Energy and task completion time trade-off for task offloading in fog-enabled IoT networks

2021 ◽  
pp. 101395
Author(s):  
Om-Kolsoom Shahryari ◽  
Hossein Pedram ◽  
Vahid Khajehvand ◽  
Mehdi Dehghan TakhtFooladi
Keyword(s):  
Completion Time ◽  
Task Completion ◽  
Trade Off ◽  
Task Completion Time ◽  
And Task ◽  
Task Offloading

scholarly journals A Correlated Model for Evaluating Performance and Energy of Cloud System Given System Reliability

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Hongli Zhang ◽  
Panpan Li ◽  
Zhigang Zhou
Keyword(s):  
Energy Consumption ◽  
Energy Saving ◽  
Completion Time ◽  
High Performance ◽  
Virtual Machines ◽  
Memory Storage ◽  
Task Completion ◽  
Task Completion Time ◽  
Hardware Failure ◽  
And Task

The serious issue of energy consumption for high performance computing systems has attracted much attention. Performance and energy-saving have become important measures of a computing system. In the cloud computing environment, the systems usually allocate various resources (such as CPU, Memory, Storage, etc.) on multiple virtual machines (VMs) for executing tasks. Therefore, the problem of resource allocation for running VMs should have significant influence on both system performance and energy consumption. For different processor utilizations assigned to the VM, there exists the tradeoff between energy consumption and task completion time when a given task is executed by the VMs. Moreover, the hardware failure, software failure and restoration characteristics also have obvious influences on overall performance and energy. In this paper, a correlated model is built to analyze both performance and energy in the VM execution environment given the reliability restriction, and an optimization model is presented to derive the most effective solution of processor utilization for the VM. Then, the tradeoff between energy-saving and task completion time is studied and balanced when the VMs execute given tasks. Numerical examples are illustrated to build the performance-energy correlated model and evaluate the expected values of task completion time and consumed energy.

Effects of Brake Pedal Horizontal Location of Hospital Beds on Force Exertions and Work Efficiency

2017 ◽  
Vol 61 (1) ◽  
pp. 1005-1009
Author(s):  
Jie Zhou ◽  
Neal Wiggermann
Keyword(s):  
Maximal Voluntary Contraction ◽  
Completion Time ◽  
Voluntary Contraction ◽  
Task Completion ◽  
Brake Pedal ◽  
Work Efficiency ◽  
Task Completion Time ◽  
Hospital Beds ◽  
Hospital Bed ◽  
And Task

The brake pedal on hospital beds is critical during bed maneuvering, however, substantial force and awkward postures are usually required during pedal engagement tasks. Nine professional caregivers were recruited to investigate how brake pedal horizontal location affected maximal voluntary contraction (MVC) force, acceptable force to engage the pedal (AFE), force efficiency and task completion time. The results demonstrated reduced MVC, AFE and force efficiency whereas increased task completion time with greater pedal depths. Pedal depth was significantly correlated with MVC, force efficiency and task completion time and these correlations are moderate (0.25≤r<0.50) or good (0.50≤r<075). These findings provide important information for hospital bed design.

Age-Related Decrements in Automobile Instrument Panel Task Performance

1989 ◽  
Vol 33 (3) ◽  
pp. 159-163 ◽  
Author(s):  
Brian C. Hayes ◽  
Ko Kurokawa ◽  
Walter W. Wierwille
Keyword(s):  
Task Performance ◽  
Completion Time ◽  
Dwell Time ◽  
Age Groups ◽  
Instrument Panel ◽  
Task Completion ◽  
Task Completion Time ◽  
Age Related ◽  
Instrumented Vehicle ◽  
And Task

This research was undertaken, in part, to determine the magnitudes of performance decrements associated with automotive instrument panel tasks as a function of driver age. Driver eye scanning and dwell time measures and task completion measures were collected while 24 drivers aged 18 to 72 performed a variety of instrument panel tasks as each drove an instrumented vehicle along preselected routes. The results indicated a monotonically increasing relationship between driver age and task completion time and the number of glances to the instrument panel. Mean glance dwell times, either to the roadway or the instrument, were not significantly different among the various age groups. The nature of these differences for the various task categories used in the present study was examined.

