Clustering in WSN with Latency and Energy Consumption Constraints

2006 ◽  
Vol 14 (3) ◽  
pp. 415-439 ◽  
Author(s):  
Bassam Aoun ◽  
Raouf Boutaba
Keyword(s):  
Energy Consumption ◽  
Consumption Constraints

UAVs Fleet Mission Planning Subject to Weather Fore-Cast and Energy Consumption Constraints

2019 ◽  
pp. 104-114 ◽  
Author(s):  
Amila Thibbotuwawa ◽  
Peter Nielsen ◽  
Grzegorz Bocewicz ◽  
Zbigniew Banaszak
Keyword(s):  
Energy Consumption ◽  
Mission Planning ◽  
Consumption Constraints

scholarly journals Task-Level Aware Scheduling of Energy-Constrained Applications on Heterogeneous Multi-Core System

Electronics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2077
Author(s):  
Kai Huang ◽  
Ming Jing ◽  
Xiaowen Jiang ◽  
Siheng Chen ◽  
Xiaobo Li ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Schedule Algorithm ◽  
Parallel Applications ◽  
Core System ◽  
Parallel Application ◽  
Average Value ◽  
Schedule Length ◽  
Energy Constrained ◽  
Execution Frequency ◽  
Consumption Constraints

Minimizing the schedule length of parallel applications, which run on a heterogeneous multi-core system and are subject to energy consumption constraints, has recently attracted much attention. The key point of this problem is the strategy to pre-allocate the energy consumption of unscheduled tasks. Previous articles used the minimum value, average value or a power consumption weight value as the pre-allocation energy consumption of tasks. However, they all ignored the different levels of tasks. The tasks in different task levels have different impact on the overall schedule length when they are allocated the same energy consumption. Considering the task levels, we designed a novel task energy consumption pre-allocation strategy that is conducive to minimizing the scheduling time and developed a novel task schedule algorithm based on it. After getting the preliminary scheduling results, we also proposed a task execution frequency re-adjustment mechanism that can re-adjust the execution frequency of tasks, to further reduce the overall schedule length. We carried out a considerable number of experiments with practical parallel application models. The results of the experiments show that our method can reach better performance compared with the existing algorithms.

Resource allocation in two-tier small-cell networks with energy consumption constraints

2019 ◽  
Vol 13 (1) ◽  
pp. 1-15
Author(s):  
Libo Jiao ◽  
Hao Yin ◽  
Dongchao Guo ◽  
Haojun Huang ◽  
Qin Gao
Keyword(s):  
Resource Allocation ◽  
Energy Consumption ◽  
Small Cell ◽  
Small Cell Networks ◽  
Cell Networks ◽  
Consumption Constraints

Enhanced Parallel Application Scheduling Algorithm with Energy Consumption Constraint in Heterogeneous Distributed Systems

2019 ◽  
Vol 28 (11) ◽  
pp. 1950190 ◽  
Author(s):  
Jinghong Li ◽  
Guoqi Xie ◽  
Keqin Li ◽  
Zhuo Tang
Keyword(s):  
Energy Consumption ◽  
Distributed Systems ◽  
Gaussian Elimination ◽  
Scheduling Algorithm ◽  
Parallel Applications ◽  
Parallel Application ◽  
Heterogeneous Distributed Systems ◽  
Dynamic Voltage ◽  
Cluster Computer Systems ◽  
Consumption Constraints

Energy consumption has always been one of the main design problems in heterogeneous distributed systems, whether for large cluster computer systems or small handheld terminal devices. And as energy consumption explodes for complex performance, many efforts and work are focused on minimizing the schedule length of parallel applications that meet the energy consumption constraints currently. In prior studies, a pre-allocation method based on dynamic voltage and frequency scaling (DVFS) technology allocates unassigned tasks with minimal energy consumption. However, this approach does not necessarily result in minimal scheduling length. In this paper, we propose an enhanced scheduling algorithm, which allocates the same energy consumption for each task by selecting a relatively intermediate value among the unequal allocations. Based on the two real-world applications (Fast Fourier transform and Gaussian elimination) and the randomly generated parallel application, experiments show that the proposed algorithm not only achieves better scheduling length while meeting the energy consumption constraints, but also has better performance than the existing parallel algorithms.

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