Okokufaka Kwesitaki Okugcwele kweJuniper, Ukukhipha Okukhulu

UMHLAHLANDLELA WOMSEBENZISI

Okokufaka Kwesitaki Okugcwele, Okukhiphayo Okuphezulu:

Ungayenza Kanjani I-AI Eningi Kunethiwekhi

Ukusebenzisa amandla esitaki senethiwekhi esigcwele esingcono kakhulu ukuletha ukuzizwisa okuhlukile

 

Ukucabanga kabusha campthina kanye namanethiwekhi egatsha enkathi ye-AI

Ama-CEO emhlabeni jikelele akhiphe iziqondiso zebhizinisi zokutshala ubuhlakani bokufakelwa (AI) ebhizinisini lonke. Bahlose ukuguqula ukusebenza futhi bangene emalini engenayo efihliwe. Futhi abathengisi kuyo yonke imikhakha, okuhlanganisa nenethiwekhi ye-IT, bazimisele ukusebenzisa leli thuba.

Kubaholi bezokuxhumana abaphethe izinto eziyinkimbinkimbi nezimba eqolo campthina kanye nezindawo zamagatsha, kuye kwavela imibuzo ebalulekile:

• Mangaki ama-advantagIngabe i-AI ingaletha ngempela?
• Yikuphi ukubekezelela ubungozi okufanele?
• Iyiphi indlela engcono kakhulu eya phambili yokuthuthukisa okuphumayo?

Njengoba kunezinketho eziningi ezitholakalayo zokusatshalaliswa, izinto ezingokoqobo ezethulwa ukubona kusengaphambili komthengisi, amakhono, nobungcweti kubaluleke kakhulu kunangaphambili. Futhi abathengisi abaphishekela i-AI bahlukene ngezigaba ezimbalwa ezibanzi, okuhlanganisa:

• I-Siloed, abathengisi be-niche abanamakhono ahlukahlukene we-AI abangakwazi ukuletha isitaki esigcwele campthina kanye nokuhlanganiswa kwegatsha
• Abathengisi abanezixazululo ezihlukahlukene ze-Bolt-on AI ezidala inkohliso yokusebenza kahle kwezitaki
• Abathengisi abanezakhiwo zezitaki ezigcwele ezifakazelwe ezidizayinwe kusukela phansi kuya phezulu ukuze basebenzise amandla e-AI aphelele

Funda kabanzi mayelana ne-Juniper's AI-Native kanye nephothifoliyo yesisombululo sesitaki esigcwele samafu.
Funda kabanzi →

Lesi sakamuva simele inguquko ebalulekile ekuxhumekeni kwenethiwekhi:

Ukuhlanganiswa okuqinile phakathi kwezingxenye zenethiwekhi ezingcono kakhulu nezici ezintsha ze-AI-Native kuholela ku-opharetha ongcono nolwazi lwabasebenzisi—okuchaza kabusha ukuthi igama elithi “inqwaba egcwele” lisho ukuthini endaweni yesimanje yokuxhumana.

UJuniper ukholelwa ukuthi amanethiwekhi ezitaki agcwele aphambili namuhla kufanele abe namandla kakhulu futhi akhule ekusekeleni izimfuno zebhizinisi eziguqukayo. Futhi kufanele afake i-AI namandla okuzenzakalela enza kube lula ukuphatha nezindleko ze-slash ngenkathi ethuthukisa futhi evikela ulwazi lwabasebenzisi kusukela ekuqaleni kuya ekugcineni.

Le ebook ihlanganisa indaba eguqukayo. Ihlola indima yedatha kunethiwekhi ye-AI kanye nenani lokuxhumanisa isigaba sebhizinisi, izixazululo ezigcwele. Iphinde ihlolisise ukubaluleka kokufaka idatha yekhwalithi ukuze kuqinisekiswe ukuphuma okuphezulu kwesisombululo se-AI kunethiwekhi ye-IT.

Ake siqale

ukukhishwa okukhulu [igama]

Ukuzuza kokusebenza okuphezulu nokusebenza kahle kakhulu ekusebenzeni kwenethiwekhi, okuphawuleka ngokuletha ukuzizwisa komsebenzisi okukhethekile nokuvikelekile kuwo wonke amanethiwekhi e-LAN nawe-WAN. Lokhu kuhlanganisa isikali soguquko nokuba bukhali, ukusebenzelana okungcono, imisebenzi eyenziwe lula, kanye nokuzuza i-TCO ephansi kakhulu ne-OpEx.

Key takaways

Ngamakhono afana nokuhlaziya okubikezelayo nokugcinwa, i-automation, kanye nokuqapha kwenethiwekhi okukhaliphile, i-AI iqhamuke njengamandla okuguqula amanethiwekhi. Ngo campthina kanye nezindawo zegatsha ezisabalalisiwe, indlela efanele “yesitaki esigcwele” ingaqhubeka nokunciphisa ubunkimbinkimbi nezindleko.

1. Isitaki esigcwele sangempela singaphezu “kwezakhiwo”
Isu lesimanje lisebenzisa i-Hardware nesofthiwe indlela ehlanganisiwe (okuhlanganisa ne-AI), esekelwe ukwakheka kwe-API evulekile engu-100% ukuze kuqondiswe ukusebenza nokuthuthukisa ukuzizwisa.

