Research on the optimum synchronous network search data extraction based on swarm intelligence algorithm
Existing synchronous network search data extraction techniques suffer from load unbalance in the face of increasing number of concurrent users. For this reason, this paper presents the research on the optimum synchronous network search data extraction based on swarm intelligence algorithm. A traffic...
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| Vydáno v: | Future generation computer systems Ročník 125; s. 151 - 155 |
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| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier B.V
01.12.2021
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| Témata: | |
| ISSN: | 0167-739X, 1872-7115 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | Existing synchronous network search data extraction techniques suffer from load unbalance in the face of increasing number of concurrent users. For this reason, this paper presents the research on the optimum synchronous network search data extraction based on swarm intelligence algorithm. A traffic balancing model is constructed to determine the node balance state according to the traffic variation and influence factors of network nodes. Under the condition that the node state is known, the swarm intelligence algorithm is used to cluster the data to be synchronized and adjust the node state so that it is kept stable throughout the synchronization process. The clustered data act as the target to connect the user with the server side to achieve the optimum network search data extraction and synchronization. The experimental results show that when the number of concurrent network users is increasing, the designed technique features stable load balancing, and achieves optimum data extraction performance and low execution cost when the task completion time is less than 0.5 s.
•A traffic balancing model is constructed to determine node balance state.•The swarm intelligence algorithm is used to cluster data and adjust node state.•Clustered data is used to achieve optimum search extraction and synchronization. |
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| ISSN: | 0167-739X 1872-7115 |
| DOI: | 10.1016/j.future.2021.05.001 |