The Application of Fast Pruning Algorithm Under Map/ReduceTask Scheduling

2017 ◽  
Vol 4 (2) ◽  
pp. 1-8
Author(s):  
Pei Shu-jun ◽  
Keyword(s):  
Task Scheduling ◽  
Map Reduce ◽  
Pruning Algorithm

scholarly journals A Persuasive Resource-Aware Allocation Scheduler for Enhancing Task Scheduling

2019 ◽  
Vol 8 (6) ◽  
pp. 2808-2811
Keyword(s):  
Resource Allocation ◽  
Resource Utilization ◽  
Task Scheduling ◽  
Map Reduce ◽  
Total System ◽  
Master Node ◽  
Resource Aware

The deployment of Map Reduce has been built to grant enhancements to total system objectives such as job throughput. Hence, the support for user-specific objectives and resource allocation management has been least regarded and addressed. Schedulers enable users to assign jobs to queues that fulfil shared of specific resource. Existing work mainly focus on scheduling glitch occurring on the master’s side where the scheduler on the master node tries to allocate same work across all the worker nodes. The proposed scheduler focus on enhancing resource allocation when various kinds of workloads execute on the clusters. In order to evaluate the performance on the proposed scheduler which enhances resource utilization, an accomplishing time goal with each job is created.

The Map-Reduce Parallelism Framework for Task Scheduling in Grid Computing

2011 ◽  
Vol 216 ◽  
pp. 111-115 ◽  
Author(s):  
Yun Xia Pei ◽  
Yue Zhang
Keyword(s):  
Human Resources ◽  
Grid Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Optimization Procedure ◽  
Map Reduce ◽  
Research Issues ◽  
Job Management ◽  
Task Scheduling Algorithm ◽  
Simulation Results

As a rapid developing infrastructure, the grid can share widely distributed computing, storage, data and human resources. In order to improve the usability and QoS of the grid, the job management in the grid is very important, and becomes one of the key research issues in grid computing. Map-Reduce provide an efficient and easy-to-use framework for parallelizing the global optimization procedure. The simulation results show the usefulness and effectiveness of our task scheduling algorithm.

How does manipulation of secondary task scheduling affect human performance?

2006 ◽  
Author(s):  
Patrice D. Tremoulet ◽  
Kathleen M. Stibler ◽  
Patrick Craven ◽  
Joyce Barton ◽  
Adam Gifford ◽  
...  
Keyword(s):  
Task Scheduling ◽  
Secondary Task ◽  
Human Performance

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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