2. I-AI ekuxhumekeni kwenethiwekhi inomthelela omkhulu, ubungozi obuphansi
I-AI kunethiwekhi igqama ngekhono layo lokuletha imithelela esheshayo, engaguquki, nebalulekile kubasebenzisi naku-IT.

3. Okuphuma phambili kokuzalanisa, okokufaka kwesitaki esigcwele kukhulisa okukhiphayo
Ukuqoqa nokusebenzisa okokufaka okuvela ku-LAN, WAN, ezokuphepha, nangale kwe-AI kunikeza amathuba angakaze abonwe

4. Ukubona izinto kusengaphambili nokuvuthwa kuyindaba
Kubalulekile ukusebenzisa ama-algorithms esayensi yedatha ekhulile futhi efunda ngokuqhubekayo kumasethi edatha ahlelwe kahle.

5. Inhlangano yazisa i-orchestration eqhubekayo
Ngaphandle kwezendlalelo zobuchwepheshe, ukuhlela okufanele kanye ne-orchestration ngaphakathi kwamaqembu abathengisi kubalulekile.

6. I-AI-Native isitaki esigcwele sisebenza kahle kakhulu
I-Juniper inikeza imboni kuphela isisombululo se-AI-Native kanye ne-cloudnative esigcwele esingaguqula amathuba okuxhumana.

Izithiyo ezinkulu empumelelweni ye-NetOps zihlanganisa i-shortagabasebenzi abanamakhono, amathuluzi okuphatha amaningi kakhulu, ikhwalithi yedatha yenethiwekhi empofu, nokuntuleka kokubonakala kwesizinda, ngokocwaningo lwe-EMA

Cishe u-25% wamaqembu okusebenza kwenethiwekhi asasebenzisa phakathi kwamathuluzi ayi-11-25 okuqapha, ukuphatha, nokuxazulula izinkinga.

U-30% wezinkinga zenethiwekhi kungenxa yamaphutha okwenziwa ngesandla

Isithembiso esingenakuphikiswa se-AI ekuxhumekeni kwenethiwekhi

Namuhla campthina kanye namanethiwekhi egatsha asebenza njengamasistimu wokujikeleza kanye nezinzwa zebhizinisi.
Bahambisa ukuhamba okubalulekile kwedatha futhi banike amandla izimpendulo ezisheshayo, ezihlakaniphile.
Ukuxhumana kwenethiwekhi ngakunye kunamandla okushayela ukukhiqiza nokuqamba okusha.
Nokho ukugcina lokhu kuxhumene web akukaze kube inselele.

Amaqembu e-IT abhekene nezidingo zebhizinisi ezithuthuka ngokushesha. Babhekene nobunzima bokuvikela izindawo zokuhlasela ezihlala zikhula ezinsongweni eziyinkimbinkimbi. Futhi kufanele babhekane nokuhlasela kwamadivayisi amasha, izinhlobo zokuxhuma, nokwanda kwezinhlelo zokusebenza ezishayela izidingo zomkhawulokudonsa.

Ukulinganisa isidingo sokuqhathanisa nezinsizakusebenza kanye nemikhawulo yesabelomali kanye nokushoda kwamakhono akhethekile kuhlanganisa inkimbinkimbi kuphela.

Kulesi simo, i-AI iqhamuke njengamandla aguqulayo ngempela ekuxhumaneni kwenethiwekhi. Eqinisweni, izixazululo ezithuthuke kakhulu zenethiwekhi ye-AI sezivele zincipha kakhulu futhi, kwezinye izimo, ngisho nokuqeda amaphuzu amaningi obuhlungu bomhlaba wangempela. Exampzihlanganisa:

• Izibalo eziqagelayo nokulungiswa: Amathuluzi okuphatha inethiwekhi anikwe amandla yi-AI angahlaziya idatha yesikhathi sangempela futhi abikezele izinkinga ezingaba khona ngaphambi kokuba zenzeke. Lokhu kunika amandla ukulungiswa okusebenzayo futhi kunciphisa isikhathi sokuphumula. Kubandakanya ukuhlonza izinsongo zokuphepha ezingaba khona, ukuthola okudidayo, nokuthuthukisa ukusebenza kwenethiwekhi.
• I-Automation kanye ne-orchestration: I-automation ethuthukisiwe ye-AI yenza amanethiwekhi akwazi ukuziphilisa, ukuzilungiselela, nokuzilungiselela wona. Konke kuholela ekunciphiseni ukungenelela okwenziwa ngesandla kanye nokwandisa ukusebenza kahle kukonke ngenkathi kuphakamisa ulwazi lwabasebenzisi kanye nabasebenzisi. Amathuluzi e-orchestration anamandla e-AI angakwazi futhi ukwenza izinqubo eziyinkimbinkimbi, ezifana nokuhlinzekwa kwenethiwekhi noshintsho lokuphatha.
• Ukuqapha kwenethiwekhi okuhlakaniphile kanye nemininingwane: Amathuluzi okuqapha anikwe amandla yi-AI ahlinzeka ngokubonakala kwesikhathi sangempela ekusebenzeni kwenethiwekhi futhi anganikeza imininingwane ebambekayo futhi anike amandla ukuthathwa kwezinqumo okuqhutshwa idatha.

