Investigating a Dynamic Loop Scheduling with Reinforcement Learning Approach to Load Balancing in Scientific Applications

2008 ◽  
Author(s):  
Mahbubur Rashid ◽  
Ioana Banicescu ◽  
Ricolindo L. Carino
Keyword(s):  
Reinforcement Learning ◽  
Load Balancing ◽  
Learning Approach ◽  
Loop Scheduling ◽  
Scientific Applications

LOAD BALANCING IN HOME-BASED SOFTWARE DSMS

2001 ◽  
Vol 12 (03) ◽  
pp. 307-324
Author(s):  
WEISONG SHI ◽  
ZHIMIN TANG
Keyword(s):  
Load Balancing ◽  
Dynamic Scheduling ◽  
Critical Issue ◽  
Research Area ◽  
Data Migration ◽  
Loop Scheduling ◽  
Scientific Applications ◽  
Task Migration ◽  
Dynamic Task ◽  
Home Based

Load balancing is a critical issue for achieving good performance in parallel and distributed systems. However, this issue is neglected in the research area of software DSMs in the past decade. Based on the observation that scientific applications can be classified into two categories: iterative and non-iterative, we propose two dynamic scheduling schemes for these two cases respectively in this paper. For iterative scientific applications, a dynamic task migration technique is proposed which characterizes itself with integrating computation migration and data migration together. An affinity-based self scheduling (ABS) is proposed for non-iterative scientific applications, which take both the static and dynamic processor affinity into consideration when scheduling. The target experiment platform is a state-of-the-art home-based DSM system named JIAJIA. Performance evaluation results show that the novel task migration scheme improves the performance ranging from 36% to 50% compared with a static task allocation scheme in a metacomputing environment, and performs better than traditional task (computation-only) migration approach about 12.5% for MAT, and 37.5% for SOR and EM3D. Higher resource utilization is achieved via the new task migration scheme too. In comparison with other loop scheduling schemes, the ABS achieves the best performance among all scheduling schemes in a metacomputing environment because of the reduction of synchronization overhead and the great improvement of waiting time resulting from load imbalance.

Energy-efficient UAV trajectory design for backscatter communication: A deep reinforcement learning approach

2020 ◽  
Vol 17 (10) ◽  
pp. 129-141
Author(s):  
Yiwen Nie ◽  
Junhui Zhao ◽  
Jun Liu ◽  
Jing Jiang ◽  
Ruijin Ding
Keyword(s):  
Reinforcement Learning ◽  
Energy Efficient ◽  
Learning Approach ◽  
Trajectory Design ◽  
Backscatter Communication

Attentional Shifting and Curiosity: a Reinforcement Learning Approach

2016 ◽  
Author(s):  
Dario di Nocera ◽  
Alberto Finzi ◽  
Silvia Rossi ◽  
Mariacarla Staffa
Keyword(s):  
Reinforcement Learning ◽  
Learning Approach ◽  
Attentional Shifting
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