Load Balancing on Dynamic Network Using Mobile Process Groups

2007 ◽  
Author(s):  
Aakanksha Vatsa ◽  
Punam Bedi
Keyword(s):  
Load Balancing ◽  
Dynamic Network ◽  
Mobile Process ◽  
Process Groups

Link Assignment for Wireless Sensor Networks with Load Balancing Control

2014 ◽  
Vol 530-531 ◽  
pp. 739-742
Author(s):  
Yan Hong Yang ◽  
Xiao Tong Zhang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Load Balancing ◽  
Dynamic Network ◽  
Wireless Sensor ◽  
Message Complexity ◽  
Original Network ◽  
Performance Results ◽  
Balancing Control ◽  
Made In

This paper proposes and analyzes load balancing control in which numbers of sensors randomly join the original network. Sensors are better suited according to some metrics than arbitrary accessing. Analytic performance results are made in terms of message complexity, link changes and delay. Specially, we discussed the link assignment in dynamic network which is caused adjustment. In load balancing control, sensors are selected based on transmission queue and children number. Results indicate that with observed metrics contributes to better performance.

Resource-Aware Load Balancing of Parallel Applications

2009 ◽  
pp. 12-21 ◽  
Author(s):  
Eric Aubanel
Keyword(s):  
Load Balancing ◽  
Dynamic Network ◽  
Computational Grid ◽  
Parallel Applications ◽  
Computational Grids ◽  
Concurrent Execution ◽  
Processor Performance ◽  
Wide Range ◽  
Tightly Coupled ◽  
Resource Aware

The problem of load balancing parallel applications is particularly challenging on computational grids, since the characteristics of both the application and the platform must be taken into account. This chapter reviews the wide range of solutions that have been proposed. It considers tightly coupled parallel applications that can be described by an undirected graph representing concurrent execution of tasks and communication of tasks, executing on computational grids with static and dynamic network and processor performance. While a rich set of solution techniques have been proposed, there has not been of yet any performance comparisons between them. Such comparisons will require parallel benchmarks and computational grid emulators and simulators.

scholarly journals Q-LBR: Q-Learning Based Load Balancing Routing for UAV-Assisted VANET

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5685
Author(s):  
Bong-Soo Roh ◽  
Myoung-Hun Han ◽  
Jae-Hyun Ham ◽  
Ki-Il Kim
Keyword(s):  
Load Balancing ◽  
Traffic Congestion ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Control Function ◽  
Dynamic Network ◽  
Relay Node ◽  
Delivery Ratio ◽  
Q Learning ◽  
Hoc Networks

Although various unmanned aerial vehicle (UAV)-assisted routing protocols have been proposed for vehicular ad hoc networks, few studies have investigated load balancing algorithms to accommodate future traffic growth and deal with complex dynamic network environments simultaneously. In particular, owing to the extended coverage and clear line-of-sight relay link on a UAV relay node (URN), the possibility of a bottleneck link is high. To prevent problems caused by traffic congestion, we propose Q-learning based load balancing routing (Q-LBR) through a combination of three key techniques, namely, a low-overhead technique for estimating the network load through the queue status obtained from each ground vehicular node by the URN, a load balancing scheme based on Q-learning and a reward control function for rapid convergence of Q-learning. Through diverse simulations, we demonstrate that Q-LBR improves the packet delivery ratio, network utilization and latency by more than 8, 28 and 30%, respectively, compared to the existing protocol.

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