{"id":34642,"date":"2025-11-20T04:00:50","date_gmt":"2025-11-20T01:00:50","guid":{"rendered":"https:\/\/fatihsoysal.com\/blog\/kubernetes-dengeleme-qos-oncelik-ve-puanlama-ile-nasil-calisir\/"},"modified":"2025-11-20T04:00:50","modified_gmt":"2025-11-20T01:00:50","slug":"kubernetes-dengeleme-qos-oncelik-ve-puanlama-ile-nasil-calisir","status":"publish","type":"post","link":"https:\/\/fatihsoysal.com\/blog\/kubernetes-dengeleme-qos-oncelik-ve-puanlama-ile-nasil-calisir\/","title":{"rendered":"Kubernetes Dengeleme: QoS, \u00d6ncelik ve Puanlama ile Nas\u0131l \u00c7al\u0131\u015f\u0131r?"},"content":{"rendered":"

\n Modern uygulamalar\u0131n karma\u015f\u0131kl\u0131\u011f\u0131 artt\u0131k\u00e7a, Kubernetes k\u00fcmelerindeki kaynak y\u00f6netimi sadece Pod’lar\u0131 \u00e7al\u0131\u015ft\u0131rman\u0131n \u00f6tesine ge\u00e7iyor. Peki, k\u00fcmelerinizdeki kritik i\u015f y\u00fcklerinin her zaman yeterli kayna\u011fa sahip oldu\u011fundan ve kararl\u0131 bir \u015fekilde \u00e7al\u0131\u015ft\u0131\u011f\u0131ndan nas\u0131l emin olabilirsiniz? Bu makalede, Kubernetes’in temel scheduling mekanizmalar\u0131n\u0131n \u00f6tesine ge\u00e7erek, Kalite Servisi (QoS), \u00d6ncelik (Priority) ve Puanlama (Scoring) kavramlar\u0131n\u0131 nas\u0131l kullanarak k\u00fcmenizi dengede tuttu\u011funu, performans\u0131 optimize etti\u011fini ve kesintileri minimize etti\u011fini derinlemesine inceleyece\u011fiz.\n<\/p>\n

\n Kubernetes, container tabanl\u0131 uygulamalar\u0131 y\u00f6netmek i\u00e7in tasarlanm\u0131\u015f g\u00fc\u00e7l\u00fc bir platformdur. Ancak, milyonlarca kullan\u0131c\u0131ya hizmet veren bir e-ticaret uygulamas\u0131ndan tutun da, arka planda \u00e7al\u0131\u015fan veri analizi i\u015flerine kadar farkl\u0131 \u00f6ncelik ve kaynak gereksinimlerine sahip Pod’lar\u0131 ayn\u0131 anda, ayn\u0131 k\u00fcme \u00fczerinde \u00e7al\u0131\u015ft\u0131rmak, yaln\u0131zca Pod’lar\u0131 uygun bir Node’a yerle\u015ftirmekten \u00e7ok daha fazlas\u0131n\u0131 gerektirir. Basit bir Pod planlamas\u0131, kaynak \u00e7at\u0131\u015fmalar\u0131, kritik hizmetlerde performans d\u00fc\u015f\u00fc\u015fleri ve hatta k\u00fcme kararl\u0131l\u0131\u011f\u0131nda ciddi sorunlara yol a\u00e7abilir. Bu nedenle, Kubernetes geli\u015ftiricileri, k\u00fcme sa\u011fl\u0131\u011f\u0131n\u0131 ve uygulama performans\u0131n\u0131 korumak i\u00e7in daha sofistike mekanizmalar tasarlam\u0131\u015ft\u0131r.\n<\/p>\n

\n \u00d6rne\u011fin, bir e-ticaret sitesinin \u00f6deme sisteminin \u00e7al\u0131\u015ft\u0131\u011f\u0131 Pod’lar, ayn\u0131 k\u00fcmede arka planda \u00e7al\u0131\u015fan stok g\u00fcncelleme Pod’lar\u0131 veya bir geli\u015ftiricinin test Pod’lar\u0131 ile ayn\u0131 \u00f6nem d\u00fczeyine sahip olmamal\u0131d\u0131r. \u00d6deme sistemi, anl\u0131k ve y\u00fcksek performansla \u00e7al\u0131\u015fmak zorundayken, stok g\u00fcncellemesi daha d\u00fc\u015f\u00fck \u00f6ncelikli olabilir ve belirli gecikmeler tolere edilebilir. \u0130\u015fte bu noktada, Kubernetes’in geleneksel planlama mant\u0131\u011f\u0131n\u0131n \u00f6tesine ge\u00e7erek devreye soktu\u011fu Kalite Servisi (QoS), Pod \u00d6nceli\u011fi (Priority) ve Scheduler Puanlama (Scoring) mekanizmalar\u0131 hayati bir rol oynar. Bu mekanizmalar, k\u00fcme y\u00f6neticilerinin, kaynaklar\u0131 daha ak\u0131ll\u0131ca tahsis etmesine, Pod’lar\u0131n ya\u015fam d\u00f6ng\u00fcs\u00fcn\u00fc daha iyi y\u00f6netmesine ve sonu\u00e7 olarak daha kararl\u0131, g\u00fcvenilir ve verimli bir altyap\u0131 sunmas\u0131na olanak tan\u0131r. K\u0131sacas\u0131, Kubernetes, yaln\u0131zca Pod’lar\u0131 bir yere koymakla kalmaz, ayn\u0131 zamanda onlar\u0131n “ne kadar iyi” \u00e7al\u0131\u015faca\u011f\u0131n\u0131 da y\u00f6netir. Bu ileri d\u00fczey kontrol mekanizmalar\u0131 sayesinde, kritik uygulamalar\u0131n\u0131z\u0131n her ko\u015fulda beklendi\u011fi gibi \u00e7al\u0131\u015ft\u0131\u011f\u0131ndan emin olabilirsiniz.\n<\/p>\n

Kubernetes Kalite Servisi (QoS) S\u0131n\u0131flar\u0131 Nelerdir ve Uygulamalar\u0131n\u0131za Nas\u0131l De\u011fer Katarlar?<\/h2>\n

\n Kalite Servisi (QoS), Kubernetes’in Pod’lara kaynak garantileri atamak ve Node’larda kaynak s\u0131k\u0131nt\u0131s\u0131 ya\u015fand\u0131\u011f\u0131nda hangi Pod’lar\u0131n \u00f6ncelikle sonland\u0131r\u0131laca\u011f\u0131n\u0131 (evict edilece\u011fini) belirlemek i\u00e7in kulland\u0131\u011f\u0131 temel bir kavramd\u0131r. Kubernetes’te her Pod, tan\u0131mlanan kaynak istekleri (requests<\/code>) ve limitleri (limits<\/code>) baz al\u0131narak \u00fc\u00e7 farkl\u0131 QoS s\u0131n\u0131f\u0131ndan birine atan\u0131r: Guaranteed, Burstable ve BestEffort. Bu s\u0131n\u0131fland\u0131rma, Pod’lar\u0131n kararl\u0131l\u0131\u011f\u0131n\u0131 ve performans\u0131n\u0131 do\u011frudan etkilerken, ayn\u0131 zamanda Node kaynaklar\u0131n\u0131n verimli kullan\u0131m\u0131n\u0131 da optimize eder. Do\u011fru QoS s\u0131n\u0131f\u0131n\u0131 se\u00e7mek, uygulaman\u0131z\u0131n kritiklik d\u00fczeyine ve kaynak t\u00fcketim desenine g\u00f6re b\u00fcy\u00fck \u00f6nem ta\u015f\u0131r.\n<\/p>\n

