Mr. Guoli Yang Profile

Guoli Yang

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Proceedings Article | 29 April 2023 Paper

Research and improvement of load balancing technology based on SIP server clusters

Jun Liu, Shuming Jiang, Guoli Yang

Proceedings Volume 12610, 1261047 (2023) https://doi.org/10.1117/12.2671098

KEYWORDS: Mathematical optimization, Reliability, Analytical research, Multimedia, Internet technology, Internet, Distributed computing

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In order to solve the problem of unbalanced Session Initiation Protocol (SIP) requests distribution in SIP server clusters when processing concurrent task requests, an improved dynamic load balancing optimization algorithm based on SIP transactions is proposed. Firstly, according to the characteristics of SIP protocol, SIP transactions are used as the unit to measure the load, different weights are assigned to different types of transactions, and the variability between server nodes and network performance are considered to measure the load of server nodes together with the response time. Then, the load factor values dynamically obtained are used to calculate the load ratio of the server nodes and compare it with the set threshold value, and the server node with the lowest load ratio in the normal state is selected to allocate SIP requests according to the comparison results, which allows the server nodes to have a buffering process, reduces the occurrence of throughput jitter phenomenon, and effectively avoids system overload. By using the open-source testing tool SIPp to conduct experiments, the results show that the improved method can solve the load imbalance problem, which makes the SIP server cluster system allocate resources efficiently and reasonably, improves the utilization of system resources, has a better balance and reliability compared with other algorithm models, and achieves the expected results.

Proceedings Article | 29 April 2023 Paper

Dynamic optimization of load balancing for streaming server clusters based on LAE-KNN

Nengwu Liu, Shuming Jiang, Guoli Yang

Proceedings Volume 12610, 126103C (2023) https://doi.org/10.1117/12.2671079

KEYWORDS: Information operations, Matrices, Mathematical optimization, Education and training, Video, Reflection, Random forests, Information technology

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In order to improve the timeliness of load feedback of streaming media cluster nodes and the processing efficiency of multiple concurrent requests, an improved algorithm based on dynamic feedback is proposed. The optimization of the method is as follows: (1) The calculation method of the load index weight coefficient and load weight value is improved, where the Least Squares is used to combine the optimization Analysis Hierarchy Process and Entropy Weight Method (denoted as LAE), which combine subjective weighting and objective weighting; (2) The feedback period is dynamically modified by the change in the number of tasks of the cluster nodes; (3) The Euclidean distance of the KNN algorithm is changed to a weighted Euclidean distance based on the weights obtained in (1). The cluster nodes are classified according to the improved KNN algorithm (denoted as LAE-KNN) and the load information, and the tasks are assigned to the class with the smallest total weight ratio; (4) At the same time, load migration is realized by setting the threshold of the load index, and random tasks of nodes exceeding the threshold of the load index are redirected to the low-load class according to the load information in each feedback cycle, to improve the load balancing effect of the cluster. Experiments show that the algorithm can effectively solve the problem of cluster load skew caused by many concurrent requests and can improve the load balancing effect of streaming media clusters.

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