Kubernetes, da\u011f\u0131t\u0131k sistemlerin y\u00f6netiminde devrim yaratan bir platformdur. Temel i\u015flevlerinden biri olan pod zamanlama (scheduling), uygulamalar\u0131n k\u00fcme i\u00e7indeki d\u00fc\u011f\u00fcmlere ak\u0131ll\u0131ca yerle\u015ftirilmesini sa\u011flar. Ancak varsay\u0131lan zamanlay\u0131c\u0131, her senaryo i\u00e7in yeterli olmayabilir. Bu makalede, Kubernetes’in ileri seviye zamanlama yeteneklerini, yani “gizli cevherlerini” ke\u015ffederek, uygulamalar\u0131n\u0131z i\u00e7in daha sofistike ve optimize edilmi\u015f da\u011f\u0131t\u0131m stratejileri olu\u015fturmay\u0131 \u00f6\u011freneceksiniz.<\/p>\n
Kubernetes’te podlar\u0131n belirli d\u00fc\u011f\u00fcmlere yerle\u015ftirilmesi veya belirli d\u00fc\u011f\u00fcmlerden uzak tutulmas\u0131, Node Affinity ve Anti-Affinity kurallar\u0131 ile sa\u011flan\u0131r. Bu kurallar, uygulaman\u0131z\u0131n performans, g\u00fcvenlik veya maliyet gereksinimlerine g\u00f6re d\u00fc\u011f\u00fcm se\u00e7imini optimize etmenize olanak tan\u0131r.<\/p>\n
Bu kural, pod’un belirli etiketlere sahip bir d\u00fc\u011f\u00fcme yerle\u015ftirilmesini *zorunlu* k\u0131lar. E\u011fer k\u00fcmede bu ko\u015fulu sa\u011flayan bir d\u00fc\u011f\u00fcm yoksa, pod zamanlanamaz ve beklemede kal\u0131r. Kural bir kez uyguland\u0131ktan sonra, d\u00fc\u011f\u00fcm\u00fcn etiketleri de\u011fi\u015fse bile pod’un yerle\u015fimi etkilenmez.<\/p>\n
\napiVersion: v1\nkind: Pod\nmetadata:\n name: my-app-required\nspec:\n containers:\n - name: my-container\n image: nginx\n affinity:\n nodeAffinity:\n requiredDuringSchedulingIgnoredDuringExecution:\n nodeSelectorTerms:\n - matchExpressions:\n - key: disktype\n operator: In\n values:\n - ssd\n<\/pre>\n<\/code><\/p>\n
preferredDuringSchedulingIgnoredDuringExecution<\/h4>\n
Bu kural, pod'un belirli etiketlere sahip bir d\u00fc\u011f\u00fcme yerle\u015ftirilmesini *tercih eder*, ancak zorunlu k\u0131lmaz. E\u011fer tercih edilen ko\u015fulu sa\u011flayan bir d\u00fc\u011f\u00fcm yoksa, pod yine de ba\u015fka bir d\u00fc\u011f\u00fcme yerle\u015ftirilebilir. Bu, esneklik sa\u011flarken belirli bir d\u00fc\u011f\u00fcm t\u00fcr\u00fcn\u00fc hedeflemek istedi\u011finiz durumlarda kullan\u0131\u015fl\u0131d\u0131r.<\/p>\n
\napiVersion: v1\nkind: Pod\nmetadata:\n name: my-app-preferred\nspec:\n containers:\n - name: my-container\n image: nginx\n affinity:\n nodeAffinity:\n preferredDuringSchedulingIgnoredDuringExecution:\n - weight: 100\n preference:\n matchExpressions:\n - key: environment\n operator: In\n values:\n - production\n<\/pre>\n<\/code><\/p>\n
Kullan\u0131m Senaryolar\u0131 ve \u00d6rnekler<\/h4>\n
* Donan\u0131m Spesifikasyonu:<\/strong> Y\u00fcksek performansl\u0131 GPU'lara sahip d\u00fc\u011f\u00fcmlere sadece makine \u00f6\u011frenimi i\u015f y\u00fcklerini yerle\u015ftirmek.
