An Energy-Aware Optimization Model Based on Data Placement and Task Scheduling

2013 ◽  
Author(s):  
Xiaoli Wang ◽  
Yuping Wang ◽  
Kun Meng
Keyword(s):  
Task Scheduling ◽  
Optimization Model ◽  
Data Placement ◽  
Energy Aware ◽  
Model Based ◽  
And Task

scholarly journals Group Mobility Model for Complex Multimission Cooperation of UAV Swarm

2022 ◽  
Vol 2022 ◽  
pp. 1-22
Author(s):  
Xiaoyan Gu ◽  
Feng He ◽  
Rongwei Wang ◽  
Liang Chen
Keyword(s):  
Real Time ◽  
Task Scheduling ◽  
Network Connectivity ◽  
Mobility Model ◽  
Reference Node ◽  
Class Mobility ◽  
Time Performance ◽  
Model Based ◽  
Uav Swarm ◽  
And Task

In the unmanned aerial vehicle (UAV) swarm combat system, multiple UAVs’ collaborative operations can solve the bottleneck of the limited capability of a single UAV when they carry out complicated missions in complex combat scenarios. As one of the critical technologies of UAV collaborative operation, the mobility model is the basic infrastructure that plays an important role for UAV networking, routing, and task scheduling, especially in high dynamic and real-time scenarios. Focused on real-time guarantee and complex mission cooperative execution, a multilevel reference node mobility model based on the reference node strategy, namely, the ML-RNGM model, is proposed. In this model, the task decomposition and task correlation of UAV cluster execution are realized by using the multilayer task scheduling model. Based on the gravity model of spatial interaction and the correlation between tasks, the reference node selection algorithm is proposed to select the appropriate reference node in the process of node movement. This model can improve the real-time performance of individual tasks and the overall mission group carried out by UAVs. Meanwhile, this model can enhance the connectivity between UAVs when they are performing the same mission group. Finally, OMNeT++ is used to simulate the ML-RNGM model with three experiments, including the different number of nodes and clusters. Within the three experiments, the ML-RNGM model is compared with the random class mobility model, the reference class mobility model, and the associated class mobility model for the network connectivity rate, the average end-to-end delay, and the overhead caused by algorithms. The experimental results show that the ML-RNGM model achieves an obvious improvement in network connectivity and real-time performance for missions and tasks.

Adaptive NUMA-aware data placement and task scheduling for analytical workloads in main-memory column-stores

2016 ◽  
Vol 10 (2) ◽  
pp. 37-48 ◽  
Author(s):  
Iraklis Psaroudakis ◽  
Tobias Scheuer ◽  
Norman May ◽  
Abdelkader Sellami ◽  
Anastasia Ailamaki
Keyword(s):  
Task Scheduling ◽  
Data Placement ◽  
Main Memory ◽  
Column Stores ◽  
And Task
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