A Taxonomy for Secondary In-Vehicle Tasks Based on Eye Glance and Task completion Time

2005 ◽  
Vol 49 (22) ◽  
pp. 1975-1979 ◽  
Author(s):  
Myra Blanco ◽  
Jonathan M. Hankey ◽  
Jacqueline A. Chestnut
Keyword(s):  
Performance Measures ◽  
Completion Time ◽  
Dynamic Testing ◽  
Task Completion ◽  
Task Completion Time ◽  
And Task

The objective of this research was to develop an initial taxonomy that grouped similar secondary in-vehicle tasks based on driving-related performance measures. This type of taxonomy would be useful to system designers when developing in-vehicle tasks and to researchers. Research was conducted using 2 infotainment systems, 17 tasks, and 89 participants to develop and validate an initial taxonomy. The results indicate that the 17 tasks could be parsed into four distinct groups ranging from selecting an AM band to destination entry. The groupings are based on number of glances and task completion time, which provided the best separation between the groups and consistent results for both static and dynamic testing.

scholarly journals Trade-Off between Task Accuracy, Task Completion Time and Naturalness for Direct Object Manipulation in Virtual Reality

2022 ◽  
Vol 6 (1) ◽  
pp. 6
Author(s):  
Jari Kangas ◽  
Sriram Kishore Kumar ◽  
Helena Mehtonen ◽  
Jorma Järnstedt ◽  
Roope Raisamo
Keyword(s):  
Virtual Reality ◽  
Completion Time ◽  
Object Manipulation ◽  
Direct Object ◽  
Task Completion ◽  
Interaction Techniques ◽  
Interaction Technique ◽  
Trade Off ◽  
Task Completion Time ◽  
Task Accuracy

Virtual reality devices are used for several application domains, such as medicine, entertainment, marketing and training. A handheld controller is the common interaction method for direct object manipulation in virtual reality environments. Using hands would be a straightforward way to directly manipulate objects in the virtual environment if hand-tracking technology were reliable enough. In recent comparison studies, hand-based systems compared unfavorably against the handheld controllers in task completion times and accuracy. In our controlled study, we compare these two interaction techniques with a new hybrid interaction technique which combines the controller tracking with hand gestures for a rigid object manipulation task. The results demonstrate that the hybrid interaction technique is the most preferred because it is intuitive, easy to use, fast, reliable and it provides haptic feedback resembling the real-world object grab. This suggests that there is a trade-off between naturalness, task accuracy and task completion time when using these direct manipulation interaction techniques, and participants prefer to use interaction techniques that provide a balance between these three factors.

scholarly journals Validation of Trade-Off in Human–Automation Interaction: An Empirical Study of Contrasting Office Automation Effects on Task Performance and Workload

2020 ◽  
Vol 10 (4) ◽  
pp. 1288
Author(s):  
Byung Cheol Lee ◽  
Jangwoon Park ◽  
Heejin Jeong ◽  
Jaehyun Park
Keyword(s):  
Task Performance ◽  
Completion Time ◽  
Office Automation ◽  
Task Completion ◽  
Trade Off ◽  
Task Completion Time ◽  
Task Load ◽  
Subjective Workload ◽  
Human Operators ◽  
High Level

Automation aims to improve the task performance and the safety of human operators. The success of automation can be facilitated with well-designed human–automation interaction (HAI), which includes the consideration of a trade-off between the benefits of reliable automation and the cost of Failed automation. This study evaluated four different types of HAIs in order to validate the automation trade-off, and HAI types were configured by the levels and the statuses of office automation. The levels of automation were determined by information amount (i.e., Low and High), and the statues were decided by automation function (i.e., Routine and Failed). Task performance including task completion time and accuracy and subjective workload of participants were measured in the evaluation of the HAIs. Relatively better task performance (short task completion time and high accuracy) were presented in the High level in Routine automation, while no significant effects of automation level were reported in Failed automation. The subjective workload by the National Aeronautics and Space Administration (NASA) Task Load Index (TLX) showed higher workload in High and Failed automation than Low and Failed automation. The type of sub-functions and the task classification can be estimated as major causes of automation trade-off, and dissimilar results between empirical and subjective measures need to be considered in the design of effective HAI.

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