\n* Lisanslama K\u0131s\u0131tlamalar\u0131:<\/strong> Belirli yaz\u0131l\u0131mlar\u0131n sadece lisansl\u0131 d\u00fc\u011f\u00fcmlerde \u00e7al\u0131\u015fmas\u0131n\u0131 sa\u011flamak.
\n* Maliyet Optimizasyonu:<\/strong> Geli\u015ftirme ortamlar\u0131n\u0131 daha ucuz spot instancelara, \u00fcretim ortamlar\u0131n\u0131 ise kararl\u0131 on-demand instancelara y\u00f6nlendirmek.<\/p>\nTaints ve Tolerations: D\u00fc\u011f\u00fcmleri \u00d6zel Ama\u00e7lar \u0130\u00e7in Ay\u0131rmak<\/h3>\n
Taints (leke) ve Tolerations (tolerans), Kubernetes'te d\u00fc\u011f\u00fcmleri belirli podlardan \"uzak tutmak\" veya sadece belirli podlar\u0131n o d\u00fc\u011f\u00fcmlerde \u00e7al\u0131\u015fmas\u0131na izin vermek i\u00e7in kullan\u0131lan g\u00fc\u00e7l\u00fc mekanizmalard\u0131r. Bir d\u00fc\u011f\u00fcme taint uyguland\u0131\u011f\u0131nda, o d\u00fc\u011f\u00fcmde sadece ilgili toleransa sahip podlar zamanlanabilir.<\/p>\n
Taint T\u00fcrleri ve Uygulama<\/h4>\n
Bir taint, key=value:effect<\/code> \u015feklinde tan\u0131mlan\u0131r. effect<\/code> parametresi, pod'un d\u00fc\u011f\u00fcme nas\u0131l tepki verece\u011fini belirtir:
\n* NoSchedule<\/code>: Tolerans\u0131 olmayan podlar d\u00fc\u011f\u00fcme zamanlanamaz.
\n* PreferNoSchedule<\/code>: Tolerans\u0131 olmayan podlar\u0131n d\u00fc\u011f\u00fcme zamanlanmamas\u0131 tercih edilir, ancak zorunlu de\u011fildir.
\n* NoExecute<\/code>: Tolerans\u0131 olmayan podlar d\u00fc\u011f\u00fcme zamanlanamaz ve halihaz\u0131rda d\u00fc\u011f\u00fcmde \u00e7al\u0131\u015fan tolerans\u0131 olmayan podlar tahliye edilir.<\/p>\nBir d\u00fc\u011f\u00fcme taint eklemek i\u00e7in kubectl taint<\/code> komutu kullan\u0131l\u0131r:<\/p>\n\nkubectl taint nodes node1 key1=value1:NoSchedule\n<\/pre>\n<\/code><\/p>\n
Toleration Tan\u0131mlama<\/h4>\n
Bir pod'un belirli bir taint'e tolerans g\u00f6stermesi i\u00e7in pod tan\u0131m\u0131na tolerations<\/code> alan\u0131 eklenir.<\/p>\n\napiVersion: v1\nkind: Pod\nmetadata:\n name: my-app-tolerated\nspec:\n containers:\n - name: my-container\n image: nginx\n tolerations:\n - key: \"key1\"\n operator: \"Equal\"\n value: \"value1\"\n effect: \"NoSchedule\"\n<\/pre>\n<\/code><\/p>\n
Pratik Kullan\u0131m Alanlar\u0131<\/h4>\n
* Ayr\u0131lm\u0131\u015f D\u00fc\u011f\u00fcmler:<\/strong> Belirli bir ekibe veya kritik uygulamalara \u00f6zel d\u00fc\u011f\u00fcmler ay\u0131rmak. \u00d6rne\u011fin, \"database-node\" olarak i\u015faretlenmi\u015f bir d\u00fc\u011f\u00fcmde sadece veritaban\u0131 podlar\u0131n\u0131n \u00e7al\u0131\u015fmas\u0131n\u0131 sa\u011flamak.
\n* D\u00fc\u011f\u00fcm Bak\u0131m\u0131:<\/strong> Bir d\u00fc\u011f\u00fcm\u00fc bak\u0131ma al\u0131rken yeni podlar\u0131n o d\u00fc\u011f\u00fcme zamanlanmas\u0131n\u0131 engellemek veya mevcut podlar\u0131 tahliye etmek.
\n* Karma \u0130\u015f Y\u00fckleri:<\/strong> GPU'lu d\u00fc\u011f\u00fcmlerde sadece GPU gerektiren i\u015f y\u00fcklerinin \u00e7al\u0131\u015fmas\u0131n\u0131 sa\u011flamak, di\u011fer podlar\u0131n bu pahal\u0131 kaynaklar\u0131 kullanmas\u0131n\u0131 engellemek.<\/p>\nPod Affinity ve Anti-Affinity: Podlar Aras\u0131 \u0130li\u015fkileri Y\u00f6netmek<\/h3>\n
Node Affinity, podlar\u0131 d\u00fc\u011f\u00fcmlerle ili\u015fkilendirirken, Pod Affinity ve Anti-Affinity kurallar\u0131 podlar\u0131 *birbirleriyle* ili\u015fkilendirir. Bu sayede, uygulamalar\u0131n\u0131z\u0131n bile\u015fenlerini ayn\u0131 d\u00fc\u011f\u00fcmde veya farkl\u0131 d\u00fc\u011f\u00fcmlerde \u00e7al\u0131\u015fmaya zorlayabilirsiniz.<\/p>\n
Pod Affinity ile Birlikte \u00c7al\u0131\u015fma<\/h4>\n
Pod Affinity, belirli etiketlere sahip podlar\u0131n ayn\u0131 d\u00fc\u011f\u00fcmde \u00e7al\u0131\u015fmas\u0131n\u0131 sa\u011flar. Bu, y\u00fcksek bant geni\u015fli\u011fi gerektiren veya d\u00fc\u015f\u00fck gecikme s\u00fcresiyle ileti\u015fim kurmas\u0131 gereken uygulama bile\u015fenleri i\u00e7in idealdir. \u00d6rne\u011fin, bir \u00f6nbellek servisi ile bu \u00f6nbelle\u011fi kullanan bir mikroservisi ayn\u0131 d\u00fc\u011f\u00fcmde tutmak.<\/p>\n
\napiVersion: v1\nkind: Pod\nmetadata:\n name: web-app\nspec:\n containers:\n - name: web-container\n image: webapp:1.0\n affinity:\n podAffinity:\n requiredDuringSchedulingIgnoredDuringExecution:\n - labelSelector:\n matchLabels:\n app: cache-service\n topologyKey: \"kubernetes.io\/hostname\"\n<\/pre>\n<\/code>
\nYukar\u0131daki \u00f6rnekte, web-app<\/code> pod'u, app: cache-service<\/code> etiketine sahip bir pod'un \u00e7al\u0131\u015ft\u0131\u011f\u0131 bir d\u00fc\u011f\u00fcme yerle\u015ftirilmek zorundad\u0131r. topologyKey: \"kubernetes.io\/hostname\"<\/code> ifadesi, bu kural\u0131n d\u00fc\u011f\u00fcm baz\u0131nda uygulanaca\u011f\u0131n\u0131 belirtir.<\/p>\nPod Anti-Affinity ile Ayr\u0131\u015ft\u0131rma<\/h4>\n
Pod Anti-Affinity, belirli etiketlere sahip podlar\u0131n *farkl\u0131* d\u00fc\u011f\u00fcmlerde \u00e7al\u0131\u015fmas\u0131n\u0131 sa\u011flar. Bu, y\u00fcksek eri\u015filebilirlik ve hata tolerans\u0131 i\u00e7in kritik \u00f6neme sahiptir. Ayn\u0131 uygulaman\u0131n replikalar\u0131n\u0131 farkl\u0131 d\u00fc\u011f\u00fcmlere da\u011f\u0131tarak tek bir d\u00fc\u011f\u00fcm ar\u0131zas\u0131n\u0131n t\u00fcm uygulamay\u0131 etkilemesini \u00f6nleyebilirsiniz.<\/p>\n
