Elite learning Harris hawks optimizer for multi-objective task scheduling in cloud computing

2021 ◽  
Author(s):  
Dina A. Amer ◽  
Gamal Attiya ◽  
Ibrahim Zeidan ◽  
Aida A. Nasr
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Multi Objective

scholarly journals An EDA-GA Hybrid Algorithm for Multi-Objective Task Scheduling in Cloud Computing

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 146379-146389 ◽  
Author(s):  
Shanchen Pang ◽  
Wenhao Li ◽  
Hua He ◽  
Zhiguang Shan ◽  
Xun Wang
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Hybrid Algorithm ◽  
Multi Objective

scholarly journals Multi-objective Prediction based Task Scheduling Method in Cloud Computing

2019 ◽  
Vol 8 (4) ◽  
pp. 9388-9394 ◽  
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Task Scheduling ◽  
Job Scheduling ◽  
Sigmoid Function ◽  
Service Model ◽  
Multi Objective ◽  
Computing Paradigm ◽  
Optimal Node ◽  
Scheduling Method

Cloud Computing is Internet based computing where one can store and access their personal resources from any computer through Internet. Cloud Computing is a simple pay-per-utilize consumer-provider service model. Cloud is nothing but large pool of easily accessible and usable virtual resources. Task (Job) scheduling is always a noteworthy issue in any computing paradigm. Due to the availability of finite resources and time variant nature of incoming tasks it is very challenging to schedule a new task accurately and assign requested resources to cloud user. Traditional task scheduling techniques are improper for cloud computing as cloud computing is based on virtualization technology with disseminated nature. Cloud computing brings in new challenges for task scheduling due to heterogeneity in hardware capabilities, on-demand service model, pay-per-utilize model and guarantee to meet Quality of Service (QoS). This has motivated us to generate multi-objective methods for task scheduling. In this research paper we have presented multi-objective prediction based task scheduling method in cloud computing to improve load balancing in order to satisfy cloud consumers dynamically changing needs and also to benefit cloud providers for effective resource management. Basically our method gives low probability value for not capable and overloaded nodes. To achieve the same we have used sigmoid function and Euclidean distance. Our major goal is to predict optimal node for task scheduling which satisfies objectives like resource utilization and load balancing with accuracy.

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