Memory constraint parallelised resource allocation and optimal scheduling using oppositional GWO for handling big data in cloud environment
| Autor: | Chetana Tukkoji, K. Seetharam |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Job shop scheduling
Computer science business.industry Computer Networks and Communications Distributed computing Big data Scheduling (production processes) Cloud computing Scheduling (computing) Task (project management) Computer Science Applications Computer Science (miscellaneous) Resource allocation business Cluster analysis Sequential algorithm Software |
| Zdroj: | International Journal of Cloud Computing. 9:432 |
| ISSN: | 2043-9997 2043-9989 |
| DOI: | 10.1504/ijcc.2020.112316 |
| Popis: | In cloud computing, task scheduling is one of the challenging troubles, especially when deadline and cost are conceived. On the other hand, the key issue of task scheduling is to reach optimal allocation of user's tasks in clouds. Besides, in terms of memory space and time complexities, the processing of a huge number of tasks with sequential algorithm results in greater computational cost. Therefore, we propose an efficient memory constraint parallelised resource allocation and optimal scheduling method based on oppositional GWO for resolving the scheduling problem of big data in the cloud environment in this paper. In parallel over distributed systems, the suggested scheduling approach applies the MapReduce framework to perform scheduling. The MapReduce framework is consisted of two main processes; particularly, the task prioritisation stage (with fuzzy C-means clustering method based on memory constraint) in map phase and optimal scheduling (using oppositional grey wolf optimisation algorithm) in reduce phase. The performance of proposed methodology is analysed in terms of makespan, cost and system utilisation. |
| Databáze: | OpenAIRE |
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