EXPONENTIAL DECAY REAL-TIME CAPACITY PLANNING
Various examples are disclosed for forecasting resource usage and computing capacity utilizing an exponential decay. In some examples, a computing environment can obtain usage measurements from a data stream over a time interval, where the usage measurements describe utilization of computing resourc...
Gespeichert in:
Hauptverfasser: | , , , , , , , |
---|---|
Format: | Patent |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext bestellen |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | |
---|---|
container_issue | |
container_start_page | |
container_title | |
container_volume | |
creator | Lin, Junyuan Gao, Peng Wang, Xing Lu, Jinyi Brown, Darren Mathur, Keshav Pedersen, Paul Nutman, Leah |
description | Various examples are disclosed for forecasting resource usage and computing capacity utilizing an exponential decay. In some examples, a computing environment can obtain usage measurements from a data stream over a time interval, where the usage measurements describe utilization of computing resource. The computing environment can generate a weight function for individual ones of the usage measurements, where the weight function exponentially decays the usage measurements based on a respective time period at which the usage measurements were obtained. The computing environment can forecast a future capacity of the computing resources based on the usage measurements and the weight function assigned to the individual ones of the usage measurements. The computing environment can further upgrade a forecast engine to use the exponential decay without resetting the forecast engine or its memory. |
format | Patent |
fullrecord | <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_US2020371896A1</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>US2020371896A1</sourcerecordid><originalsourceid>FETCH-epo_espacenet_US2020371896A13</originalsourceid><addsrcrecordid>eNrjZNB1jQjw93P1C_F09FFwcXV2jFQIcnX00Q3x9HVVcHYMcHT2DIlUCPBx9PPz9HPnYWBNS8wpTuWF0twMym6uIc4euqkF-fGpxQWJyal5qSXxocFGBkYGxuaGFpZmjobGxKkCAFDuJmY</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>EXPONENTIAL DECAY REAL-TIME CAPACITY PLANNING</title><source>esp@cenet</source><creator>Lin, Junyuan ; Gao, Peng ; Wang, Xing ; Lu, Jinyi ; Brown, Darren ; Mathur, Keshav ; Pedersen, Paul ; Nutman, Leah</creator><creatorcontrib>Lin, Junyuan ; Gao, Peng ; Wang, Xing ; Lu, Jinyi ; Brown, Darren ; Mathur, Keshav ; Pedersen, Paul ; Nutman, Leah</creatorcontrib><description>Various examples are disclosed for forecasting resource usage and computing capacity utilizing an exponential decay. In some examples, a computing environment can obtain usage measurements from a data stream over a time interval, where the usage measurements describe utilization of computing resource. The computing environment can generate a weight function for individual ones of the usage measurements, where the weight function exponentially decays the usage measurements based on a respective time period at which the usage measurements were obtained. The computing environment can forecast a future capacity of the computing resources based on the usage measurements and the weight function assigned to the individual ones of the usage measurements. The computing environment can further upgrade a forecast engine to use the exponential decay without resetting the forecast engine or its memory.</description><language>eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; ELECTRIC DIGITAL DATA PROCESSING ; PHYSICS</subject><creationdate>2020</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=20201126&DB=EPODOC&CC=US&NR=2020371896A1$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,776,881,25543,76294</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20201126&DB=EPODOC&CC=US&NR=2020371896A1$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>Lin, Junyuan</creatorcontrib><creatorcontrib>Gao, Peng</creatorcontrib><creatorcontrib>Wang, Xing</creatorcontrib><creatorcontrib>Lu, Jinyi</creatorcontrib><creatorcontrib>Brown, Darren</creatorcontrib><creatorcontrib>Mathur, Keshav</creatorcontrib><creatorcontrib>Pedersen, Paul</creatorcontrib><creatorcontrib>Nutman, Leah</creatorcontrib><title>EXPONENTIAL DECAY REAL-TIME CAPACITY PLANNING</title><description>Various examples are disclosed for forecasting resource usage and computing capacity utilizing