Machine learning segment routing for multiple traffic matrices
In some embodiments, a method may be provided, the method comprising: receiving a first traffic matrix; receiving information about a link associated with each segment of the network; determining a total amount of segment streams using at least one non-linear deflection parameter applied to the traf...
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| creator | KODIALAM MURALIDHARAN LAKSHMAN T V |
| description | In some embodiments, a method may be provided, the method comprising: receiving a first traffic matrix; receiving information about a link associated with each segment of the network; determining a total amount of segment streams using at least one non-linear deflection parameter applied to the traffic requirements of the first traffic matrix; determining a link flow for each link using the total amount of segment flows and a second input of the machine learning model; determining a link utilization rate of each link by using the link flow and the capacity of each link; by adjusting at least a value of at least one non-linear deflection parameter, a gradient descent method is used to learn a minimum value of a maximum amount of link utilization over the link by the machine learning model. Related systems, methods, and articles of manufacture are also disclosed.
在一些实施例中，可提供一种方法，该方法包括：接收第一业务矩阵；接收关于与网络的每个分段相关联的链路的信息；使用应用于第一业务矩阵的业务需求的至少一个非线性偏转参数，确定分段流的总量；使用分段流的总量、以及机器学习模型的第二输入，确定每个链路的链路流；使用链路流、以及每个链路的容量，确定每个链路的链路 |
| format | Patent |
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在一些实施例中，可提供一种方法，该方法包括：接收第一业务矩阵；接收关于与网络的每个分段相关联的链路的信息；使用应用于第一业务矩阵的业务需求的至少一个非线性偏转参数，确定分段流的总量；使用分段流的总量、以及机器学习模型的第二输入，确定每个链路的链路流；使用链路流、以及每个链路的容量，确定每个链路的链路</description><language>chi ; eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; ELECTRIC COMMUNICATION TECHNIQUE ; ELECTRICITY ; PHYSICS ; TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHICCOMMUNICATION</subject><creationdate>2024</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20241018&DB=EPODOC&CC=CN&NR=118802702A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,776,881,25542,76289</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20241018&DB=EPODOC&CC=CN&NR=118802702A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>KODIALAM MURALIDHARAN</creatorcontrib><creatorcontrib>LAKSHMAN T V</creatorcontrib><title>Machine learning segment routing for multiple traffic matrices</title><description>In some embodiments, a method may be provided, the method comprising: receiving a first traffic matrix; receiving information about a link associated with each segment of the network; determining a total amount of segment streams using at least one non-linear deflection parameter applied to the traffic requirements of the first traffic matrix; determining a link flow for each link using the total amount of segment flows and a second input of the machine learning model; determining a link utilization rate of each link by using the link flow and the capacity of each link; by adjusting at least a value of at least one non-linear deflection parameter, a gradient descent method is used to learn a minimum value of a maximum amount of link utilization over the link by the machine learning model. Related systems, methods, and articles of manufacture are also disclosed.
在一些实施例中，可提供一种方法，该方法包括：接收第一业务矩阵；接收关于与网络的每个分段相关联的链路的信息；使用应用于第一业务矩阵的业务需求的至少一个非线性偏转参数，确定分段流的总量；使用分段流的总量、以及机器学习模型的第二输入，确定每个链路的链路流；使用链路流、以及每个链路的容量，确定每个链路的链路</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>ELECTRIC COMMUNICATION TECHNIQUE</subject><subject>ELECTRICITY</subject><subject>PHYSICS</subject><subject>TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHICCOMMUNICATION</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2024</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZLDzTUzOyMxLVchJTSzKy8xLVyhOTc9NzStRKMovLQHx0_KLFHJLc0oyC3JSFUqKEtPSMpMVchNLijKTU4t5GFjTEnOKU3mhNDeDoptriLOHbmpBfnxqcUFicmpeakm8s5-hoYWFgZG5gZGjMTFqAIKXMOk</recordid><startdate>20241018</startdate><enddate>20241018</enddate><creator>KODIALAM MURALIDHARAN</creator><creator>LAKSHMAN T V</creator><scope>EVB</scope></search><sort><creationdate>20241018</creationdate><title>Machine learning segment routing for multiple traffic matrices</title><author>KODIALAM MURALIDHARAN ; LAKSHMAN T V</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN118802702A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2024</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>ELECTRIC COMMUNICATION TECHNIQUE</topic><topic>ELECTRICITY</topic><topic>PHYSICS</topic><topic>TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHICCOMMUNICATION</topic><toplevel>online_resources</toplevel><creatorcontrib>KODIALAM MURALIDHARAN</creatorcontrib><creatorcontrib>LAKSHMAN T V</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>KODIALAM MURALIDHARAN</au><au>LAKSHMAN T V</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Machine learning segment routing for multiple traffic matrices</title><date>2024-10-18</date><risdate>2024</risdate><abstract>In some embodiments, a method may be provided, the method comprising: receiving a first traffic matrix; receiving information about a link associated with each segment of the network; determining a total amount of segment streams using at least one non-linear deflection parameter applied to the traffic requirements of the first traffic matrix; determining a link flow for each link using the total amount of segment flows and a second input of the machine learning model; determining a link utilization rate of each link by using the link flow and the capacity of each link; by adjusting at least a value of at least one non-linear deflection parameter, a gradient descent method is used to learn a minimum value of a maximum amount of link utilization over the link by the machine learning model. Related systems, methods, and articles of manufacture are also disclosed.
在一些实施例中，可提供一种方法，该方法包括：接收第一业务矩阵；接收关于与网络的每个分段相关联的链路的信息；使用应用于第一业务矩阵的业务需求的至少一个非线性偏转参数，确定分段流的总量；使用分段流的总量、以及机器学习模型的第二输入，确定每个链路的链路流；使用链路流、以及每个链路的容量，确定每个链路的链路</abstract><oa>free_for_read</oa></addata></record> |
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| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING ELECTRIC COMMUNICATION TECHNIQUE ELECTRICITY PHYSICS TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHICCOMMUNICATION |
| title | Machine learning segment routing for multiple traffic matrices |
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