\n QoS s\u0131n\u0131flar\u0131n\u0131 anlamak, Pod’lar\u0131n\u0131z\u0131n ne zaman ve hangi ko\u015fullarda Node’dan at\u0131laca\u011f\u0131n\u0131 (eviction) \u00f6ng\u00f6rmenizi sa\u011flar. Bu da, \u00f6zellikle yo\u011fun anlarda veya Node ar\u0131zalar\u0131 gibi beklenmedik durumlarda k\u00fcmenizin direncini art\u0131rman\u0131za yard\u0131mc\u0131 olur. \u015eimdi bu \u00fc\u00e7 ana QoS s\u0131n\u0131f\u0131n\u0131 daha yak\u0131ndan inceleyelim:\n<\/p>\n

Guaranteed QoS: Kritik \u0130\u015f Y\u00fckleriniz \u0130\u00e7in S\u00fcrekli Performans Nas\u0131l Sa\u011flan\u0131r?<\/h3>\n

\n Guaranteed<\/code> s\u0131n\u0131f\u0131, Kubernetes k\u00fcmenizdeki en kritik i\u015f y\u00fckleri i\u00e7in tasarlanm\u0131\u015ft\u0131r. Bu Pod’lar, Node’da en y\u00fcksek \u00f6nceli\u011fe sahiptir ve kaynak s\u0131k\u0131nt\u0131s\u0131 durumunda en son evict edilecek Pod’lar aras\u0131ndad\u0131r. Bir Pod’un Guaranteed<\/code> s\u0131n\u0131f\u0131na girmesi i\u00e7in, her bir Container’\u0131 i\u00e7in CPU ve bellek kaynaklar\u0131 i\u00e7in hem requests<\/code> hem de limits<\/code> de\u011ferlerinin tan\u0131mlanmas\u0131 ve bu de\u011ferlerin birbirine e\u015fit olmas\u0131 gerekir. Ayr\u0131ca, limits<\/code> de\u011ferleri, e\u011fer tan\u0131ml\u0131ysa, requests<\/code> de\u011ferlerinden b\u00fcy\u00fck olmamal\u0131d\u0131r. Bu s\u0131k\u0131 gereksinimler, Pod’un her zaman talep etti\u011fi kadar kayna\u011fa sahip olmas\u0131n\u0131 garanti eder.\n<\/p>\n

\n \u00d6rne\u011fin, bir bankac\u0131l\u0131k uygulamas\u0131n\u0131n veritaban\u0131 Pod’lar\u0131 veya y\u00fcksek trafikli bir API a\u011f ge\u00e7idi gibi s\u00fcrekli ve kesintisiz \u00e7al\u0131\u015fmas\u0131 gereken servisler i\u00e7in Guaranteed<\/code> s\u0131n\u0131f\u0131 idealdir. Bu sayede, Pod’lar, Node’da ba\u015fka Pod’lar kaynaklar\u0131 zorlasa bile tan\u0131mlanan performans seviyelerini koruyabilir.\n<\/p>\n

\napiVersion: v1\nkind: Pod\nmetadata:\n  name: guaranteed-pod\nspec:\n  containers:\n  - name: my-container\n    image: nginx\n    resources:\n      requests:\n        memory: \"256Mi\"\n        cpu: \"500m\"\n      limits:\n        memory: \"256Mi\"\n        cpu: \"500m\"\n<\/pre>\n
<\/code><\/p>\n
Burstable QoS: Esnekli\u011fin ve Verimlili\u011fin Dengesi Nas\u0131l Kurulur?<\/h3>\n
\n  Burstable<\/code> s\u0131n\u0131f\u0131, \u00e7o\u011fu tipik uygulama i\u00e7in varsay\u0131lan ve en yayg\u0131n kullan\u0131lan QoS s\u0131n\u0131f\u0131d\u0131r. Bu Pod'lar, Guaranteed<\/code> s\u0131n\u0131f\u0131na k\u0131yasla daha esnektir ve kaynaklar\u0131 daha verimli kullanabilirler. Bir Pod'un Burstable<\/code> s\u0131n\u0131f\u0131na girmesi i\u00e7in a\u015fa\u011f\u0131daki ko\u015fullardan biri veya birka\u00e7\u0131 sa\u011flanmal\u0131d\u0131r:\n<\/p>\n
    \n
  • En az bir Container i\u00e7in CPU veya bellek requests<\/code> tan\u0131mlanm\u0131\u015f olmal\u0131, ancak bu kaynaklar i\u00e7in limits<\/code> tan\u0131mlanmam\u0131\u015f olabilir.<\/li>\n
  • T\u00fcm Container'lar i\u00e7in requests<\/code> tan\u0131mlanm\u0131\u015f, ancak requests<\/code> ve limits<\/code> de\u011ferleri birbirinden farkl\u0131d\u0131r.<\/li>\n<\/ul>\n
    \n  Burstable<\/code> Pod'lar, Node'da bo\u015fta kalan kaynaklar\u0131 kullanabilirler ve bu sayede k\u0131sa s\u00fcreli ani y\u00fck art\u0131\u015flar\u0131n\u0131 (burst) kar\u015f\u0131layabilirler. Ancak, Node kaynak s\u0131k\u0131nt\u0131s\u0131 ya\u015fad\u0131\u011f\u0131nda, Guaranteed<\/code> Pod'lardan sonra, ancak BestEffort<\/code> Pod'lardan \u00f6nce evict edilme riski ta\u015f\u0131rlar. Web sunucular\u0131, mikroservisler veya orta \u00f6ncelikli i\u015fleme servisleri gibi bir\u00e7ok uygulama, Burstable<\/code> s\u0131n\u0131f\u0131 ile gayet iyi \u00e7al\u0131\u015f\u0131r.\n<\/p>\n
    \napiVersion: v1\nkind: Pod\nmetadata:\n  name: burstable-pod\nspec:\n  containers:\n  - name: my-container\n    image: nginx\n    resources:\n      requests:\n        memory: \"128Mi\"\n        cpu: \"250m\"\n      limits:\n        memory: \"512Mi\"\n        cpu: \"1000m\"\n<\/pre>\n
    <\/code><\/p>\n
    BestEffort QoS: Arka Plan G\u00f6revleriniz \u0130\u00e7in En Uygun Maliyetli \u00c7\u00f6z\u00fcm Nas\u0131l Uygulan\u0131r?<\/h3>\n