an exponential decay. In some examples, a computing environment can obtain usage measurements from a data stream over a time interval, where the usage measurements describe utilization of computing resource. The computing environment can generate a weight function for individual ones of the usage measurements, where the weight function exponentially decays the usage measurements based on a respective time period at which the usage measurements were obtained. The computing environment can forecast a future capacity of the computing resources based on the usage measurements and the weight function assigned to the individual ones of the usage measurements. The computing environment can further upgrade a forecast engine to use the exponential decay without resetting the forecast engine or its memory.</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>ELECTRIC DIGITAL DATA PROCESSING</subject><subject>PHYSICS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2020</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZNB1jQjw93P1C_F09FFwcXV2jFQIcnX00Q3x9HVVcHYMcHT2DIlUCPBx9PPz9HPnYWBNS8wpTuWF0twMym6uIc4euqkF-fGpxQWJyal5qSXxocFGBkYGxuaGFpZmjobGxKkCAFDuJmY</recordid><startdate>20201126</startdate><enddate>20201126</enddate><creator>Lin, Junyuan</creator><creator>Gao, Peng</creator><creator>Wang, Xing</creator><creator>Lu, Jinyi</creator><creator>Brown, Darren</creator><creator>Mathur, Keshav</creator><creator>Pedersen, Paul</creator><creator>Nutman, Leah</creator><scope>EVB</scope></search><sort><creationdate>20201126</creationdate><title>EXPONENTIAL DECAY REAL-TIME CAPACITY PLANNING</title><author>Lin, Junyuan ; Gao, Peng ; Wang, Xing ; Lu, Jinyi ; Brown, Darren ; Mathur, Keshav ; Pedersen, Paul ; Nutman, Leah</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_US2020371896A13</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>eng</language><creationdate>2020</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>ELECTRIC DIGITAL DATA PROCESSING</topic><topic>PHYSICS</topic><toplevel>online_resources</toplevel><creatorcontrib>Lin, Junyuan</creatorcontrib><creatorcontrib>Gao, Peng</creatorcontrib><creatorcontrib>Wang, Xing</creatorcontrib><creatorcontrib>Lu, Jinyi</creatorcontrib><creatorcontrib>Brown, Darren</creatorcontrib><creatorcontrib>Mathur, Keshav</creatorcontrib><creatorcontrib>Pedersen, Paul</creatorcontrib><creatorcontrib>Nutman, Leah</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Lin, Junyuan</au><au>Gao, Peng</au><au>Wang, Xing</au><au>Lu, Jinyi</au><au>Brown, Darren</au><au>Mathur, Keshav</au><au>Pedersen, Paul</au><au>Nutman, Leah</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>EXPONENTIAL DECAY REAL-TIME CAPACITY PLANNING</title><date>2020-11-26</date><risdate>2020</risdate><abstract>Various examples are disclosed for forecasting resource usage and computing capacity utilizing an exponential decay. In some examples, a computing environment can obtain usage measurements from a data stream over a time interval, where the usage measurements describe utilization of computing resource. The computing environment can generate a weight function for individual ones of the usage measurements, where the weight function exponentially decays the usage measurements based on a respective time period at which the usage measurements were obtained. The computing environment can forecast a future capacity of the computing resources based on the usage measurements and the weight function assigned to the individual ones of the usage measurements. The computing environment can further upgrade a forecast engine to use the exponential decay without resetting the forecast engine or its memory.</abstract><oa>free_for_read</oa></addata></record> |
fulltext | fulltext_linktorsrc |
identifier | |
ispartof | |
issn | |
language | eng |
recordid | cdi_epo_espacenet_US2020371896A1 |
source | esp@cenet |
subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING ELECTRIC DIGITAL DATA PROCESSING PHYSICS |
title | EXPONENTIAL DECAY REAL-TIME CAPACITY PLANNING |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-21T18%3A41%3A48IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-epo_EVB&rft_val_fmt=info:ofi/fmt:kev:mtx:patent&rft.genre=patent&rft.au=Lin,%20Junyuan&rft.date=2020-11-26&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3EUS2020371896A1%3C/epo_EVB%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |