Academic Journal
An enhanced ordinal optimization with lower scheduling overhead based novel approach for task scheduling in cloud computing environment
| العنوان: | An enhanced ordinal optimization with lower scheduling overhead based novel approach for task scheduling in cloud computing environment |
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| المؤلفون: | Monika Yadav, Atul Mishra |
| المصدر: | Journal of Cloud Computing: Advances, Systems and Applications, Vol 12, Iss 1, Pp 1-14 (2023) |
| بيانات النشر: | SpringerOpen, 2023. |
| سنة النشر: | 2023 |
| المجموعة: | LCC:Computer engineering. Computer hardware LCC:Electronic computers. Computer science |
| مصطلحات موضوعية: | Cloud computing, Ordinal optimization, Makespan, CloudSim, Schedules, Computer engineering. Computer hardware, TK7885-7895, Electronic computers. Computer science, QA75.5-76.95 |
| الوصف: | Abstract Efficient utilization of available computing resources in Cloud computing is one of the most challenging problems for cloud providers. This requires the design of an efficient and optimal task-scheduling strategy that can play a vital role in the functioning and overall performance of the cloud computing system. Optimal Schedules are specifically needed for scheduling virtual machines in fluctuating & unpredictable dynamic cloud scenario. Although there exist numerous approaches for enhancing task scheduling in the cloud environment, it is still an open issue. The paper focuses on an improved & enhanced ordinal optimization technique to reduce the large search space for optimal scheduling in the minimum time to achieve the goal of minimum makespan. To meet the current requirement of optimal schedule for minimum makespan, ordinal optimization that uses horse race conditions for selection rules is applied in an enhanced reiterative manner to achieve low overhead by smartly allocating the load to the most promising schedule. This proposed ordinal optimization technique and linear regression generate optimal schedules that help achieve minimum makespan. Furthermore, the proposed mathematical equation, derived using linear regression, predicts any future dynamic workload for a minimum makespan period target. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2192-113X |
| Relation: | https://doaj.org/toc/2192-113X |
| DOI: | 10.1186/s13677-023-00392-z |
| URL الوصول: | https://doaj.org/article/eccd9406cf9a44e6b5c87c3c597d3e27 |
| رقم الانضمام: | edsdoj.9406cf9a44e6b5c87c3c597d3e27 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 2192113X |
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| DOI: | 10.1186/s13677-023-00392-z |