    \n  BestEffort<\/code> s\u0131n\u0131f\u0131, en d\u00fc\u015f\u00fck \u00f6nceli\u011fe sahip Pod'lar\u0131 temsil eder. Bu Pod'lar i\u00e7in hi\u00e7bir kaynak requests<\/code> veya limits<\/code> tan\u0131mlanmaz. Kubernetes, bu Pod'lara herhangi bir kaynak garantisi vermez ve Node'da kaynak s\u0131k\u0131nt\u0131s\u0131 ya\u015fand\u0131\u011f\u0131nda ilk evict edilecek Pod'lar olurlar. Genellikle \u00f6nemsiz arka plan g\u00f6revleri, geli\u015ftirme ortam\u0131ndaki test Pod'lar\u0131 veya batch i\u015fleri gibi kesintiye u\u011framas\u0131 performans\u0131 kritik \u00f6l\u00e7\u00fcde etkilemeyecek i\u015f y\u00fckleri i\u00e7in kullan\u0131l\u0131rlar. BestEffort<\/code> Pod'lar, kaynaklar\u0131 en esnek \u015fekilde kullan\u0131r ancak kararl\u0131l\u0131klar\u0131 en d\u00fc\u015f\u00fckt\u00fcr.\n<\/p>\n
    \napiVersion: v1\nkind: Pod\nmetadata:\n  name: besteffort-pod\nspec:\n  containers:\n  - name: my-container\n    image: nginx\n    # Hi\u00e7bir requests veya limits tan\u0131mlanmaz\n<\/pre>\n
    <\/code><\/p>\n
    \n  QoS s\u0131n\u0131flar\u0131n\u0131n kar\u015f\u0131la\u015ft\u0131rmas\u0131 a\u015fa\u011f\u0131daki tabloda \u00f6zetlenmi\u015ftir:\n<\/p>\n\n\n\n\n\n\n\n
    QoS S\u0131n\u0131f\u0131<\/th>\nRequests\/Limits Tan\u0131m\u0131<\/th>\nNode Kaynak S\u0131k\u0131nt\u0131s\u0131 Davran\u0131\u015f\u0131<\/th>\nKullan\u0131m Alan\u0131<\/th>\n<\/tr>\n<\/thead>\n
    Guaranteed<\/td>\nT\u00fcm Container'lar i\u00e7in requests == limits<\/code><\/td>\nEn y\u00fcksek \u00f6ncelik, en son evict edilir.<\/td>\nKritik veritabanlar\u0131, \u00f6deme sistemleri.<\/td>\n<\/tr>\n
    Burstable<\/td>\nEn az bir Container i\u00e7in requests<\/code> var, requests != limits<\/code> olabilir.<\/td>\nOrta \u00f6ncelik, Guaranteed<\/code>'den sonra, BestEffort<\/code>'tan \u00f6nce evict edilir.<\/td>\nWeb sunucular\u0131, mikroservisler.<\/td>\n<\/tr>\n
    BestEffort<\/td>\nHi\u00e7bir requests<\/code> veya limits<\/code> tan\u0131ml\u0131 de\u011fil.<\/td>\nEn d\u00fc\u015f\u00fck \u00f6ncelik, ilk evict edilir.<\/td>\nArka plan g\u00f6revleri, test Pod'lar\u0131.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
    Pod \u00d6nceli\u011fi (Priority) ve Preemption: Acil \u0130\u015f Y\u00fckleri Di\u011ferlerini Nas\u0131l Yerinden Eder?<\/h2>\n
    \n  Kubernetes'teki QoS s\u0131n\u0131flar\u0131, Pod'lar\u0131n kaynak garantilerini ve evict edilme davran\u0131\u015flar\u0131n\u0131 belirlerken, Pod \u00d6nceli\u011fi (Priority) mekanizmas\u0131, Pod'lar\u0131n k\u00fcme i\u00e7inde ne kadar \u00f6nemli oldu\u011funu ifade eden daha \u00fcst d\u00fczey bir kavramd\u0131r. \u00d6zellikle k\u00fcme kaynaklar\u0131n\u0131n s\u0131n\u0131rl\u0131 oldu\u011fu durumlarda, daha y\u00fcksek \u00f6nceli\u011fe sahip Pod'lar\u0131n, daha d\u00fc\u015f\u00fck \u00f6nceli\u011fe sahip Pod'lar\u0131 Node'dan \"yerinden etme\" (preemption) yetene\u011fi sayesinde kritik i\u015f y\u00fcklerinin \u00e7al\u0131\u015fmaya devam etmesi sa\u011flan\u0131r. Bu mekanizma, cluster y\u00f6neticilerine, en \u00f6nemli uygulamalar\u0131n her ko\u015fulda \u00e7al\u0131\u015fmas\u0131n\u0131 garantileme konusunda g\u00fc\u00e7l\u00fc bir ara\u00e7 sunar.\n<\/p>\n
    \n  \u00d6ncelik, PriorityClass<\/code> ad\u0131nda bir k\u00fcme genelindeki (cluster-scoped) API nesnesiyle tan\u0131mlan\u0131r. Bu nesne, bir isim ve bir say\u0131sal value<\/code> i\u00e7erir. Y\u00fcksek value<\/code> de\u011ferleri, daha y\u00fcksek \u00f6nceli\u011fi g\u00f6sterir. \u00d6rne\u011fin, 1000000 gibi bir de\u011fere sahip bir PriorityClass<\/code>, 1000 de\u011fere sahip olandan daha y\u00fcksek \u00f6ncelikli kabul edilir. K\u00fcme y\u00f6neticisi, farkl\u0131 \u00f6ncelik seviyeleri i\u00e7in birden fazla PriorityClass<\/code> tan\u0131mlayabilir ve Pod'lar bu s\u0131n\u0131flar\u0131 referans g\u00f6stererek \u00f6nceliklerini belirtirler. E\u011fer bir Pod'a PriorityClass<\/code> atanmazsa, varsay\u0131lan olarak s\u0131f\u0131r \u00f6nceli\u011fe sahip olur.\n<\/p>\n
    \n  Peki, bu \u00f6ncelik mekanizmas\u0131 pratikte nas\u0131l \u00e7al\u0131\u015f\u0131r? Bir Pod, bir Node'a yerle\u015femedi\u011finde (yani, Node'da yeterli kaynak yoksa), Kubernetes Scheduler, daha y\u00fcksek \u00f6nceli\u011fe sahip Pod'un yerle\u015febilmesi i\u00e7in, daha d\u00fc\u015f\u00fck \u00f6nceli\u011fe sahip Pod'lar\u0131 Node'dan zorla \u00e7\u0131karmay\u0131 (preemption) deneyebilir. Bu s\u00fcre\u00e7te, Scheduler \u00f6ncelikle Node \u00fczerindeki kaynaklar\u0131 bo\u015faltmak i\u00e7in en d\u00fc\u015f\u00fck \u00f6ncelikli Pod'lar\u0131 belirler ve bunlar\u0131 sonland\u0131r\u0131r. Bu, \u00f6zellikle s\u00fcrekli entegrasyon\/s\u00fcrekli da\u011f\u0131t\u0131m (CI\/CD) boru hatlar\u0131nda, acil \u00fcretim yamalar\u0131nda veya kritik veri i\u015fleme g\u00f6revlerinde \u00e7ok kullan\u0131\u015fl\u0131d\u0131r. \u00d6rne\u011fin, bir kritik g\u00fcvenlik g\u00fcncellemesinin devreye al\u0131nmas\u0131 gerekti\u011finde, bu g\u00fcncellemenin Pod'lar\u0131, mevcut daha d\u00fc\u015f\u00fck \u00f6ncelikli test Pod'lar\u0131n\u0131 preempt ederek an\u0131nda \u00e7al\u0131\u015fmaya ba\u015flayabilir.\n<\/p>\n
    \n  Preemption<\/code> mekanizmas\u0131, k\u00fcme kaynaklar\u0131n\u0131n verimli kullan\u0131lmas\u0131n\u0131 sa\u011flarken, ayn\u0131 zamanda kritik servislerin s\u00fcreklili\u011fini teminat alt\u0131na al\u0131r. Ancak, bu g\u00fcc\u00fcn dikkatli kullan\u0131lmas\u0131 gerekir. Yanl\u0131\u015f yap\u0131land\u0131r\u0131lm\u0131\u015f veya a\u015f\u0131r\u0131 agresif \u00f6nceliklendirme, \u00f6nemli olmayan Pod'lar\u0131n s\u00fcrekli olarak yerinden edilmesine ve uygulama kesintilerine yol a\u00e7abilir. Bu nedenle, PriorityClass<\/code> tan\u0131mlarken ve Pod'lara atarken iyi d\u00fc\u015f\u00fcn\u00fclm\u00fc\u015f bir stratejiye sahip olmak esast\u0131r.\n<\/p>\n
    \n  \u0130\u015fte bir PriorityClass<\/code> tan\u0131m\u0131 ve bu s\u0131n\u0131f\u0131 kullanan bir Pod \u00f6rne\u011fi:\n<\/p>\n
    \napiVersion: scheduling.k8s.io\/v1\nkind: PriorityClass\nmetadata:\n  name: high-priority\nvalue: 1000000\nglobalDefault: false\ndescription: \"Bu s\u0131n\u0131f, \u00fcretimdeki kritik servisler i\u00e7in kullan\u0131l\u0131r.\"\n---\napiVersion: v1\nkind: Pod\nmetadata:\n  name: critical-api-pod\nspec:\n  containers:\n  - name: api-container\n    image: my-critical-api:v1.0\n    resources:\n      requests:\n        memory: \"512Mi\"\n        cpu: \"1\"\n      limits:\n        memory: \"1Gi\"\n        cpu: \"2\"\n  priorityClassName: high-priority # Tan\u0131mlad\u0131\u011f\u0131m\u0131z PriorityClass'\u0131 kullan\u0131yoruz\n<\/pre>\n
    <\/code><\/p>\n
    \n  Bu yap\u0131land\u0131rma, critical-api-pod<\/code>'un di\u011fer Pod'lara g\u00f6re daha y\u00fcksek bir \u00f6nceli\u011fe sahip olmas\u0131n\u0131 sa\u011flar, b\u00f6ylece kaynak s\u0131k\u0131nt\u0131s\u0131 durumunda hayati \u00f6nem ta\u015f\u0131yan bu servis kesintisiz \u00e7al\u0131\u015fmaya devam edebilir.\n<\/p>\n
    Scheduler Puanlama (Scoring): Pod'lar En Uygun Node'u Nas\u0131l Bulur?<\/h2>\n
    \n  Kubernetes Scheduler'\u0131n temel g\u00f6revi, yeni olu\u015fturulan veya planlanmam\u0131\u015f Pod'lar\u0131 (yani Node<\/code> alan\u0131 bo\u015f olan Pod'lar\u0131) bir Node'a atamakt\u0131r. Bu s\u00fcre\u00e7 iki ana a\u015famadan olu\u015fur: Filtreleme (Filtering) ve Puanlama (Scoring). Filtreleme a\u015famas\u0131nda, Pod'un gereksinimlerini kar\u015f\u0131lamayan (\u00f6rne\u011fin, yeterli kayna\u011f\u0131 olmayan, belirli bir taint'e sahip olan ancak toleration'\u0131 olmayan) Node'lar elenir. Ger\u00e7ek dengeleme sanat\u0131 ise Puanlama a\u015famas\u0131nda ba\u015flar. Puanlama (Scoring), Pod'un kalan uygun Node'lar aras\u0131nda en \"uygun\" olan\u0131n\u0131 belirlemek i\u00e7in kullan\u0131l\u0131r. Her bir uygun Node'a bir puan atan\u0131r ve en y\u00fcksek puan\u0131 alan Node, Pod i\u00e7in se\u00e7ilir.\n<\/p>\n
    \n  Puanlama, \u00e7e\u015fitli fakt\u00f6rlere dayal\u0131 olarak yap\u0131l\u0131r ve Scheduler, bu fakt\u00f6rleri kullanarak her Node'un Pod i\u00e7in ne kadar ideal oldu\u011funu de\u011ferlendirir. Bu fakt\u00f6rler aras\u0131nda \u015funlar bulunabilir:\n<\/p>\n
      \n
    • Kaynak Kullan\u0131m\u0131:<\/strong> Node'un mevcut CPU ve bellek kullan\u0131m\u0131. Scheduler genellikle, kaynaklar\u0131 en az kullanan veya en dengeli da\u011f\u0131t\u0131ma sahip Node'lar\u0131 tercih eder.<\/li>\n
    • Node Affinity\/Anti-Affinity:<\/strong> Pod'un belirli etiketlere (labels) sahip Node'larda \u00e7al\u0131\u015fmas\u0131n\u0131 isteyen veya belirli Node'lardan uzak durmas\u0131n\u0131 isteyen kurallar. \u00d6rne\u011fin, bir veritaban\u0131 Pod'u, y\u00fcksek performansl\u0131 depolama birimine sahip Node'lar\u0131 tercih edebilir.<\/li>\n
    • Pod Affinity\/Anti-Affinity:<\/strong> Pod'un belirli di\u011fer Pod'larla ayn\u0131 Node \u00fczerinde veya farkl\u0131 Node'lar \u00fczerinde \u00e7al\u0131\u015fmas\u0131n\u0131 sa\u011flayan kurallar. Bu, y\u00fcksek eri\u015filebilirlik veya performans i\u00e7in kritik \u00f6neme sahiptir.<\/li>\n
    • Taints ve Tolerations:<\/strong> Node'lar\u0131 yaln\u0131zca belirli Pod'lar\u0131n \u00fczerinde \u00e7al\u0131\u015fmas\u0131na izin verecek \u015fekilde i\u015faretlemek i\u00e7in kullan\u0131l\u0131r.<\/li>\n
    • Volume Kullan\u0131m\u0131:<\/strong> Pod'un persistent volume taleplerini kar\u015f\u0131layabilecek Node'lar.<\/li>\n
    • Dengeleme Politikalar\u0131:<\/strong> Pod'lar\u0131 Node'lar aras\u0131nda e\u015fit da\u011f\u0131tma veya belirli Pod'lar\u0131 belirli Node'larda yo\u011funla\u015ft\u0131rma gibi politikalar.<\/li>\n<\/ul>\n
      \n  Scheduler, bu ve benzeri bir\u00e7ok \"puanlama fonksiyonunu\" kullanarak her Node i\u00e7in bir toplam puan hesaplar. Her fonksiyon, Pod'un ve Node'un \u00f6zelliklerine g\u00f6re bir puan d\u00f6nd\u00fcr\u00fcr ve bu puanlar a\u011f\u0131rl\u0131kland\u0131r\u0131larak nihai bir toplam puan elde edilir. \u00d6rne\u011fin, bir Pod'un bellek iste\u011fi y\u00fcksekse, Scheduler daha fazla bo\u015f belle\u011fi olan Node'lara daha y\u00fcksek puan verebilir. Bu dinamik puanlama sistemi sayesinde, Kubernetes k\u00fcmesi sadece kaynaklar\u0131 bo\u015f olan Node'a Pod yerle\u015ftirmekle kalmaz, ayn\u0131 zamanda gelecekteki performans, eri\u015filebilirlik ve dengeleme hedeflerini de g\u00f6z \u00f6n\u00fcnde bulundurarak en optimum karar\u0131 verir. Bu yakla\u015f\u0131m, k\u00fcmenin uzun vadeli sa\u011fl\u0131\u011f\u0131 ve verimlili\u011fi i\u00e7in kritik \u00f6neme sahiptir.\n<\/p>\n
      \n  Geli\u015fmi\u015f senaryolarda, Kubernetes'in varsay\u0131lan Scheduler'\u0131n\u0131n davran\u0131\u015f\u0131n\u0131 de\u011fi\u015ftirmek veya geni\u015fletmek m\u00fcmk\u00fcnd\u00fcr. Scheduler Extenders<\/code> ve Custom Schedulers<\/code> kullanarak, kendi puanlama algoritmalar\u0131n\u0131z\u0131 veya filtreleme kurallar\u0131n\u0131z\u0131 uygulayabilir, b\u00f6ylece i\u015f y\u00fcklerinize \u00f6zel optimizasyonlar yapabilirsiniz. Bu, \u00f6zellikle karma\u015f\u0131k veya \u00f6zel gereksinimleri olan kurumsal ortamlarda b\u00fcy\u00fck avantaj sa\u011flar.\n<\/p>\n
      \n  \u0130\u015fte bir Pod Affinity<\/code> \u00f6rne\u011fi, Scheduler'\u0131n puanlama a\u015famas\u0131nda bu t\u00fcr kurallar\u0131 nas\u0131l de\u011ferlendirece\u011fine dair bir ipucu sunar:\n<\/p>\n
      \napiVersion: v1\nkind: Pod\nmetadata:\n  name: web-server-pod\n  labels:\n    app: webserver\nspec:\n  containers:\n  - name: web-container\n    image: nginx\n  affinity:\n    podAffinity:\n      requiredDuringSchedulingIgnoredDuringExecution:\n      - labelSelector:\n          matchLabels:\n            app: cache-service # Bu Pod'un, 'app: cache-service' etiketli Pod'larla ayn\u0131 Node'a yerle\u015fmesini istiyoruz\n        topologyKey: \"kubernetes.io\/hostname\"\n<\/pre>\n
      <\/code><\/p>\n
      \n  Bu Pod tan\u0131m\u0131, Scheduler'a, web-server-pod<\/code>'u, app: cache-service<\/code> etiketine sahip ba\u015fka bir Pod'un \u00e7al\u0131\u015ft\u0131\u011f\u0131 bir Node'a yerle\u015ftirmesi i\u00e7in y\u00fcksek puan vermesini s\u00f6yler. Bu, web sunucusunun \u00f6nbellek servisine d\u00fc\u015f\u00fck gecikmeli eri\u015fim sa\u011flamas\u0131 gereken durumlarda kritik bir optimizasyondur.\n<\/p>\n
      Geli\u015fmi\u015f Dengeleme Stratejileri: QoS, \u00d6ncelik ve Puanlamay\u0131 Birlikte Kullanmak<\/h2>\n
      \n  Kubernetes'teki QoS, \u00d6ncelik ve Puanlama mekanizmalar\u0131, tek ba\u015flar\u0131na g\u00fc\u00e7l\u00fc ara\u00e7lar olsalar da, ger\u00e7ek anlamda dengeli ve verimli bir k\u00fcme olu\u015fturmak i\u00e7in bunlar\u0131n birle\u015fik kullan\u0131m\u0131 vazge\u00e7ilmezdir. Bu \u00fc\u00e7l\u00fcn\u00fcn ak\u0131ll\u0131ca orkestrasyonu, sadece Pod'lar\u0131n uygun Node'lara yerle\u015fmesini sa\u011flamakla kalmaz, ayn\u0131 zamanda k\u00fcmenin ya\u015fam d\u00f6ng\u00fcs\u00fc boyunca kaynak kullan\u0131m\u0131n\u0131 dinamik olarak optimize eder ve beklenmedik durumlar kar\u015f\u0131s\u0131nda dayan\u0131kl\u0131l\u0131\u011f\u0131 art\u0131r\u0131r.\n<\/p>\n
      \n  \u00d6rne\u011fin, bir b\u00fcy\u00fck veri i\u015fleme platformunda, kritik veritaban\u0131 Pod'lar\u0131 Guaranteed<\/code> QoS ve y\u00fcksek PriorityClass<\/code> ile yap\u0131land\u0131r\u0131labilirken, veri i\u015fleme i\u015f y\u00fckleri Burstable<\/code> QoS ile daha orta seviye bir \u00f6ncelik alabilir. Anl\u0131k raporlama veya ad-hoc analizler i\u00e7in kullan\u0131lan Pod'lar ise BestEffort<\/code> ve d\u00fc\u015f\u00fck \u00f6ncelik ile \u00e7al\u0131\u015ft\u0131r\u0131labilir. Scheduler, bu Pod'lar\u0131 yerle\u015ftirirken, puanlama algoritmalar\u0131 arac\u0131l\u0131\u011f\u0131yla Node kaynaklar\u0131n\u0131, mevcut i\u015f y\u00fck\u00fcn\u00fc ve Pod'lar aras\u0131ndaki affinity\/anti-affinity kurallar\u0131n\u0131 g\u00f6z \u00f6n\u00fcnde bulundurarak en uygun karar\u0131 verir. Kaynak s\u0131k\u0131nt\u0131s\u0131 ya\u015fand\u0131\u011f\u0131nda, y\u00fcksek \u00f6ncelikli Pod'lar, d\u00fc\u015f\u00fck \u00f6ncelikli Pod'lar\u0131 preempt ederek kritik operasyonlar\u0131n aksamamas\u0131n\u0131 sa\u011flar. Bu entegre yakla\u015f\u0131m, operasyonel s\u00fcreklili\u011fi maksimize ederken, ayn\u0131 zamanda kaynaklar\u0131n israf\u0131n\u0131 \u00f6nler.\n<\/p>\n
