Running Many-Task Applications Across Multiple Resources with Everest Platform

2020 ◽  
pp. 634-646
Author(s):  
Oleg Sukhoroslov ◽  
Vladimir Voloshinov ◽  
Sergey Smirnov
Keyword(s):  
Multiple Resources

An empirical demonstration of multiple resources

1984 ◽  
Author(s):  
William L. Derrick ◽  
Thomas M. McCloy
Keyword(s):  
Multiple Resources ◽  
Empirical Demonstration

Application of the multiple resources questionnaire (MRQ) to a complex gaming environment

2004 ◽  
Author(s):  
David B. Boles ◽  
Jeffrey B. Phillips ◽  
Jason R. Perdelwitz ◽  
Jonathan H. Bursk
Keyword(s):  
Multiple Resources ◽  
Gaming Environment

scholarly journals Multiple Resources Planning in Microgrid Based on Modeling Demand Response of User Side

2021 ◽  
Vol 634 (1) ◽  
pp. 012048
Author(s):  
Si-cong Wang ◽  
Qi-xin Wang ◽  
Zi-xia Sang ◽  
Ji-feng He ◽  
Jia-qi Huang ◽  
...  
Keyword(s):  
Demand Response ◽  
Multiple Resources ◽  
Resources Planning

scholarly journals IoT Resource Allocation and Optimization Based on Heuristic Algorithm

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 539 ◽  
Author(s):  
Arun Kumar Sangaiah ◽  
Ali Asghar Rahmani Hosseinabadi ◽  
Morteza Babazadeh Shareh ◽  
Seyed Yaser Bozorgi Rad ◽  
Atekeh Zolfagharian ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Distributed System ◽  
Communication Cost ◽  
The Internet ◽  
Multiple Resources ◽  
Whale Optimization ◽  
Total Communication ◽  
The Internet Of Things ◽  
Proper Performance ◽  
Better Than

The Internet of Things (IoT) is a distributed system that connects everything via internet. IoT infrastructure contains multiple resources and gateways. In such a system, the problem of optimizing IoT resource allocation and scheduling (IRAS) is vital, because resource allocation (RA) and scheduling deals with the mapping between recourses and gateways and is also responsible for optimally allocating resources to available gateways. In the IoT environment, a gateway may face hundreds of resources to connect. Therefore, manual resource allocation and scheduling is not possible. In this paper, the whale optimization algorithm (WOA) is used to solve the RA problem in IoT with the aim of optimal RA and reducing the total communication cost between resources and gateways. The proposed algorithm has been compared to the other existing algorithms. Results indicate the proper performance of the proposed algorithm. Based on various benchmarks, the proposed method, in terms of “total communication cost”, is better than other ones.

Human Performance Modeling of Reduced Manning Concepts for Navy Ships

1996 ◽  
Vol 40 (19) ◽  
pp. 987-991 ◽  
Author(s):  
Rick D. Archer ◽  
G.W. Lewis ◽  
John Lockett
Keyword(s):  
System Integration ◽  
Human Performance ◽  
Performance Modeling ◽  
Assessment Tool ◽  
Simulation Models ◽  
Hierarchical Decomposition ◽  
Army Research Laboratory ◽  
Multiple Resources ◽  
Bridge Models ◽  
And Task

WinCrew is a human performance assessment tool developed by the Army Research Laboratory, Human Research and Engineering Directorate that implements the Wickens' Theory of Multiple Resources. WinCrew supports the hierarchical decomposition of missions into functions and tasks. As a demonstration of the capability of WinCrew, simulation models of the activities performed by bridge personnel on a Navy Guided Missile Destroyer DDG51 were developed. The scenarios were chosen to illustrate the potential of task network based human performance modeling to address reduced manning issues for naval ship operations. In order to fully exercise the functionality of WinCrew, four bridge models were developed. One scenario was modeled for four different manning, automation, and task allocation configurations. The scenario modeled was entry into San Diego Harbor. The major events for the bridge team were: bearing fixes, gyro error checks, turns, precision anchoring, and obstacle avoidance. Results of the effort demonstrated the utility of workload modeling for assessing human system integration alternatives for shipboard manning.

The Effect of Processing Code, Response Modality and Task Difficulty on Dual Task Performance and Subjective Workload in a Manual System

1987 ◽  
Vol 31 (7) ◽  
pp. 847-851 ◽  
Author(s):  
Yili Liu ◽  
Christopher D. Wickens
Keyword(s):  
Task Performance ◽  
Dual Task ◽  
Task Difficulty ◽  
Task Structure ◽  
Time Sharing ◽  
Multiple Resources ◽  
Response Modality ◽  
Dual Task Performance ◽  
Processing Resources ◽  
And Task

We report here the first experiment of a series studying the effect of task structure and difficulty demand on time-sharing performance and workload in both automated and corresponding manual systems. The experimental task involves manual control time-shared with spatial and verbal decisions tasks of two levels of difficulty and two modes of response (voice or manual). The results provide strong evidence that tasks and processes competing for common processing resources are time shared less effectively and have higher workload than tasks competing for separate resources. Subjective measures and the structure of multiple resources are used in conjunction to predict dual task performance. The evidence comes from both single task and from dual task performance.

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