\napiVersion: v1\nkind: Pod\nmetadata:\n name: my-app-replica-1\nspec:\n containers:\n - name: my-container\n image: myapp:1.0\n affinity:\n podAntiAffinity:\n requiredDuringSchedulingIgnoredDuringExecution:\n - labelSelector:\n matchLabels:\n app: my-app\n topologyKey: \"kubernetes.io\/hostname\"\n<\/pre>\n<\/code>
\nBu \u00f6rnek, app: my-app<\/code> etiketine sahip ba\u015fka bir pod'un \u00e7al\u0131\u015ft\u0131\u011f\u0131 bir d\u00fc\u011f\u00fcme my-app-replica-1<\/code> pod'unun yerle\u015ftirilmesini engeller. B\u00f6ylece, my-app<\/code> uygulamas\u0131n\u0131n t\u00fcm replikalar\u0131 farkl\u0131 d\u00fc\u011f\u00fcmlerde da\u011f\u0131t\u0131l\u0131r.<\/p>\nUygulama \u00d6rnekleri<\/h4>\n
* Y\u00fcksek Eri\u015filebilirlik:<\/strong> Bir veritaban\u0131 k\u00fcmesinin replikalar\u0131n\u0131 farkl\u0131 d\u00fc\u011f\u00fcmlere da\u011f\u0131tarak, bir d\u00fc\u011f\u00fcm ar\u0131zas\u0131 durumunda servisin kesintiye u\u011framamas\u0131n\u0131 sa\u011flamak.
\n* Performans Optimizasyonu:<\/strong> Bir mikroservis ile onun ba\u011f\u0131ml\u0131 oldu\u011fu bir mesaj kuyru\u011fu servisini ayn\u0131 d\u00fc\u011f\u00fcmde \u00e7al\u0131\u015ft\u0131rarak a\u011f gecikmesini azaltmak.
\n* Kaynak \u0130zolasyonu:<\/strong> Yo\u011fun kaynak kullanan podlar\u0131n farkl\u0131 d\u00fc\u011f\u00fcmlere da\u011f\u0131t\u0131lmas\u0131n\u0131 sa\u011flayarak d\u00fc\u011f\u00fcm a\u015f\u0131r\u0131 y\u00fcklenmesini \u00f6nlemek.<\/p>\nTopology Spread Constraints: Y\u00fcksek Eri\u015filebilirlik ve Da\u011f\u0131t\u0131m\u0131 Sa\u011flamak<\/h3>\n
Topology Spread Constraints, podlar\u0131n k\u00fcme i\u00e7indeki farkl\u0131 topoloji alanlar\u0131na (b\u00f6lgeler, b\u00f6lgeler, d\u00fc\u011f\u00fcmler vb.) dengeli bir \u015fekilde da\u011f\u0131t\u0131lmas\u0131n\u0131 sa\u011flayan g\u00fc\u00e7l\u00fc bir zamanlama \u00f6zelli\u011fidir. Bu, uygulaman\u0131z\u0131n hata tolerans\u0131n\u0131 art\u0131r\u0131r ve kaynak kullan\u0131m\u0131n\u0131 optimize eder.<\/p>\n
Constraint Tan\u0131mlama<\/h4>\n
Bir topologySpreadConstraints<\/code> objesi, podlar\u0131n nas\u0131l da\u011f\u0131t\u0131laca\u011f\u0131n\u0131 tan\u0131mlar. \u00d6nemli parametreleri \u015funlard\u0131r:
\n* maxSkew<\/code>: Hedef topoloji alan\u0131nda izin verilen maksimum dengesizlik.
\n* topologyKey<\/code>: Podlar\u0131n da\u011f\u0131t\u0131laca\u011f\u0131 topoloji alan\u0131 (\u00f6rne\u011fin, kubernetes.io\/hostname<\/code>, topology.kubernetes.io\/zone<\/code>).
