A Novel Cache-based Framework for Accelerating Real-time Flow Transmission
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Xingyan Zhang, Luokai Hu, Haijun Wang, Limin Liu
Real-time flow transmission is widely used in various online applications, such as video transmission, social networks, etc. Most of these tasks which require strict deadlines need to be handled by distributed systems, so the deadline guarantee of the whole system can be divided into each node. However, due to insufficient computing power or software errors, some nodes are prone to delay data processing, leading to the loss of computing deadline, resulting in the loss of the cut-off event of the whole system, and causing the data processing delay of the next link. In view of the performance loss caused by this situation, this paper proposes a cache-based processing framework to speed up the forwarding of real-time data flows. By estimating the data processing ability of nodes online, when the processing ability of nodes is out of order, some computing tasks can be mapped to other preparatory nodes online, so as to achieve fast task. Migration ensures the deadline of computing tasks on nodes. By adjusting reasonable system parameters, the processing capacity of nodes in distributed system can be accelerated, so that the processing capacity of the system can be increased dynamically and the data flow forwarding capability of the whole system can be accelerated. The results show that this method can increase the number of data forwarding by 23% and reduce the data loss rate by 31%.
Online Social Networks, Data Center, Flow Transmission