      \n  Bu mekanizmalar\u0131 daha da ileri ta\u015f\u0131mak i\u00e7in, Kubernetes ekosistemindeki di\u011fer ara\u00e7larla entegrasyon \u00f6nemlidir. \u00d6rne\u011fin:\n<\/p>\n
        \n
      • Horizontal Pod Autoscaler (HPA) ve Vertical Pod Autoscaler (VPA):<\/strong> HPA, CPU veya bellek kullan\u0131m\u0131 gibi metrikleri izleyerek Pod say\u0131s\u0131n\u0131 yatayda \u00f6l\u00e7eklendirirken, VPA, Pod'lar\u0131n kaynak isteklerini (requests) ve limitlerini dikeyde optimize eder. Bu otomasyon ara\u00e7lar\u0131, QoS ve Priority ile birlikte \u00e7al\u0131\u015farak Pod'lar\u0131n do\u011fru boyutta olmas\u0131n\u0131 ve de\u011fi\u015fen y\u00fck alt\u0131nda bile do\u011fru \u00f6ncelikle kaynak almas\u0131n\u0131 sa\u011flar.<\/li>\n
      • Cluster Autoscaler:<\/strong> Node'da yeterli kaynak kalmad\u0131\u011f\u0131nda yeni Node'lar ekler veya Node'lar bo\u015fald\u0131\u011f\u0131nda gereksiz Node'lar\u0131 kald\u0131rarak maliyet optimizasyonu sa\u011flar. Priority ve QoS, Cluster Autoscaler'\u0131n hangi Pod'lar\u0131n Node'lar\u0131 dolduraca\u011f\u0131n\u0131 ve hangi Pod'lar\u0131n yeni Node tetikleyece\u011fini anlamas\u0131na yard\u0131mc\u0131 olur.<\/li>\n
      • Descheduler:<\/strong> Zaman zaman Pod'lar, ilk planland\u0131klar\u0131 Node'da en iyi yeri bulamayabilirler (\u00f6rne\u011fin, Node'a sonradan y\u00fcksek \u00f6ncelikli bir Pod gelmi\u015f olabilir). Descheduler, k\u00fcme genelindeki Pod da\u011f\u0131l\u0131m\u0131n\u0131 optimize etmek i\u00e7in Pod'lar\u0131 Node'lar aras\u0131nda yeniden da\u011f\u0131tarak daha iyi denge sa\u011flar.<\/li>\n<\/ul>\n
        \n  Vaka Analizi: B\u00fcy\u00fck Bir SaaS \u015eirketinin Kaynak Optimizasyon Hikayesi<\/strong>
        \n  B\u00fcy\u00fck bir SaaS \u015firketi, artan m\u00fc\u015fteri talepleriyle birlikte Kubernetes k\u00fcmesinde performans sorunlar\u0131 ya\u015famaya ba\u015flam\u0131\u015ft\u0131. Kritik m\u00fc\u015fteri odakl\u0131 servisler (\u00f6deme, kullan\u0131c\u0131 giri\u015fi) ile arka plan veri i\u015fleme ve analitik servisleri ayn\u0131 k\u00fcmede \u00e7al\u0131\u015f\u0131yordu. \u00d6zellikle yo\u011fun saatlerde, arka plan i\u015fleri kaynaklar\u0131 t\u00fcketerek \u00f6n u\u00e7 servislerin yava\u015flamas\u0131na neden oluyordu.\n<\/p>\n
        \n  \u015eirket, bu durumu \u00e7\u00f6zmek i\u00e7in a\u015fa\u011f\u0131daki ad\u0131mlar\u0131 izledi:\n<\/p>\n
          \n
        1. QoS S\u0131n\u0131fland\u0131rmas\u0131:<\/strong> \u00d6deme ve kullan\u0131c\u0131 giri\u015fi servisleri Guaranteed<\/code> QoS s\u0131n\u0131f\u0131na al\u0131nd\u0131. Orta \u00f6ncelikli API servisleri Burstable<\/code>, arka plan i\u015fleri ise BestEffort<\/code> olarak belirlendi.<\/li>\n
        2. PriorityClass Tan\u0131mlar\u0131:<\/strong> \u00dcretimdeki kritik servisler i\u00e7in \"prod-critical\", di\u011fer servisler i\u00e7in \"prod-standard\" ve test\/dev ortamlar\u0131 i\u00e7in \"low-priority\" \u015feklinde PriorityClass<\/code>'lar olu\u015fturuldu ve Pod'lara atand\u0131.<\/li>\n
        3. Affinity\/Anti-affinity Kurallar\u0131:<\/strong> Veritaban\u0131 Pod'lar\u0131 ile uygulama Pod'lar\u0131 aras\u0131nda b\u00f6lgesel anti-affinity kurallar\u0131 tan\u0131mlanarak y\u00fcksek eri\u015filebilirlik sa\u011fland\u0131.<\/li>\n
        4. Autoscaler Entegrasyonu:<\/strong> HPA ve VPA, kritik servislerin her zaman do\u011fru boyutta ve yeterli kaynakla \u00e7al\u0131\u015fmas\u0131n\u0131 sa\u011flamak i\u00e7in devreye al\u0131nd\u0131. Cluster Autoscaler ise Node kaynaklar\u0131 yetersiz kald\u0131\u011f\u0131nda otomatik olarak yeni Node'lar ekledi.<\/li>\n<\/ol>\n
          \n  Bu entegre yakla\u015f\u0131m sayesinde \u015firket, yo\u011fun saatlerde bile kritik servislerinin performans\u0131n\u0131 stabilize etmeyi ba\u015fard\u0131, kaynak israf\u0131n\u0131 azaltt\u0131 ve operasyonel maliyetlerden \u00f6nemli \u00f6l\u00e7\u00fcde tasarruf etti. Ayr\u0131ca, preemption<\/code> mekanizmas\u0131 sayesinde, kritik g\u00fcvenlik yamalar\u0131 gibi acil durum Pod'lar\u0131, di\u011fer i\u015f y\u00fcklerini durdurarak an\u0131nda devreye al\u0131nabildi. Bu \u00f6rnek, Kubernetes'in QoS, Priority ve Puanlama yeteneklerinin birle\u015fiminin, modern i\u015f y\u00fcklerinin karma\u015f\u0131kl\u0131\u011f\u0131n\u0131 y\u00f6netmek i\u00e7in ne kadar hayati oldu\u011funu a\u00e7\u0131k\u00e7a g\u00f6stermektedir.\n<\/p>\n
