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A resource pre-allocation method for cognitive analytics-based social media service in edge computing.

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Title: A resource pre-allocation method for cognitive analytics-based social media service in edge computing.
Authors: Wang, Ruizhi1 (AUTHOR) 2478672934@qq.com, Zhang, Difei2 (AUTHOR)
Source: Wireless Networks (10220038). Aug2024, Vol. 30 Issue 6, p6135-6150. 16p.
Subjects: Web hosting, Edge computing, Telecommunication, Computer systems, Smart cities
Abstract: With the rapid development of 5G mobile communication technology, the growing demand for media services in society is becoming a characteristic of smart cities. Technical study has exhaustively investigated the low-latency resource supply of edge computing systems for web hosting services. However, external variables (such as transmission and network delays) impact the transmission between edge servers and service requests, resulting in service request delays. In addition, resource service requests that are constantly updated and in dynamic distribution may overload some servers while others are idle, resulting in poor load balancing. Consequently, this research presents a Resource Pre-Allocation method (RPA) for cognitive analytics-based social media platforms, which aims to improve the load balancing of edge servers while serving requests with strict latency requirements, so as to obtain the optimal resource allocation strategy. First, the resource requirement prediction algorithm is developed based on temporal-spatial demand history. Then, we propose a multi-objective algorithm combined with the optimal solution selection techniques to obtain the ideal resource allocation decisions. Finally, the performance of RPA is tested and evaluated. The experimental results show that RPA can allocate resources better than other methods. [ABSTRACT FROM AUTHOR]
Copyright of Wireless Networks (10220038) is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: A resource pre-allocation method for cognitive analytics-based social media service in edge computing.
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  Data: <searchLink fieldCode="AR" term="%22Wang%2C+Ruizhi%22">Wang, Ruizhi</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> 2478672934@qq.com</i><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Difei%22">Zhang, Difei</searchLink><relatesTo>2</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Wireless+Networks+%2810220038%29%22">Wireless Networks (10220038)</searchLink>. Aug2024, Vol. 30 Issue 6, p6135-6150. 16p.
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  Data: <searchLink fieldCode="DE" term="%22Web+hosting%22">Web hosting</searchLink><br /><searchLink fieldCode="DE" term="%22Edge+computing%22">Edge computing</searchLink><br /><searchLink fieldCode="DE" term="%22Telecommunication%22">Telecommunication</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+systems%22">Computer systems</searchLink><br /><searchLink fieldCode="DE" term="%22Smart+cities%22">Smart cities</searchLink>
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  Label: Abstract
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  Data: With the rapid development of 5G mobile communication technology, the growing demand for media services in society is becoming a characteristic of smart cities. Technical study has exhaustively investigated the low-latency resource supply of edge computing systems for web hosting services. However, external variables (such as transmission and network delays) impact the transmission between edge servers and service requests, resulting in service request delays. In addition, resource service requests that are constantly updated and in dynamic distribution may overload some servers while others are idle, resulting in poor load balancing. Consequently, this research presents a Resource Pre-Allocation method (RPA) for cognitive analytics-based social media platforms, which aims to improve the load balancing of edge servers while serving requests with strict latency requirements, so as to obtain the optimal resource allocation strategy. First, the resource requirement prediction algorithm is developed based on temporal-spatial demand history. Then, we propose a multi-objective algorithm combined with the optimal solution selection techniques to obtain the ideal resource allocation decisions. Finally, the performance of RPA is tested and evaluated. The experimental results show that RPA can allocate resources better than other methods. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Wireless Networks (10220038) is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Aug2024
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