Izibalo eziqhutshwa yi-AI zingakwazi ukuhlonza amathrendi, zihlonze amaphethini, futhi zinikeze izincomo zokuthuthukisa, ukuphepha, nokuhlelwa kwamandla.

Nakuba lezi zinhlobo zamakhono zikhona namuhla, zihlukile futhi aziyona inkambiso. Izixazululo eziningi azinakho ukuhlanganiswa kanye nedatha edingekayo ukuze kuguqulwe kakhulu ukusebenza kwansuku zonke.

“Uma ufuna ukwenza i-tier 2/tier 3 ngokuzenzakalelayo lapho ungena khona esitakini senethiwekhi bese uzama ukuthola ukuthi inkinga [yenethiwekhi] ikuphi nokuthi ungayilungisa kanjani—inhloso enkulu evamile, amapulatifomu e-AIOps esizindalwazi awakwenzi lokho. yenza lokho; ababona ochwepheshe besizinda.”

UShamus McGillicuddy, iPhini likaMongameli Wezocwaningo, i-EMA

04. Indaba yokufaka

Okukhiphayo okuphezulu kuqala ngokufaka idatha efanele

Uma kuziwa ekukhipheni inani eligcwele ku-AI nokufunda komshini (ML) kunethiwekhi, umthamo, ukufinyelela, ikhwalithi, isikhathi, nokucubungula— kanye nezinsiza zokuhlaziya nokwenza idatha—kubalulekile. Phela, izenzo ezisebenzayo ezinikwe amandla i-AI zincike ekuqondeni okuphelele kwesimo samanje.

Ukwazi kahle ukuthi kwenzekani, ukuthi kwenzekani, nokuthi kungani kwenzeka kubalulekile ekwaziseni izimpendulo ezifika ngesikhathi nezifanele. Futhi idatha yekhwalithi iyisisekelo sayo yonke into.

Njengoba nje inqubo yokudala iwayini eliyingqayizivele incike ezintweni ezihlukahlukene, ukukhiqizwa kwedatha yekhwalithi ye-AI ekusebenzeni kwenetha kwenza kanjalo. Ngokufanayo nendlela iwayini elidinga ngayo amagilebhisi, inhlabathi, nesikhathi sokuguga, ubuchwepheshe bokuxhumana, ukusebenza kanzima, nokubekezela kubalulekile ekukhuliseni amasethi edatha ahlukahlukene ngolwazi olubhalwe kahle futhi olukhethwe ngokucophelela.

Noma ubani angaqoqa idatha eyisisekelo empilweni yenethiwekhi futhi ayifake enjini ye-AI. Kodwa-ke, ukukhuthaza i-AI enomthelela wangempela ekwazi ukunika amandla ulwazi olukhethekile lomsebenzisi kanye nokunciphisa imibono engamanga kuhilela ukucatshangelwa okuningi. Ukuze kuzuzwe lezi zinhloso, abathengisi kufanele bacabangele yonke into kusukela esakhiweni senhlangano kuya ekuthuthukisweni kwehadiwe/isofthiwe, i-spectrum yedatha, namasethi amathuluzi. Ngaphezu kwalokho, kubalulekile ukusebenzisa ama-algorithms esayensi yedatha asebekhulile futhi ngokuqhubekayo kumasethi edatha akhethwe kahle.
Ngaphezu kwalokho, ukukhulisa okukhiphayo okuvela ku-AI ekuxhumekeni kuncike enanini nobubanzi bokufakwayo kwedatha. Futhi yilapho kanye izixazululo eziningi zenethiwekhi ye-AI zilinganiselwe. Okwamanje, ezinye izixazululo zenethiwekhi ye-IT zingaqoqa idatha ku-LAN, ezinye ku-WAN. Kodwa zimbalwa izixazululo ezingahlanganisa futhi zisebenzise idatha evela kukho kokubili i-LAN ne-WAN (nangaphezulu) ngempumelelo—esikubiza ngokuthi “isitaki esigcwele.” Lokhu kugcizelela isidingo esibalulekile sokubona kusengaphambili komthengisi ekuqinisekiseni ukuhlanganiswa nokusebenzisana.

Indima yokufaka uma iqhathaniswa nokuphumayo yokuthuthukiswa kwenethiwekhi ye-AI

I-LAN enhle noma i-WAN	I-LAN engcono ne-WAN	I-LAN ephezulu, i-WAN, ukuphepha, indawo, nokuningi okunekhono le-AI-Native
Inikeza okuhlukanisiwe view yokusebenza kwenethiwekhi nokuphepha	Iqala ukunikeza okuphelele okwengeziwe view yokusebenza kwenethiwekhi, okwenza amasistimu e-AI enze izinqumo ezinolwazi	Iletha isethi yedatha ebanzi futhi inikeza i-panoramic view lokho kwenza amasistimu e-AI akwazi ukufeza amandla awo aphelele
Isifinyezo sezinzuzo: Ububanzi obunomkhawulo bukhawulela izinzuzo ezingaba khona, ukukhiqiza izithuthukisi eziyisisekelo ekusebenzeni kahle nokutholwa kosongo	Isifinyezo sezinzuzo: Isekela ukuthuthukiswa okumaphakathi ekuphathweni kwenethiwekhi, ukunciphisa isikhathi sokuphumula nokuhlonza inkinga eyinkimbinkimbi	Isifinyezo sezinzuzo: • Inika amandla i-AI ukuze ithuthukise ngokuqhubekayo ukusebenza kwenethiwekhi • Ithuthukisa ukuvikeleka ngokuhlaziywa kokusongela okubikezelwayo • Iletha okuhlangenwe nakho komsebenzisi komuntu siqu

Ukudlulela ngale kwamamodeli enethiwekhi we-AI wendabuko kanye nesafufusa abathengisi abaningi, indlela yesitaki egcwele ye-Juniper ye-AI-Native imele umngcele olandelayo ekusungulweni kwenethiwekhi.

