How to Sign Paper Contracts? Conjectures and Evidence Related to Equitable and Efficient Collaborative Task Scheduling

2014 ◽  
pp. 317-340
Author(s):  
Eric Brier ◽  
David Naccache ◽  
Li-yao Xia
Keyword(s):  
Task Scheduling ◽  
Collaborative Task

scholarly journals In-Space Crew-Collaborative Task Scheduling

2006 ◽  
Author(s):  
John Jaap ◽  
Patrick Meyer ◽  
Elizabeth Davis ◽  
Lea Richardson
Keyword(s):  
Task Scheduling ◽  
Collaborative Task

scholarly journals Collaborative Task Scheduling for IoT-Assisted Edge Computing

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 216593-216606
Author(s):  
Youngjin Kim ◽  
Chiwon Song ◽  
Hyuck Han ◽  
Hyungsoo Jung ◽  
Sooyong Kang
Keyword(s):  
Task Scheduling ◽  
Edge Computing ◽  
Collaborative Task

Collaborative task scheduling with new task arrival in cloud manufacturing using improved multi-population biogeography-based optimization

2021 ◽  
pp. 1-24
Author(s):  
Ziwei Dai ◽  
Zhiyong Zhang ◽  
Mingzhou Chen
Keyword(s):  
Task Scheduling ◽  
Optimal Schedule ◽  
Cloud Manufacturing ◽  
Wilcoxon Test ◽  
Superior Performance ◽  
Supply Chain Collaboration ◽  
Collaborative Task ◽  
Dynamic Task ◽  
Comparison Algorithms ◽  
Better Than

Task scheduling is important in cloud manufacturing because of customers’ increasingly individualized demands. However, when various changes occur, a previous optimal schedule may become non-optimal or even infeasible owing to the uncertainty of the real manufacturing environment where dynamic task arrival over time is a vital source. In this paper, we propose a novel collaborative task scheduling (CTS) model dealing with new task arrival which considers multi-supply chain collaboration. We present an improved multi-population biogeography-based optimization (IMPBBO) algorithm that uses a matrix-based solution representation and integrates the multi-population strategy, local search for the best solution, and the collaboration mechanism, for determining the optimal schedule. A series of experiments are conducted for verifying the effectiveness of the IMPBBO algorithm for solving the CTS model by comparing it with five other algorithms. The experimental results concerning average best values obtained by the IMPBBO algorithm are better than that obtained by comparison algorithms for 41 out of 45 cases, showing its superior performance. Wilcoxon-test has been employed to strengthen the fact that IMPBBO algorithm performs better than five comparison algorithms.

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.

Fast Pruning Algorithm and Task Scheduling under Map/Reduce

2020 ◽  
Vol 16 (10) ◽  
pp. 1627
Author(s):  
Pei Shujun ◽  
Zhang Yu ◽  
Liang Chao
Keyword(s):  
Task Scheduling ◽  
Map Reduce ◽  
Pruning Algorithm ◽  
And Task
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