$$\varvec{Q}ET$$ Q E T : a QoS-based energy-aware task scheduling method in cloud environment

2017 ◽  
Vol 20 (4) ◽  
pp. 3199-3212 ◽  
Author(s):  
Shengjun Xue ◽  
Yiyun Zhang ◽  
Xiaolong Xu ◽  
Guowen Xing ◽  
Haolong Xiang ◽  
...  
Keyword(s):  
Task Scheduling ◽  
Cloud Environment ◽  
Energy Aware ◽  
Scheduling Method

scholarly journals Optimization Algorithms based Task Scheduling Method for Cloud Computing Environment

2019 ◽  
Vol 8 (3) ◽  
pp. 3608-3613
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Large Scale ◽  
Data Centers ◽  
Computing Environment ◽  
Energy Aware ◽  
Cloud Computing Environment ◽  
Scheduling Method ◽  
Consistent Performance ◽  
Computing Structure

There are various enhancements in the world of technology. Among that Cloud computing delivers numerous amenities over the Internet. It employs data centers which comprise hardware and software provision for loading, servers, and systems. The primary reason for the popularity of Cloud computing is consistent performance, economical operation, prompt accessibility, rapid scaling and much more. The chief cause for concern in cloud computing are the errors that happen either in the software or the hardware and energy consumption on a large scale. The clients pay only for resources utilized by them and assets which are accessible during the computing in a cloud setting. In the environment of cloud computing, Task scheduling is significant concepts which can be used to minimize the energy and time spent. The algorithms in Task scheduling might employ various measures toward dispense preference to subtasks that may generate many schedules to the divergent computing structure. Moreover, consumption of energy could be dissimilar for every source which is allocated to a job. This present research explores that the PSO-CA based energy aware task scheduling method can predict with the aim to enhance the resource distribution.

A new energy-aware task scheduling method for data-intensive applications in the cloud

2016 ◽  
Vol 59 ◽  
pp. 14-27 ◽  
Author(s):  
Qing Zhao ◽  
Congcong Xiong ◽  
Ce Yu ◽  
Chuanlei Zhang ◽  
Xi Zhao
Keyword(s):  
Task Scheduling ◽  
Energy Aware ◽  
Data Intensive ◽  
New Energy ◽  
Scheduling Method ◽  
Data Intensive Applications

Task Scheduling Algorithm based on Resources Segregation in Cloud Environment

2018 ◽  
pp. 96-101
Author(s):  
Shailendra Raghuvanshi ◽  
Priyanka Dubey
Keyword(s):  
Load Balancing ◽  
Task Scheduling ◽  
Waiting Time ◽  
Virtual Machines ◽  
Scheduling Algorithm ◽  
Cloud Environment ◽  
Task Scheduling Algorithm ◽  
Machine Utilization ◽  
Independent Tasks ◽  
Optimal Machine

Load balancing of non-preemptive independent tasks on virtual machines (VMs) is an important aspect of task scheduling in clouds. Whenever certain VMs are overloaded and remaining VMs are under loaded with tasks for processing, the load has to be balanced to achieve optimal machine utilization. In this paper, we propose an algorithm named honey bee behavior inspired load balancing, which aims to achieve well balanced load across virtual machines for maximizing the throughput. The proposed algorithm also balances the priorities of tasks on the machines in such a way that the amount of waiting time of the tasks in the queue is minimal. We have compared the proposed algorithm with existing load balancing and scheduling algorithms. The experimental results show that the algorithm is effective when compared with existing algorithms. Our approach illustrates that there is a significant improvement in average execution time and reduction in waiting time of tasks on queue using workflowsim simulator in JAVA.

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