05. Ukwenza ngcono okuphumayo

Indlela isitaki esigcwele se-AI-Native sithuthukela ngayo amanethiwekhi

Kuze kube manje, sesithole ukuthi kungani idatha yekhwalithi iwumthombo wezempilo we-AI nokuthi kungani ukuphuma okuphezulu ekuxhumekeni kwenethiwekhi kuthatha idatha yekhwalithi evela kunethiwekhi yonkana. Umbuzo omkhulu olandelayo uthi: Iyiphi indlela engcono kakhulu yokuthola nokusebenzisa idatha yekhwalithi kuwo wonke amaleveli ukuze uthuthukise imiphumela yenethiwekhi?

Isu elingcono kakhulu lisebenzisa indlela ebumbene ngezingxenyekazi zekhompuyutha ezihamba phambili embonini nezitaki zesofthiwe—isitaki esigcwele—ukuthuthukisa ukusebenza kahle, ukwenza lula ukusebenza, nokuthuthukisa ulwazi lomsebenzisi nokuphepha. Isekelwa ifu le-microservices kanye ne-API evulekile engu-100% yokwakheka ukuze inwebeke kwezinye izixazululo eziholayo kuzo zonke izizinda, njenge-5G, ITSM, izinkundla zokuxhumana, i-cybersecurity, kanye nokuhamba.

I-Juniper iguqula ukuqoqwa kwedatha yenethiwekhi yendabuko ngokuphatha amadivaysi enethiwekhi njengezinzwa, ithatha idatha ebanzi yobubanzi kusuka kuyo yonke i-LAN ne-WAN, kanye nokuhlanganisa ukuphepha nokokufaka okusekelwe endaweni. Okwesiboneloample, izici ezibalulekile zendlela yethu yokwenza zihlanganisa (bheka ikhasi 12 ukuze uthole isithombe esikhulu):

• I-telemetry ethuthukisiwe yokuphela-siya-ekupheleni: Ilinganisa i-150+ yesikhathi sangempela somsebenzisi ongenazintambo ngokusakaza i-telemetry kusuka kumarutha, amaswishi, nama-firewall, athuthukiswe yi-Mist AI™ ukuze uthole ukuhlaziya okubikezelayo.
• I-Cloud-native, i-microservices architecture: Isekela ukucutshungulwa kwesikhathi sangempela kwedatha ye-AI futhi inike amandla ukusebenza okunamandla, okuqinile, nokusebenza kahle kwezinhlelo zokuphatha inethiwekhi.
• Injini ye-AI evamile: Ukuhlanganisa ukuhlaziya idatha yenethiwekhi nezinqubo zokwenza izinqumo ngaphansi kohlaka olulodwa, oluhlakaniphile oluxhaswe yi-Mist AI olusiza ukusebenza okuhlelekile, ukuxazulula izinkinga ezibikezelayo, nokufunda okuguquguqukayo kuyo yonke inethiwekhi ye-ecosystem.

Ngokufunda okuqhubekayo komsebenzisi okusekelwe kudatha ye-telemetry enemininingwane, iJuniper ihlanganisa idatha yohlelo lokusebenza eceleni kwedatha yenethiwekhi. Lokhu kuvumela isistimu ye-AI ukuthi ifunde mayelana nezinhlelo zokusebenza ezisetshenziswayo futhi ibikezele imithelela engaba khona kulwazi lomsebenzisi lohlelo lokusebenza ngokusekelwe kuzimo zenethiwekhi ezimbi.

Ukwengeza, umsizi wethu wokuqala we-AI-Native Virtual Network, i-Marvis™, wenza ukuphatha nokuxazulula izinkinga kube lula. I-Marvis ifaka isixhumi esibonakalayo sengxoxo sokuxazulula izinkinga ngendlela elula kanye nohlaka lwesenzo oluzenzakalelayo, oluqhuba ukuthuthukiswa kwenethiwekhi okuqhubekayo. I-Marvis iphinde ibe ne-Marvis Minis, iwele lokuqala lokuhlangenwe nakho kwedijithali embonini. Ama-Minis ahlonza ngokuqhubekayo izinkinga zokuxhuma ngaphambi kokuthi zenzeke, avikele nakakhulu abasebenzisi kulwazi lwenethiwekhi olukhungathekisayo.

