve the convergence speed for optimal results. Besides, a load-balancing technique is incorporated in line with job scheduling. Dynamically independent and non-pre-emptive jobs were considered for the simulations, which were simulated on two disparate test cases with homogeneous and heterogeneous VMs. The efficiency of the proposed technique was validated against a synthetic and real-world dataset from NASA, and evaluated against several top-of-the-line intelligent optimization techniques, based on the Holms test and Friedman test. Findings of the experiment show that the suggested approach performs better than the alternative approaches.
Related Topics
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/tnsm.2024.3443285
- OA Status
- hybrid
- Cited By
- 4
- References
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- OpenAlex ID
- https://openalex.org/W4401567688
Raw OpenAlex JSON
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https://openalex.org/W4401567688Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/tnsm.2024.3443285Digital Object Identifier
- Title
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Collaborative Cloud Resource Management and Task Consolidation Using JAYA VariantsWork title
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articleOpenAlex work type
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enPrimary language
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2024Year of publication
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2024-08-14Full publication date if available
- Authors
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Kaushik Mishra, Santosh Kumar Majhi, Kshira Sagar Sahoo, Sourav Kumar Bhoi, Monowar Bhuyan, Amir H. GandomiList of authors in order
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https://doi.org/10.1109/tnsm.2024.3443285Publisher landing page
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://doi.org/10.1109/tnsm.2024.3443285Direct OA link when available
- Concepts
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Computer science, Cloud computing, Resource management (computing), Consolidation (business), Task (project management), Task management, Distributed computing, Operating system, Business, Systems engineering, Engineering, AccountingTop concepts (fields/topics) attached by OpenAlex
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4Total citation count in OpenAlex
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2025: 2, 2024: 2Per-year citation counts (last 5 years)
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38Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2792637407, https://openalex.org/W2275530856, https://openalex.org/W4379467930, https://openalex.org/W3009846452, https://openalex.org/W871453037, https://openalex.org/W2157016369, https://openalex.org/W2501976760, https://openalex.org/W644481803, https://openalex.org/W2010203032, https://openalex.org/W2416610140, https://openalex.org/W2097025565, https://openalex.org/W1120107613, https://openalex.org/W2057392949, https://openalex.org/W1982900913, https://openalex.org/W2810488531, https://openalex.org/W2775104899, https://openalex.org/W2087656234, https://openalex.org/W2101651025, https://openalex.org/W1984216891, https://openalex.org/W2567175581, https://openalex.org/W2898751681, https://openalex.org/W4388485466, https://openalex.org/W4388283675, https://openalex.org/W3023710040, https://openalex.org/W1698059316, https://openalex.org/W2899404179, https://openalex.org/W2804396946, https://openalex.org/W2523702618, https://openalex.org/W4239337465, https://openalex.org/W3037590577, https://openalex.org/W3122326797, https://openalex.org/W2741500440, https://openalex.org/W3112008338, https://openalex.org/W1988696535, https://openalex.org/W3133983943, https://openalex.org/W2045287414, https://openalex.org/W1603445711, https://openalex.org/W2997546679 |
| referenced_works_count | 38 |
| abstract_inverted_index.a | 40, 50, 76, 118 |
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| abstract_inverted_index.an | 46 |
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| abstract_inverted_index.weight | 58 |
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| abstract_inverted_index.dataset | 122 |
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| abstract_inverted_index.improve | 68 |
| abstract_inverted_index.optimal | 73 |
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| abstract_inverted_index.version | 48 |
| abstract_inverted_index.(CJAYA), | 64 |
| abstract_inverted_index.(LWJAYA) | 60 |
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| abstract_inverted_index.Findings | 141 |
| abstract_inverted_index.Friedman | 139 |
| abstract_inverted_index.However, | 16 |
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| abstract_inverted_index.evaluated | 126 |
| abstract_inverted_index.on-demand | 12, 29 |
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| abstract_inverted_index.suggested | 148 |
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| abstract_inverted_index.approaches. | 155 |
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| abstract_inverted_index.simulations, | 95 |
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| institutions_distinct_count | 6 |
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| citation_normalized_percentile.is_in_top_10_percent | True |