          \n  Uzman \u0130pucu: Kubernetes k\u00fcmenizi mobil cihazlardan etkin bir \u015fekilde y\u00f6netmek i\u00e7in, kontrol paneli ve CLI ara\u00e7lar\u0131n\u0131z\u0131n duyarl\u0131 (responsive) aray\u00fczler sundu\u011fundan emin olun. Geli\u015ftirme a\u015famas\u0131nda, CSS media query'leri kullanarak farkl\u0131 ekran boyutlar\u0131na uygun tasar\u0131mlar yapmay\u0131 ihmal etmeyin. \u00d6rne\u011fin: <\/p>\n
          @media (max-width: 768px) { \/* Mobil \u00f6zel CSS kurallar\u0131 *\/ }<\/pre>\n
          <\/code> Bu, acil durumlarda uzaktan m\u00fcdahale yetene\u011finizi art\u0131racakt\u0131r.\n<\/div>\n
          Sonu\u00e7: Kubernetes K\u00fcmelerinizde Kararl\u0131l\u0131\u011f\u0131 ve Verimlili\u011fi Nas\u0131l Sa\u011flayabilirsiniz?<\/h2>\n
          \n  G\u00f6r\u00fcld\u00fc\u011f\u00fc \u00fczere, Kubernetes, Pod'lar\u0131 sadece Node'lara yerle\u015ftiren basit bir planlay\u0131c\u0131 olmaktan \u00e7ok daha fazlas\u0131d\u0131r. Kalite Servisi (QoS), Pod \u00d6nceli\u011fi (Priority) ve Scheduler Puanlama (Scoring) gibi sofistike mekanizmalar arac\u0131l\u0131\u011f\u0131yla, k\u00fcme y\u00f6neticilerine i\u015f y\u00fcklerini \u00f6nceliklendirme, kaynaklar\u0131 garanti alt\u0131na alma ve Node'lar aras\u0131nda dengeli bir da\u011f\u0131l\u0131m sa\u011flama yetene\u011fi sunar. Bu entegre yakla\u015f\u0131m, modern, dinamik ve \u00e7e\u015fitli i\u015f y\u00fcklerine sahip k\u00fcmeler i\u00e7in kesintisiz performans, y\u00fcksek eri\u015filebilirlik ve optimum kaynak kullan\u0131m\u0131 sa\u011flaman\u0131n anahtar\u0131d\u0131r. Do\u011fru yap\u0131land\u0131rma ve stratejik kullan\u0131m ile Kubernetes k\u00fcmelerinizde kararl\u0131l\u0131\u011f\u0131 ve verimlili\u011fi en \u00fcst d\u00fczeye \u00e7\u0131karabilir, b\u00f6ylece uygulamalar\u0131n\u0131z\u0131n her zaman en iyi \u015fekilde \u00e7al\u0131\u015ft\u0131\u011f\u0131ndan emin olabilirsiniz. Gelecekte, bu mekanizmalar\u0131n yapay zeka ve makine \u00f6\u011frenimi ile daha da entegre olarak \u00f6zerk ve kendi kendini optimize eden k\u00fcmeler olu\u015fturma potansiyeli de bulunmaktad\u0131r.\n<\/p>\n
          S\u0131k\u00e7a Sorulan Sorular<\/h3>\n
            \n
          • \n    QoS S\u0131n\u0131flar\u0131 Aras\u0131nda Ge\u00e7i\u015f Yap\u0131labilir mi?<\/strong>
            \n    Bir Pod'un QoS s\u0131n\u0131f\u0131, Pod olu\u015fturuldu\u011fu anda belirlenir ve ya\u015fam d\u00f6ng\u00fcs\u00fc boyunca de\u011fi\u015fmez. E\u011fer bir Pod'un QoS s\u0131n\u0131f\u0131n\u0131 de\u011fi\u015ftirmek istiyorsan\u0131z, Pod'u silip, istedi\u011finiz kaynak istekleri ve limitleri ile yeni bir Pod olu\u015fturman\u0131z gerekir.\n  <\/li>\n
          • \n    PriorityClass Kullan\u0131m\u0131 Performans\u0131 Nas\u0131l Etkiler?<\/strong>
            \n    PriorityClass<\/code> kullan\u0131m\u0131, kritik i\u015f y\u00fcklerinin Node \u00fczerinde \u00f6ncelik kazanmas\u0131n\u0131 sa\u011flayarak onlar\u0131n performans\u0131n\u0131 art\u0131r\u0131r. Ancak, d\u00fc\u015f\u00fck \u00f6ncelikli Pod'lar\u0131n preempt edilmesine yol a\u00e7abilece\u011fi i\u00e7in, bu Pod'lar\u0131n performans\u0131nda ge\u00e7ici d\u00fc\u015f\u00fc\u015fler veya kesintiler ya\u015fanabilir. Dengeli bir kullan\u0131m stratejisi \u00f6nemlidir.\n  <\/li>\n
          • \n    Puanlama Mekanizmas\u0131n\u0131 \u00d6zelle\u015ftirmek Ne Zaman Gerekir?<\/strong>
            \n    Varsay\u0131lan Kubernetes Scheduler \u00e7o\u011fu senaryo i\u00e7in yeterli olsa da, \u00e7ok \u00f6zel gereksinimleri olan karma\u015f\u0131k k\u00fcmelerde (\u00f6rne\u011fin, belirli donan\u0131m akselerat\u00f6rlerine sahip Node'lar, lisanslama k\u0131s\u0131tlamalar\u0131 veya \u00f6zel co\u011frafi da\u011f\u0131t\u0131m kurallar\u0131) puanlama mekanizmas\u0131n\u0131 \u00f6zelle\u015ftirmek gerekebilir. Bu genellikle Scheduler Extenders<\/code> veya Custom Schedulers<\/code> ile yap\u0131l\u0131r.\n  <\/li>\n
          • \n    BestEffort Pod'lar Ne Zaman Evict Edilir?<\/strong>
            \n    BestEffort<\/code> Pod'lar, Node'da kaynak s\u0131k\u0131nt\u0131s\u0131 (\u00f6rne\u011fin, bellek bas\u0131nc\u0131) ya\u015fand\u0131\u011f\u0131nda, Burstable<\/code> ve Guaranteed<\/code> Pod'lardan \u00f6nce, yani ilk olarak evict edilirler. Kubernetes'in kubelet<\/code> bile\u015feni, bu eviction kararlar\u0131n\u0131 al\u0131r.\n  <\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"Modern uygulamalar\u0131n karma\u015f\u0131kl\u0131\u011f\u0131 artt\u0131k\u00e7a, Kubernetes k\u00fcmelerindeki kaynak y\u00f6netimi sadece Pod’lar\u0131 \u00e7al\u0131\u015ft\u0131rman\u0131n \u00f6tesine ge\u00e7iyor. Peki, k\u00fcmelerinizdeki kritik i\u015f y\u00fcklerinin…","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"csco_page_header_type":"","csco_page_load_nextpost":"","csco_page_subscribe_form":"","csco_page_contact_form":"","footnotes":""},"categories":[1491],"tags":[],"class_list":{"0":"post-34642","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-kubernetes","7":"cs-entry","8":"cs-video-wrap"},"yoast_head":"\nKubernetes Dengeleme: QoS, \u00d6ncelik ve Puanlama ile Nas\u0131l \u00c7al\u0131\u015f\u0131r?