Kakhulu campthina kanye nezindawo zegatsha ezisabalalisiwe, le nhlanganisela yamakhono iyashintsha umdlalo. Isusa ngempumelelo ukukhishwa, ukuxazulula izinkinga, kanye nezinselele zokulungisa ezikhuphula izindleko, ukwelula amaqembu e-IT kuze kufike emikhawulweni yawo, kucekele phansi okuhlangenwe nakho kwabasebenzisi, futhi kuvimbele ukuscalability nokusebenza kahle. Ngokuhlangene, bahlanganisa uguquko lwangempela endleleni yokuxhumana yebhizinisi ezoqhubeka nokuba ngcono ngokuhamba kwesikhathi.

Ukubona isithombe esikhulu

Isisekelo senethiwekhi yesimanje yesitaki esigcwele sibalulekile kumvelo yayo eguquguqukayo futhi sivumele ukuhlanganiswa okungenamthungo ezizindeni ezintsha zokuxhumana—nangaphezulu. Ukwenyuka kokuvumelana nezimo kuzoba inkomba yenkathi entsha kunethiwekhi ye-IT, ukuphazamisa amamodeli we-TCO wendabuko wobuchwepheshe obusunguliwe kanye nokuguqula ulwazi lwenethiwekhi kubo bobabili opharetha nabasebenzisi. Nawa ama-ex ambalwa akhethiweampokuncane kwamakhono abonisa ukuthi iJuniper icabanga kabusha kanjani ukusebenza kwesitaki esigcwele:

UMTHETHO 1
Usekelo lwe-AI-Native luqhubeka luba ngcono ngokuhamba kwesikhathi: iphesenti lamathikithi enethiwekhi yekhasimende ye-IT axazululwe ngokuqhubekayo nge-AI phakathi neminyaka embalwa.

Amasevisi wendawo ahlanganisiwe

Amaphoyinti okufinyelela okungenantambo (ama-AP) asebenzisa izinto ezingu-16 ze-antenna ye-Bluetooth® ekubekweni/ukuma kwe-AP okuzenzakalelayo kanye nokubonakala kwempahla okunembile kanye ne-vBLE yamasevisi endawo anembe nakalekayo angakhuphula ukusebenzisana komsebenzisi futhi athuthukise ukugeleza komsebenzi kuzo zonke izimboni.

I-SD-WAN esebenza kahle kakhulu
I-SD-WAN engenawo umhubhe, eseshini esekwe kuseshini isebenzisa i-Session Smart Networking ukuze kuthuthukiswe ukusetshenziswa komkhawulokudonsa kanye nokuhluleka okusheshayo okusekelwe ezimeni zenethiwekhi zesikhathi sangempela.

I-AI-Native Edge evikelekile
Ezokuphepha, i-WAN, i-LAN, kanye ne-NAC (Network Access Control) kuphothali yokusebenza eyodwa, enikeza ukuvikeleka okuphezulu kwezinsongo nge-wire-speed, kanye nesinyathelo esibalulekile sokuqhubekela phambili kwe-AI-Native uZTNA kanye

Izakhiwo ezisekelwe ku-SASE
Ukuhlanganiswa kwesikhungo sedatha okungenazihibe
I-Industry-first Virtual Network Assistant (VNA) ihlinzeka ngokubonakala ekupheleni kuya ekupheleni kanye nesiqiniseko kuzo zonke izizinda zebhizinisi, kusukela c.ampthina kanye negatsha esikhungweni sedatha

Ukuqinisekiswa Komzila Okuthuthukisiwe
I-AI-Native automation kanye nemininingwane ye-topology yendabuko yomzila onqenqemeni

I-Wi-Fi 6E ehamba phambili ne-Wi-Fi 7 hardware
Ama-AP adizayinelwe ukwenza imisebenzi yenethiwekhi ibe lula kuyilapho kukhuliswa isikali nokuba bukhali. Amaswishi anamandla aphezulu e-Wi-Fi 7 anamandla amaphakathi asebenzayo kanye nokuphathwa kwedatha kumasistimu wokwakha

06. Ngale kwezobuchwepheshe

Ngale kobuchwepheshe: ukubaluleka kwesakhiwo senhlangano

Ukuzuza okukhiphayo okuphezulu kusuka endleleni yenethiwekhi yesitaki egcwele akuncikile kuphela kubuchwepheshe obusetshenzisiwe; futhi kuncike kakhulu ekwakhekeni kwenhlangano.
Ukuhleleka ngendlela efanele kanye ne-orchestration kuzo zonke izingqimba zobuchwepheshe ezihlukene nangaphakathi kwamaqembu ngokwawo kubalulekile empumelelweni.
E-Juniper, senze indawo yokusebenzisana lapho amaqembu ethu esayensi yedatha kanye namaqembu asekela amakhasimende asebenza ndawonye. Kuqondaniswe ngokomzimba nangokokusebenza, womabili amaqembu asebenzisa ithuluzi lethu elithuthukisiwe le-AIOps ukuze ahlale ehambisana nezinkinga zesikhathi sangempela zamakhasimende kanye nempendulo.

Lokhu kubambisana kuqinisekisa ukuthi ochwepheshe bethu besayensi yedatha kanye nochwepheshe besizinda bahambisana ngokuqhubekayo nezidingo zamakhasimende eziguqukayo kanye nokubekwa phambili kwezisombululo, okuqhubekisela phambili inqubekelaphambili.

