{"id":33660,"date":"2019-10-31T21:53:59","date_gmt":"2019-10-31T18:53:59","guid":{"rendered":"https:\/\/prohoster.info\/blog\/campusinsight-ot-monitoringa-infrastruktury-k-analizu-polzovatelskogo-opyta\/"},"modified":"2019-10-31T21:53:59","modified_gmt":"2019-10-31T18:53:59","slug":"campusinsight-ot-monitoringa-infrastruktury-k-analizu-polzovatelskogo-opyta","status":"publish","type":"post","link":"https:\/\/prohoster.info\/sl\/blog\/administrirovanie\/campusinsight-ot-monitoringa-infrastruktury-k-analizu-polzovatelskogo-opyta","title":{"rendered":"CampusInsight: od spremljanja infrastrukture do analize uporabni\u0161ke izku\u0161nje","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"<p>Kakovost brez\u017ei\u010dne mre\u017ee je \u017ee privzeto vklju\u010dena v pojem ravni storitve. \u010ce \u017eelite zadovoljiti visoke zahteve strank, ni dovolj le hitro re\u0161evanje omre\u017enih te\u017eav, temve\u010d je treba tudi napovedovati najpogostej\u0161e med njimi.<\/p>\n<p>Kako to dose\u010di? Le z opazovanjem tega, kar je v tem kontekstu resni\u010dno pomembno \u2014 interakcijo uporabnika z brez\u017ei\u010dno mre\u017eo.<\/p>\n<p><img decoding=\"async\" alt=\"CampusInsight: od spremljanja infrastrukture do analize uporabni\u0161ke izku\u0161nje\" src=\"\/wp-content\/uploads\/2019\/05\/de74e4bb1c0b8394428124df2cfcee10.png\" style=\"display:block;margin: 0 auto;\" \/><br \/>\n<noindex><a rel=\"nofollow\" name=\"habracut\"><\/a><\/noindex><br \/>\nObremenitve na omre\u017eju nenehno rastejo, kar \u0161e posebej vpliva na brez\u017ei\u010dne segmente \u2014 vsaj zaradi odprtosti njihovega vmesnika. S pove\u010danjem \u0161tevila naprav in hitrosti prenosa podatkov se te\u017eave pomno\u017eijo na ve\u010d ravneh. Na fizi\u010dni ravni \u2014 \u0161tevilni prenosniki radijskih signalov vplivajo drug na drugega, tudi \u010de delujejo na sosednjih delih frekven\u010dnega spektra. Na logi\u010dni ravni \u2014 veliko \u0161tevilo povezanih naprav za\u010dne tekmovati za pravico, da za\u010dnejo prenos na izbrani frekvenci, kar pove\u010duje zakasnitev pri dostavi paketov za vsakega uporabnika.\u00a0<\/p>\n<p>Hkrati rastejo tudi pri\u010dakovanja vsake stranke glede uporabe omre\u017eja. 5-sekundno nalaganje strani v brskalniku, ki se je pred 20 leti zdelo 'vrhunec tehnologije', danes ne o\u010dara nikogar. Stranke pri\u010dakujejo HD-videoklic brez zatikanja slike.<\/p>\n<p>Delno re\u0161itev ponujajo nove razli\u010dice standardov brez\u017ei\u010dnega prenosa, ki u\u010dinkoviteje uporabljajo frekven\u010dni spekter. Vsaka <noindex><a rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/article\/449116\/\">nadaljna razli\u010dica Wi-Fi<\/a><\/noindex> je usmerjena v \u0161iritev vse bolj obremenjenih mre\u017e. V obse\u017eni mre\u017ei, kjer deluje ve\u010d deset dostopnih to\u010dk, pa \u017ee ni mo\u017eno prepustiti vsega novemu standardu (\u0161e posebej, ker naprave delujejo v na\u010dinu povratne zdru\u017eljivosti, takoj ko naletijo na staro uporabni\u0161ko napravo). Tako tudi ni mo\u017eno naprej \u017eiveti s starimi sredstvi za spremljanje \u2014 omre\u017eno okolje se stalno zapleta.<\/p>\n<h2>Zakaj obi\u010dajno spremljanje ve\u010d ne deluje<\/h2>\n<p>\nKlasi\u010den kli\u0161e, ki \u0161e vedno sledi skrbnikom vseh omre\u017eij, vklju\u010dno z brez\u017ei\u010dnimi, je delovanje izklju\u010dno po zahtevah. Ko 'alarm' zazvoni \u2014 se zbudimo in ugotovimo, kaj je \u0161lo narobe. Dokler 'alarma' ni, se lahko omejimo na preverjanje obremenitev osnovnih komponent \u2014 omre\u017enih in uporabni\u0161kih naprav.