\n* whenUnsatisfiable<\/code>: K\u0131s\u0131tlaman\u0131n kar\u015f\u0131lanamad\u0131\u011f\u0131 durumda ne yap\u0131laca\u011f\u0131 (DoNotSchedule<\/code> veya ScheduleAnyway<\/code>).
\n* labelSelector<\/code>: Da\u011f\u0131t\u0131lacak podlar\u0131 se\u00e7mek i\u00e7in kullan\u0131lan etiket se\u00e7ici.<\/p>\n\napiVersion: v1\nkind: Pod\nmetadata:\n name: my-app-pod\n labels:\n app: my-app\nspec:\n containers:\n - name: my-container\n image: myapp:1.0\n topologySpreadConstraints:\n - maxSkew: 1\n topologyKey: kubernetes.io\/hostname\n whenUnsatisfiable: DoNotSchedule\n labelSelector:\n matchLabels:\n app: my-app\n - maxSkew: 1\n topologyKey: topology.kubernetes.io\/zone\n whenUnsatisfiable: DoNotSchedule\n labelSelector:\n matchLabels:\n app: my-app\n<\/pre>\n<\/code>
\nBu \u00f6rnek, my-app<\/code> etiketine sahip podlar\u0131n hem d\u00fc\u011f\u00fcm baz\u0131nda (kubernetes.io\/hostname<\/code>) hem de b\u00f6lge baz\u0131nda (topology.kubernetes.io\/zone<\/code>) dengeli da\u011f\u0131t\u0131lmas\u0131n\u0131 sa\u011flar. maxSkew: 1<\/code> ile, herhangi bir topoloji alan\u0131ndaki pod say\u0131s\u0131 fark\u0131n\u0131n en fazla 1 olabilece\u011fi belirtilir.<\/p>\nmaxSkew<\/code> ve whenUnsatisfiable<\/code> Parametreleri<\/h4>\n* maxSkew<\/code><\/strong>: Bu de\u011fer, bir topoloji alan\u0131ndaki pod say\u0131s\u0131 ile di\u011fer topoloji alanlar\u0131ndaki pod say\u0131s\u0131 aras\u0131ndaki maksimum fark\u0131 belirler. \u00d6rne\u011fin, maxSkew: 1<\/code> ise, bir b\u00f6lgedeki pod say\u0131s\u0131 di\u011fer bir b\u00f6lgedeki pod say\u0131s\u0131ndan en fazla 1 fazla olabilir.
\n* whenUnsatisfiable<\/code><\/strong>:
\n * DoNotSchedule<\/code>: E\u011fer k\u0131s\u0131tlama kar\u015f\u0131lanam\u0131yorsa, pod zamanlanmaz. Bu, daha kat\u0131 bir da\u011f\u0131t\u0131m politikas\u0131 sa\u011flar.