Ngokuhamba kwesikhathi, inkokhelo iba ukwesekwa okuyimbudumbudu ngokwandayo, njengokuhlanganisa amaphuzu edatha kusuka kuzixazululo ezifana ne-Zoom, Amaqembu, i-ServiceNow, i-Cradlepoint, ne-Zebra ukuze ubikezele ngenkuthalo ukusebenza okuzayo kokuxazulula inkinga kuze kufike esicini esithile. Futhi inqubekelaphambili izoqhubeka kuphela.
I-Juniper's AIOps isheshisa ukuthunyelwa, yenza imisebenzi ibe lula, futhi yehlise i-TCO.

Funda ukuthi kanjani.

07. Isitaki esigcwele MANJE

Izixazululo ezihlanganisiwe zeJuniper zincike ekuhlanganiseni kwe-telemetry, ukuhamba komsebenzi okuzenzakalelayo, i-DevOps, ne-ML ukuze kunikwe amandla inethiwekhi eguquguqukayo nengabikezelwa. Indlela yethu ephelele ye-AI ekuxhumaneni nokuxhumana iholele ekutheni kube nenqwaba yezimboni, okuhlanganisa:

• Ukuxhumana okuthembekile kwabafundi, abathengi, iziguli, nabasebenzi
• Nweba futhi uvuselele i-Wi-Fi ngobunono
• Khomba futhi uvikele iselula namadivayisi nge-NAC

Ukufinyelela ngentambo
Ukuxhumana okuthembekile nokuvikelekile kwebhizinisi

• Ukuxhumana okuthembekile kwe-IoT, APs, namadivayisi anentambo
• Xhuma futhi uvikele i-IoT nabasebenzisi nge-microsegmentation
• Khomba futhi uvikele amadivayisi nge-NAC

Amasevisi endawo yasendlini
Letha ukuzizwisa komuntu siqu okususelwe ekuqondeni

• Xhumana nabafundi, abathengi, iziguli, nabasebenzi
• I-GPS yangaphakathi nendawo yempahla
• Izibalo ezisekelwe endaweni

Vikela ukufinyelela kwegatsha
Ukuxhumana okuvikelekile, okuthembekile, nokungenazihibe kwamahhovisi egatsha omhlaba

• Vikela i-SD-WAN/SASE
• Ibhizinisi elisabalalisiwe
• Lungiselela i-WAN yezinhlelo zokusebenza zamafu

07. Isitaki esigcwele MANJE

Izixazululo ezihlanganisiwe zeJuniper zincike ekuhlanganiseni kwe-telemetry, ukuhamba komsebenzi okuzenzakalelayo, i-DevOps, ne-ML ukuze kunikwe amandla inethiwekhi eguquguqukayo nengabikezelwa. Indlela yethu ephelele ye-AI ekuxhumaneni nokuxhumana iholele ekutheni kube nenqwaba yezimboni, okuhlanganisa:

• Ukulungiswa okusebenzayo kwe-AI-Driven RF ukuze uthole ukuzizwisa okungenazintambo okuphelele kuzo zonke izindawo
• Ukuthwebula iphakethe kwe-Dynamic ku-LAN ne-WAN, kuhlinzeka ngokuzenzakalelayo okungenakuqhathaniswa, ukubonakala nokuxazulula inkinga
• Ukuhlaziywa okuzenzakalelayo kwembangela yokuxilonga ukuze kuhlonzwe ngokushesha futhi kuxazululwe izinkinga zenethiwekhi, kwehliswe i-MTTR futhi kuqedwe iningi lamathikithi ezinkinga.
• I-AI-Native Digital Experience Twin ukuze ibone kusengaphambili futhi ibhekane nezinkinga zenethiwekhi enezintambo, ezingenantambo, kanye ne-WAN ngaphambi kokuthi zibe nomthelela kubasebenzisi.

Ngokuvumelana negama layo, i-AI-Native Full Stack yethu nayo idlulela ngale kwe-campthina kanye negatsha futhi siqhubekele phambili ebhizinisini esabalalisiwe. Okwesiboneloample:

• I-AI-Native VNA eshintsha ukusebenza kwesikhungo sedatha ngemininingwane esebenzayo kanye nemibuzo yesisekelo solwazi esenziwe lula ngokusebenzisa isixhumi esibonakalayo sengxoxo ngokuhlangana nohlelo lwenethiwekhi olusekelwe kunhloso (IBN), ethuthukisa isikhathi, kanye nezinqumo ezisheshisayo.
• I-Juniper Mist Routing Assurance ithuthukisa i-AIOps ekusebenzeni okuthuthukile kwe-WAN, ihlinzeka ngokubonakala komzila kanye nemininingwane esebenzayo eyenza ukuxazulula izinkinga kube lula, ukwehlisa i-MTTR/MTTI, kanye nokuhlaziya imbangela ezenzakalelayo emaphethelweni ebhizinisi.
• I-AI-Native Security iqinisekisa ukubonakala nokusetshenziswa kwengqalasizinda efanele evikelekile enokuvikelwa kokusongelwa okungcono kakhulu kwesigaba kuwo wonke ama-switch switch, amarutha, nama-APs kuwo wonke c.ampthina, igatsha, isikhungo sedatha, nezindawo zamafu, okuthuthukisa ukukhiqiza kunethiwekhi yonkana namathimba okusebenza okuvikeleka

Isitaki esigcwele BESE? 