<\/title>\n<meta name=\"description\" content=\"Modern uygulamalar\u0131n karma\u015f\u0131kl\u0131\u011f\u0131 artt\u0131k\u00e7a, Kubernetes k\u00fcmelerindeki kaynak y\u00f6netimi sadece Pod'lar\u0131 \u00e7al\u0131\u015ft\u0131rman\u0131n \u00f6tesine ge\u00e7iyor. Peki, k\u00fcmelerinizdeki kritik i\u015f y\u00fcklerinin her zaman yeterli kayna\u011fa sahip oldu\u011fundan ve kararl\u0131 bir \u015fekilde \u00e7al\u0131\u015ft\u0131\u011f\u0131ndan nas\u0131l emin olabilirsiniz? Bu makalede, Kubernetes'in temel scheduling mekanizmalar\u0131n\u0131n \u00f6tesine ge\u00e7erek, Kalite Servisi (QoS), \u00d6ncelik (Priority) ve Puanlama (Scoring) kavramlar\u0131n\u0131 nas\u0131l kullanarak k\u00fcmenizi dengede tuttu\u011funu, performans\u0131 optimize etti\u011fini ve kesintileri minimize etti\u011fini derinlemesine inceleyece\u011fiz.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/fatihsoysal.com\/blog\/kubernetes-dengeleme-qos-oncelik-ve-puanlama-ile-nasil-calisir\/\" \/>\n<meta property=\"og:locale\" content=\"tr_TR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Kubernetes Dengeleme: QoS, \u00d6ncelik ve Puanlama ile Nas\u0131l \u00c7al\u0131\u015f\u0131r?\" \/>\n<meta property=\"og:description\" content=\"Modern uygulamalar\u0131n karma\u015f\u0131kl\u0131\u011f\u0131 artt\u0131k\u00e7a, Kubernetes k\u00fcmelerindeki kaynak y\u00f6netimi sadece Pod'lar\u0131 \u00e7al\u0131\u015ft\u0131rman\u0131n \u00f6tesine ge\u00e7iyor. Peki, k\u00fcmelerinizdeki kritik i\u015f y\u00fcklerinin her zaman yeterli kayna\u011fa sahip oldu\u011fundan ve kararl\u0131 bir \u015fekilde \u00e7al\u0131\u015ft\u0131\u011f\u0131ndan nas\u0131l emin olabilirsiniz? Bu makalede, Kubernetes'in temel scheduling mekanizmalar\u0131n\u0131n \u00f6tesine ge\u00e7erek, Kalite Servisi (QoS), \u00d6ncelik (Priority) ve Puanlama (Scoring) kavramlar\u0131n\u0131 nas\u0131l kullanarak k\u00fcmenizi dengede tuttu\u011funu, performans\u0131 optimize etti\u011fini ve kesintileri minimize etti\u011fini derinlemesine inceleyece\u011fiz.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/fatihsoysal.com\/blog\/kubernetes-dengeleme-qos-oncelik-ve-puanlama-ile-nasil-calisir\/\" \/>\n<meta property=\"og:site_name\" content=\"Kodlar\u0131n Gizemli D\u00fcnyas\u0131\" \/>\n<meta property=\"article:published_time\" content=\"2025-11-20T01:00:50+00:00\" \/>\n<meta name=\"author\" content=\"Fatih Soysal\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Yazan:\" \/>\n\t<meta name=\"twitter:data1\" content=\"Fatih Soysal\" \/>\n\t<meta name=\"twitter:label2\" content=\"Tahmini okuma s\u00fcresi\" \/>\n\t<meta name=\"twitter:data2\" content=\"18 dakika\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/kubernetes-dengeleme-qos-oncelik-ve-puanlama-ile-nasil-calisir\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/kubernetes-dengeleme-qos-oncelik-ve-puanlama-ile-nasil-calisir\/\"},\"author\":{\"name\":\"Fatih Soysal\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1\"},\"headline\":\"Kubernetes Dengeleme: QoS, \u00d6ncelik ve Puanlama ile Nas\u0131l \u00c7al\u0131\u015f\u0131r?\",\"datePublished\":\"2025-11-20T01:00:50+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/kubernetes-dengeleme-qos-oncelik-ve-puanlama-ile-nasil-calisir\/\"},\"wordCount\":3326,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1\"},\"articleSection\":[\"Kubernetes\"],\"inLanguage\":\"tr\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\/\/fatihsoysal.com\/blog\/kubernetes-dengeleme-qos-oncelik-ve-puanlama-ile-nasil-calisir\/#respond\"]}],\"copyrightYear\":\"2025\",\"copyrightHolder\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#organization\"}},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/kubernetes-dengeleme-qos-oncelik-ve-puanlama-ile-nasil-calisir\/\",\"url\":\"https:\/\/fatihsoysal.com\/blog\/kubernetes-dengeleme-qos-oncelik-ve-puanlama-ile-nasil-calisir\/\",\"name\":\"Kubernetes Dengeleme: QoS, \u00d6ncelik ve Puanlama ile Nas\u0131l \u00c7al\u0131\u015f\u0131r?\",\"isPartOf\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#website\"},\"datePublished\":\"2025-11-20T01:00:50+00:00\",\"description\":\"Modern uygulamalar\u0131n karma\u015f\u0131kl\u0131\u011f\u0131 artt\u0131k\u00e7a, Kubernetes k\u00fcmelerindeki kaynak y\u00f6netimi sadece Pod'lar\u0131 \u00e7al\u0131\u015ft\u0131rman\u0131n \u00f6tesine ge\u00e7iyor. 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