\u00a0<\/p>\n<p>V skladu s to nalogo tradicionalna orodja za spremljanje in vzdr\u017eevanje delujejo po na\u010delu strogih pravil in ne prikazujejo vedno obstoje\u010dih te\u017eav pravo\u010dasno, kaj \u0161ele kak\u0161no napovedno analizo.\u00a0<\/p>\n<p>Glavni problem je v intervalu zbiranja podatkov. Informacije o stanju brez\u017ei\u010dnih omre\u017enih povezav se zbirajo vsake minute, medtem ko se incidenti lahko zgodijo v intervalih med zbiranjem podatkov (odli\u010den primer so redki vrhovi obremenitve, ki 'zamrznejo' omre\u017eje). Brez podatkov v realnem \u010dasu je precej te\u017eko razumeti, kaj je globinski vzrok te\u017eave. Ali gre za nepravilno upravljanje omre\u017enega pokritja? Ali morda zunanje motnje, ki niso povezane s poslovanjem (recimo, voja\u0161ka enota je 'motila' v eteru). Nima podatkov, ki bi pokazali postopno degradacijo dolo\u010denih zna\u010dilnosti omre\u017eja, zato lokalizacija te\u017eave ni tako enostavna. IT osebje bo moralo porabiti dodatne ure za iskanje te 'igle v senu'.<br \/>\nKonec koncev pa uporabniki te\u017eavo opazijo skoraj takoj. Napaka pri povezovanju, prekinitev prenosa videa - odli\u010dni markerji.<\/p>\n<blockquote><p>Klasi\u010dna sredstva za spremljanje poro\u010dajo, da omre\u017eni paketi potujejo. A ne morejo odgovoriti na vpra\u0161anje, ali je uporabnik re\u0161il svojo nalogo.\n<\/p><\/blockquote>\n<p>\nDa bi dobili odgovor na to vpra\u0161anje, bo potrebno spremeniti ne le orodje, ampak tudi sam pristop k organizaciji spremljanja. Preiti je treba od 'gasilske' slu\u017ebe po zahtevkih (dejansko nadziranje zmogljivosti in obremenitve konkretne strojne opreme) k nadzoru uporabni\u0161ke izku\u0161nje in prepoznavanju situacij, ki bi lahko privedle do incidentov.<\/p>\n<p>Ta transformacija predvideva uvedbo bolj zapletenih algoritmov za dolo\u010danje te\u017eav, kot so preprosta opozorila ob dosegi dolo\u010denih vrednosti. Na platformi za inteligentno omre\u017eno analizo Huawei CampusInsight so ti algoritmi osnovani na izku\u0161njah upravljanja brez\u017ei\u010dnih omre\u017eij in tehnikah samo-u\u010denja.<\/p>\n<h2>Pod pokrovom CampusInsight<\/h2>\n<p>\nHuawei CampusInsight \u2013 raz\u0161irljiva platforma za spremljanje brez\u017ei\u010dnih omre\u017eij razli\u010dnih velikosti. Zgrajena je na podlagi mikrostoritvene arhitekture. Vsaka storitev je name\u0161\u010dena na ve\u010d instancah, sporo\u010dila med njimi pa razporeja ustrezna avtobus. Dodatne instance se lahko dinami\u010dno uvajajo, kar pove\u010duje preto\u010dnost orodja.<\/p>\n<p>CampusInsight dejansko zbira, analizira in prikazuje podatke v svojem uporabni\u0161kem vmesniku v petih korakih.<\/p>\n<p><img decoding=\"async\" alt=\"CampusInsight: od spremljanja infrastrukture do analize uporabni\u0161ke izku\u0161nje\" src=\"\/wp-content\/uploads\/2019\/05\/2d2856e469a0afcb391a249768aba13c.png\" style=\"display:block;margin: 0 auto;\" \/><br \/>\n<br \/>\n<b>Prvi in drugi korak<\/b> sta dostop do podatkov (do naprav, ki jih generirajo) in zbiranje \"meritev\". Z uporabo pretokovnega zbiranja telemetrije po protokolu GPB od Googla in \"tradicionalnega\" Syslog (kjer je to mogo\u010de) Huawei CampusInsight skoraj v realnem \u010dasu akumulira podatke:<\/p>\n<ul>\n<li>o izrabi frekven\u010dnega spektra;\n<\/li>\n<li>o delovanju dostopnih to\u010dk in drugih omre\u017enih naprav (kazalniki uspe\u0161nosti, \u0161tevilo povezanih uporabnikov itd.);\n<\/li>\n<li>o poti dolo\u010denih uporabnikov \u2013 o omre\u017enih profilih, kdo, kdaj in na katero dostopno to\u010dko se je povezal ali se ni povezal (in s kak\u0161nimi parametri povezave);\n<\/li>\n<li>o delovanju avdio-video aplikacij (z uporabo eMDI, implementirane v enem od dodatnih paketov).