Kuqinile:
I-Marchitecture ithembisa ukusebenza okuphezulu kodwa iyahluleka; izixazululo ezihlanganisiwe

Ukuphatha kanzima:
Idinga izixhumanisi zokuphatha eziningi, ngokuvamile nge-CLI eyinkimbinkimbi

Ukuhlanganiswa okukhawulelwe:
Intula ukuhlanganiswa okungenamthungo kuzo zonke izindawo zenethiwekhi nezixazululo

Isebenza kabusha:
Idinga izimpendulo mathupha ezinkingeni ngemva kokwenzeka

Isitaki esigcwele MANJE

Amandla:
Iklanyelwe ukuhlangabezana nezidingo zebhizinisi zanamuhla nakusasa

Ukuphathwa kwe-AI-Native:
Ukuphathwa okuhlanganisiwe, okwakhiwe nge-AI edidiyelwe kusukela phansi kuya phezulu

Ukuhlanganiswa okuphelele:
Inkundla ehlanganisiwe ene-LAN ehamba phambili, i-WAN, isikhungo sedatha, amasevisi wendawo, ukuphepha, nesakhiwo esivulekile se-API sokuhlanganisa okungenamthungo ne-ServiceNow, Teams/Zoom, Cradlepoint, Zebra, nokunye.

Okusebenzayo:
Iyakwazi ukuhlonza izinkinga nokuzinciphisa ngaphambi kokuthi zibe nomthelela kubasebenzisi

Izinzuzo izifinyezo

Indlela yesitaki egcwele ye-AI-Native iletha ukusebenza ngendlela engakaze ibonwe kuyinkimbinkimbi campthina kanye nezindawo zegatsha. Nawa abambalwa bomhlaba wangempelaampLes.

"Isipiliyoni somsebenzisi wenethiwekhi iJuniper esinikezayo sidlula kude noma yini enye emakethe. Ukusebenza kalula kweJuniper namandla okuziphilisa, kanye namamethrikhi esipiliyoni somsebenzisi ewanikezayo, kuvelele. "

Neil Holden, CIO, Halfords

8x ukuvuselela inethiwekhi okusheshayo

I-George Washington University ithuthukisa okuhlangenwe nakho
Inethiwekhi yesimanjemanje, ephethwe ngamafu enezintambo kanye nengenantambo yenza ukuphathwa kwenethiwekhi kube lula nokuxazulula izinkinga, okuholela ekuzizweleni okungcono kakhulu kwe-IT nabasebenzisi.

Ngaphezulu kwe-US $500k yokonga ngonyaka

ILondon Borough yaseBrent ikhulisa ukukhiqiza kwabasebenzi
Inethiwekhi ye-AI-Native inika i-IT ukubonakala okucacile ezindabeni kanye nezilungiso ezinconyiwe, iqondise izinselele zokuphatha eziqhubekayo.

90%+ ukwehliswa kwamathikithi ezinkinga zenethiwekhi

I-Halfords ithembele kuma-AIOps ekuguquleni izitolo
Ngokuphendukela ku-cloud-native, indlela ye-AI-Native, i-Halfords yenze izinselele zokuphatha zaba lula ngenkathi inika amandla izixazululo zokuthenga zezitolo zesizukulwane esilandelayo.

Umhlahlandlela ogcwele wesenzo senethiwekhi yesitaki

Uma kubhekwa ububanzi bokusetshenziswa kanye nokuvela kobuchwepheshe benethiwekhi kuze kube muva nje, ubunkimbinkimbi sebunesikhathi eside bubusa c.ampthina kanye nenethiwekhi yegatsha. Ukwethulwa kwe-AI-Native Networking kushintsha yonke into.

Nakuba inethiwekhi ihlale ikhula noma ishintsha kuyo yonke indawo campthina kanye negatsha lemvelo, indlela ye-AI-Native Full Stack inikeza ithuba elingakaze libonwe lokunqamula inkimbinkimbi engadingekile, njengezilawuli nezinkundla zokuphatha ezihlukene, futhi ziqondaniswe nezixazululo ezingcono kakhulu zokuzalanisa kuyo yonke indawo ye-IT. Ingase futhi inikeze izinga “elilungile” lamakhono e-AI adingekayo ukuze kulethwe okukhiphayo okuphezulu, ukusekela okuhlangenwe nakho kwabasebenzisi abakhethekile kanye ne-IT ku-TCO ephansi kakhulu ne-OpEx.

Futhi njengewayini elimnandi, kuzoba ngcono ngokuhamba kwesikhathi.

01. Thola ithuba le-PoC
Khomba ithuba ku-campthina kanye negatsha ukuze sizibandakanye ku-PoC (isb., isayithi entsha noma ukuthuthukiswa kwezinto ezisetshenziswayo).

02. Qala ngesivivinyo esinobungozi obuncane
Zama i-AI kithi ukuze usebenzise ithrafikhi yokukhiqiza ebukhoma futhi ubone ukuthi izixazululo zethu zihambisana kanjani nenhlangano yakho. Qala noma yikuphi kusitaki esigcwele nganoma iyiphi inhlanganisela ye-Wi-Fi, ukushintsha, kanye/noma izixazululo ze-SD-WAN.

