Research on Adaptable Load Balancing of Task in Cloud Robots

Abstract:

Robotics has been widely applied in all kinds of such fields as production, service and special operation, but its integrated intelligence has been limited by the capability of equipped chips. To improve the capability, robots use the services offered by background clusters through the network. The load balancing in cloud robot, in fact, describes the problem of assigning tasks to the front or the back. So far, the load balancing strategy is only for the data server and metadata server load balancing, and does not take the load balancing of task between client and server. In this paper, we put forward an adaptable load balancing of task (ALBT), which is based on the client’s running condition and network delay cost, including basic load balancing of task (BLBT) and dynamic adjusting algorithm (DAA). The basic task load balancing can balance task between robot and cluster system according to the current condition of robot and the complexity of task. In addition, the dynamic adjusting algorithm can in real time transfer task load between them using an event mechanism. ALBT adjusts itself to the environment which can meet robot’s requirements with good performance and robustness.

• Periodical: Advanced Materials Research (Volumes 998-999)
• Pages: 1190-1194
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.998-999.1190
• Online since: July 2014
• Authors: Wen Jia He*, Kai Pan, Xun Li, An Yuan Chen
• Keywords: Adaptability, ALBT, Cloud Robot, Load Balancing of Task

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