\n<\/li>\n<\/ul>\n<p>\nDa bi ob\u0161li omejitve tradicionalnih orodij, ki za zbiranje podatkov uporabljajo SNMP in po\u0161iljanje fiksnih struktur, je bila v osnovi CampusInsight zasnovana model naro\u010danja na potrebne dnevni\u0161ke zapise ter algoritme za kodiranje in dekodiranje podatkov.<\/p>\n<p><b>Tretji korak <\/b> je razporeditev in predpomnjenje \u2013 tj. po\u0161iljanje \"surovih\" podatkov v Kafka za razporeditev v bolj visokokakovostne analiti\u010dne storitve.<\/p>\n<p><b>\u010cetrti korak<\/b> je analiza. Algoritmi Big Data in AI pomagajo hitro obdelovati \"surove\" podatke. Kot rezultat se odkrijejo posamezne te\u017eave, povezane z:<\/p>\n<ul>\n<li>avtentikacijo (podprt je protokol Dot1x) in delovanjem DHCP;\n<\/li>\n<li>stabilnostjo in hitrostjo povezave;\n<\/li>\n<li>brez\u017ei\u010dnimi vmesniki;\n<\/li>\n<li>delovanjem posameznih naprav, vklju\u010dno z \"majhnimi podrobnostmi\", kot so te\u017eave z PoE ali preklapljanjem dvobandne naprave na 2,4 GHz;\n<\/li>\n<li>kakovostjo avdio-video tokov \u2013 res pa je, da funkcija podpira le ne\u0161ifrirano SIP ali nekatere stikala;\n<\/li>\n<li>romaingom med razli\u010dnimi dostopnimi to\u010dkami.\n<\/li>\n<\/ul>\n<p>\nAlgoritmi umetne inteligence se uporabljajo za re\u0161evanje dolo\u010denih specifi\u010dnih nalog, na primer odkrivanja motenj med kanali pri brez\u017ei\u010dni prenosu.<\/p>\n<p><img decoding=\"async\" alt=\"CampusInsight: od spremljanja infrastrukture do analize uporabni\u0161ke izku\u0161nje\" src=\"\/wp-content\/uploads\/2019\/05\/d8dad206cb0f11870202a18f1d5ee070.png\" style=\"display:block;margin: 0 auto;\" \/><br \/>\n<br \/>\n<b>Peti in zadnji korak<\/b> \u2014 shranjevanje podatkov v kolonski distribuirani bazi podatkov Druid za kasnej\u0161o uporabo.<\/p>\n<p>Analiza zbranih informacij ob upo\u0161tevanju na osnovi zgodovinskih podatkov zgrajene \"referen\u010dne linije\" omogo\u010da prepoznavanje tipi\u010dnih \"vzorcev neuspehov\" \u2014 opredelitev KPI-jev, ki ustrezajo problemati\u010dnim situacijam, in lokalizacijo te\u017eav s predlogi za njihovo re\u0161itev. Tako orodje zajame pribli\u017eno 85% vseh omre\u017enih te\u017eav.\u00a0<\/p>\n<p><img decoding=\"async\" alt=\"CampusInsight: od spremljanja infrastrukture do analize uporabni\u0161ke izku\u0161nje\" src=\"\/wp-content\/uploads\/2019\/05\/6018323eae44ae5212374399f27c00e3.png\" style=\"display:block;margin: 0 auto;\" \/><br \/>\n<br \/>\nSistemski administratorji podatke prikazujejo v grafi\u010dni obliki v skladu z hierarhijo ali topologijo prostora (na primer, na\u010drtom pisarne). Mogo\u010de je ustvariti \"toplotne karte\", analizirati, koliko je oprema dolo\u010denih platform ali proizvajalcev prizadeta itd. Tako je la\u017eje razumeti, kaj je povzro\u010dilo te\u017eavo.