Abstract

Cloud computing is an emerging computer technology, that provides distributed, scalable, elastic computer resources to the end-user over the Internet. One of the most challenging tasks in the cloud computing environment is task scheduling. The main objectives of the task scheduling are to identify the appropriate resources for scheduling a specific task on time, utilize the resources more efficiently, and reduce the total completion time of all input tasks to be executed. The task scheduling problem belongs to the class NP-hard. Since metaheuristic algorithms are proven to be efficient in the NP hard optimization, in this paper, we propose a task scheduling algorithm using metaheuristics approach. The proposed scheduler is based on the grey wolf optimizer nature-inspired algorithm. The experimental results prove the quality and robustness of the proposed method.

Keywords:
Computer science Distributed computing Cloud computing Scheduling (production processes) Scalability Fair-share scheduling Metaheuristic Dynamic priority scheduling Fixed-priority pre-emptive scheduling Two-level scheduling Round-robin scheduling Rate-monotonic scheduling Quality of service Artificial intelligence Mathematical optimization Operating system Computer network

Metrics

135
Cited By
26.57
FWCI (Field Weighted Citation Impact)
30
Refs
1.00
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Cloud Computing and Resource Management
Physical Sciences →  Computer Science →  Information Systems
IoT and Edge/Fog Computing
Physical Sciences →  Computer Science →  Computer Networks and Communications
Blockchain Technology Applications and Security
Physical Sciences →  Computer Science →  Information Systems
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