03. Yizwa umehluko
Bona ukuthi indlela ye-AI-Native ikuletha kanjani ukulula okukhulu, ukukhiqiza, nokuthembeka.

04. Nweba ukusetshenziswa kwakho
Nweba ukufinyelela kwakho ngokuhlanganisa izindawo ezengeziwe ezifana campthina, izindawo zamagatsha, i-NAC, izikhungo zedatha, i-firewalling, kanye ne-Enterprise Edge.

Izinyathelo ezilandelayo

Hlola isitaki esigcwele seJuniper
Ngena ujule emathubeni esitaki agcwele kanye nezixazululo ze-campthina kanye negatsha.
Hlola izixazululo zethu →
I-AI kithi →

Bona i-Mist AI isebenza
Bona ukuthi ifu lesimanje le-microservices kuJuniper Mist AI likuletha kanjani ukubonakala kwangempela, ukuzenzekelayo, kanye nesiqiniseko.
Buka idemo yethu efunwa kakhulu →

 

Kungani Juniper
IJuniper Networks ikholelwa ukuthi ukuxhumana akufani nokuthola ukuxhumana okuhle. I-Juniper's AI-Native Networking Platform yakhiwe kusukela phansi ukuze isebenzise i-AI ukuze ilethe okuhlangenwe nakho komsebenzisi okukhethekile, okuvikeleke kakhulu, nokusimeme ukusuka emaphethelweni kuya esikhungweni sedatha kanye nefu. Ungathola imininingwane eyengeziwe ku-juniper.net noma uxhumane noJuniper ku
X (owayekade eyi-Twitter), i-LinkedIn, ne-Facebook.

Ulwazi olwengeziwe
Ukuze ufunde kabanzi mayelana nesisombululo se-Juniper Networks AI-Native Networking Full Stack, xhumana nommeleli wakho weJuniper noma umlingani wakho, noma vakashela webisayithi: https://www.juniper.net/us/en/campus-and-branch.html

Amanothi nezinkomba
01. I-Network Management Megatrends 2024:
Izikhala Zamakhono, I-Hybrid kanye ne-Multi-Cloud, i-SASE, kanye nokusebenza okuqhutshwa yi-AI. I-EMA idingeka kakhulu webngaphakathi
02. Ibid.
03. Ibid.
04. I-NetOps Expert podcast, isiqephu sesi-9: “I-AI/ ML ne-NetOps—Ingxoxo ne-EMA ngochwepheshe be-NetOps,” Julayi 2024.

© Copyright Juniper Networks Inc. 2024.

Wonke Amalungelo Agodliwe.

Inkampani Juniper Networks Inc.
1133 Innovation Way
Sunnyvale, CA 94089
7400201-001-EN Okthoba 2024
IJuniper Networks Inc., ilogo yeJuniper Networks, umjunipha.
net, Marvis, kanye ne-Mist AI yizimpawu zokuthengisa ezibhalisiwe zeJuniper Networks Incorporated, ezibhaliswe e-US nasezifundeni eziningi emhlabeni jikelele. Amanye amagama omkhiqizo noma wesevisi angaba yizimpawu zokuthengisa zeJuniper Networks noma ezinye izinkampani. Lo mbhalo ungowamanje kusukela ngosuku lokuqala lokushicilelwa futhi ungashintshwa yiJuniper Networks nganoma yisiphi isikhathi. Akuyona yonke iminikelo etholakala kuwo wonke amazwe lapho iJuniper Networks isebenza khona.

Imininingwane

• Igama Lomkhiqizo: Isixazululo Esigcwele Sokuxhumanisa Isitaki
• Umkhiqizi: Ijunipha
• Izici: Iphothifoliyo yesisombululo sesitaki esigcwele se-AI-Native kanye ne-cloud-native
• Izinzuzo: Amanethiwekhi ashukumisayo kakhulu futhi asalekayo, i-AI namandla okuzenzela, ukuphatha okwenziwe lula, ulwazi oluthuthukisiwe lwabasebenzisi

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Imibuzo Evame Ukubuzwa (FAQ)

Yiziphi izinzuzo ezibalulekile ze-Full Stack Networking Solution?

Isixazululo sinikeza amanethiwekhi ashukumisayo kakhulu futhi angakala, i-AI namandla okuzenzakalelayo, ukuphathwa okwenziwe lula, ulwazi oluthuthukisiwe lwabasebenzisi, kanye nezindleko ezincishisiwe.

Kubaluleke kangakanani ukufaka idatha ekwandiseni umphumela wezixazululo ze-AI?

Ukufakwa kwedatha kudlala indima ebalulekile ekuqinisekiseni ukusebenza kahle kwezixazululo ze-AI kunethiwekhi ye-IT. Okufakwayo kwedatha okuyikhwalithi kuholela emiphumeleni engcono.

Amadokhumenti / Izinsiza

Okokufaka Kwesitaki Okugcwele kweJuniper, Ukukhipha Okukhulu [pdf] Umhlahlandlela Womsebenzisi Okokufaka Kwesitaki Okuphelele Okuphumayo Okukhulu, Okokufaka Kwesitaki Ubukhulu Bokukhiphayo, Okokufaka Okuphezulu Okuphumayo, Okukhiphayo Okuphezulu, Okukhiphayo

Izithenjwa

• Imaniwali yosebenzisayo