<\/p>\n<p><img decoding=\"async\" alt=\"CampusInsight: od spremljanja infrastrukture do analize uporabni\u0161ke izku\u0161nje\" src=\"\/wp-content\/uploads\/2019\/05\/a05d9c2d1b238984616beddc5a80edf7.png\" style=\"display:block;margin: 0 auto;\" \/><br \/>\n<br \/>\nNa splo\u0161no CampusInsight ponuja precej veliko orodij, ki omogo\u010dajo klasifikacijo te\u017eav, primerjavo prizadetih uporabnikov, raziskovanje podatkov o delovanju dolo\u010denega naro\u010dnika in celo \"predvajanje\" dogodkov, ki so predhodili incidentu, da hitro odkrijemo vir. Pri tem izdelek podpira tudi nov Wi-Fi 6, da ne omenjamo njegovih predhodnikov.<\/p>\n<h2>\u0160tudije primerov<\/h2>\n<p>\nCampusInsight je \u017ee preizku\u0161en v praksi, \u010deprav je ve\u010dina primerov sklenjena pod NDA. Najbolj prikazan odprt primer \u2014 uporaba orodja za spremljanje v lastnem brez\u017ei\u010dnem omre\u017eju Huawei.<\/p>\n<p>Omre\u017eje zajema podjetja, kjer dela okoli 180.000 ljudi, od katerih se 80.000 nana\u0161a na R&amp;D oddelek (to so pisarne v ve\u010d kot 170 dr\u017eavah, kjer je skupno name\u0161\u010denih 62.000 dostopnih to\u010dk).<\/p>\n<p>Implementacija CampusInsight je pomagala optimizirati ve\u010d kot 630 dostopnih to\u010dk, hkrati pa pove\u010dati u\u010dinkovitost re\u0161evanja incidentov za 30%.<br \/>\nSpodaj je nekaj konkretnih situacij.<\/p>\n<h3>Primer 1. Skupinski izpad<\/h3>\n<p>\nVisok nivo te\u017eav, ki jih opa\u017eamo pri velikem \u0161tevilu uporabnikov, je pogosto posledica nizkofrekven\u010dnih napak. In tak\u0161ne te\u017eave ni enostavno odkriti. 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\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u0435\u043b\u0435\u0439 (\u0440\u0435\u0430\u043b\u044c\u043d\u044b\u0439 \u0441\u043b\u0443\u0447\u0430\u0439: \u043f\u043e\u0434\u043a\u043b\u044e\u0447\u0435\u043d\u0438\u0435 \u043a \u0442\u043e\u0447\u043a\u0435 \u0434\u043e\u0441\u0442\u0443\u043f\u0430 \u0432 \u043a\u043e\u043d\u0444\u0435\u0440\u0435\u043d\u0446-\u0437\u0430\u043b\u0435, \u043a\u043e\u0433\u0434\u0430 \u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u0435\u043b\u044c \u043f\u0440\u043e\u0441\u0442\u043e \u043f\u0440\u043e\u0445\u043e\u0434\u0438\u0442 \u043c\u0438\u043c\u043e).<\/p>\n<blockquote><p>\u0414\u043b\u044f \u0440\u0435\u0448\u0435\u043d\u0438\u044f \u043f\u0440\u043e\u0431\u043b\u0435\u043c\u044b \u0438\u043d\u043e\u0433\u0434\u0430 \u0434\u043e\u0441\u0442\u0430\u0442\u043e\u0447\u043d\u043e \u0441\u043d\u0438\u0437\u0438\u0442\u044c \u043c\u043e\u0449\u043d\u043e\u0441\u0442\u044c \u0441\u0438\u0433\u043d\u0430\u043b\u0430 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\u043f\u0440\u0438\u043d\u0435\u0441\u0435\u0442 \u0432 \u043c\u0430\u0441\u0448\u0442\u0430\u0431\u043d\u044b\u0445 \u0432\u043d\u0435\u0434\u0440\u0435\u043d\u0438\u044f\u0445, \u043d\u043e \u0441\u0435\u0439\u0447\u0430\u0441 \u0434\u043e\u0441\u0442\u0443\u043f\u043d\u0430 \u0438 \u043e\u0431\u043b\u0430\u0447\u043d\u0430\u044f \u043f\u043e\u0434\u043f\u0438\u0441\u043a\u0430 \u043d\u0430 \u0441\u0435\u0440\u0432\u0438\u0441 \u0438\u0437 \u043b\u043e\u043a\u0430\u043b\u044c\u043d\u043e\u0433\u043e Public Cloud Huawei, \u0440\u0430\u0441\u0441\u0447\u0438\u0442\u0430\u043d\u043d\u0430\u044f \u043d\u0430 \u0432\u043d\u0435\u0434\u0440\u0435\u043d\u0438\u044f \u0432 SMB \u0441\u0435\u043a\u0442\u043e\u0440\u0435. \u0412 \u043e\u0431\u0449\u0435\u043c, \u0436\u0435\u043b\u0430\u044e\u0449\u0438\u0435 \u043c\u043e\u0433\u0443\u0442 \u0432\u0441\u0435 \u043f\u043e\u043f\u0440\u043e\u0431\u043e\u0432\u0430\u0442\u044c \u0438 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