\n * ScheduleAnyway<\/code>: E\u011fer k\u0131s\u0131tlama kar\u015f\u0131lanam\u0131yorsa bile pod zamanlan\u0131r. Bu, esneklik sa\u011flar ancak da\u011f\u0131t\u0131m dengesiz olabilir.<\/p>\nFarkl\u0131 Topoloji Alanlar\u0131nda Da\u011f\u0131t\u0131m<\/h4>\n
Topology Spread Constraints, sadece d\u00fc\u011f\u00fcm veya b\u00f6lge baz\u0131nda de\u011fil, ayn\u0131 zamanda \u00f6zel etiketlerle tan\u0131mlanm\u0131\u015f herhangi bir topoloji alan\u0131 \u00fczerinde de \u00e7al\u0131\u015fabilir. \u00d6rne\u011fin, bir k\u00fcme i\u00e7inde farkl\u0131 raf (rack) etiketleri tan\u0131mlayarak podlar\u0131 fiziksel raflara g\u00f6re da\u011f\u0131tabilirsiniz. Bu, bir raf ar\u0131zas\u0131 durumunda uygulaman\u0131n etkilenme riskini azalt\u0131r.<\/p>\n
Priority ve Preemption: Kritik \u0130\u015f Y\u00fcklerine \u00d6ncelik Vermek<\/h3>\n
Kubernetes k\u00fcmelerinde kaynaklar s\u0131n\u0131rl\u0131 oldu\u011funda, hangi podlar\u0131n daha \u00f6nemli oldu\u011funa karar vermek kritik hale gelir. Priority (\u00f6ncelik) ve Preemption (\u00f6n al\u0131m) mekanizmalar\u0131, kritik i\u015f y\u00fcklerinin her zaman \u00e7al\u0131\u015fmas\u0131n\u0131 sa\u011flamak i\u00e7in podlara \u00f6ncelik ataman\u0131za ve daha az \u00f6nemli podlar\u0131 tahliye etmenize olanak tan\u0131r.<\/p>\n
PriorityClass Olu\u015fturma<\/h4>\n
PriorityClass, podlara atanabilecek bir \u00f6ncelik seviyesi tan\u0131mlayan k\u00fcme \u00e7ap\u0131nda bir nesnedir. Y\u00fcksek bir value<\/code> daha y\u00fcksek bir \u00f6nceli\u011fi temsil eder.<\/p>\n\napiVersion: scheduling.k8s.io\/v1\nkind: PriorityClass\nmetadata:\n name: high-priority\nvalue: 1000000\nglobalDefault: false\ndescription: \"Bu PriorityClass, kritik servisler i\u00e7in kullan\u0131l\u0131r.\"\n---\napiVersion: scheduling.k8s.io\/v1\nkind: PriorityClass\nmetadata:\n name: low-priority\nvalue: 100\nglobalDefault: false\ndescription: \"Bu PriorityClass, arka plan i\u015fleri i\u00e7in kullan\u0131l\u0131r.\"\n<\/pre>\n<\/code><\/p>\n
Pod'lara \u00d6ncelik Atama<\/h4>\n
Olu\u015fturulan PriorityClass'lar\u0131 pod tan\u0131m\u0131n\u0131za priorityClassName<\/code> alan\u0131 ekleyerek podlara atayabilirsiniz.<\/p>\n\napiVersion: v1\nkind: Pod\nmetadata:\n name: critical-app\nspec:\n containers:\n - name: my-critical-container\n image: critical-app:1.0\n priorityClassName: high-priority\n<\/pre>\n<\/code>
\nhigh-priority<\/code> s\u0131n\u0131f\u0131na sahip bir pod, low-priority<\/code> s\u0131n\u0131f\u0131na sahip bir poddan daha \u00f6nce zamanlanmaya \u00e7al\u0131\u015f\u0131lacak ve gerekti\u011finde onu tahliye edebilecektir.<\/p>\nPreemption Mekanizmas\u0131<\/h4>\n
E\u011fer y\u00fcksek \u00f6ncelikli bir pod zamanlanmaya \u00e7al\u0131\u015f\u0131l\u0131rken k\u00fcmede yeterli kaynak yoksa, Kubernetes zamanlay\u0131c\u0131s\u0131 daha d\u00fc\u015f\u00fck \u00f6ncelikli podlar\u0131 tahliye (preempt) etmeye \u00e7al\u0131\u015f\u0131r. Zamanlay\u0131c\u0131, yeni podun s\u0131\u011fabilece\u011fi bir d\u00fc\u011f\u00fcm bulmak i\u00e7in en az say\u0131da d\u00fc\u015f\u00fck \u00f6ncelikli podu tahliye etmeye \u00e7al\u0131\u015f\u0131r. Bu, kritik servislerin her zaman \u00e7al\u0131\u015f\u0131r durumda kalmas\u0131n\u0131 garanti eder. Preemption, genellikle d\u00fc\u011f\u00fcmde \u00e7al\u0131\u015fan podlar\u0131n \u00f6nceli\u011fine g\u00f6re yap\u0131l\u0131r.<\/p>\n
\u00d6zel Zamanlay\u0131c\u0131lar ve Geni\u015fleticiler: Zamanlama Mant\u0131\u011f\u0131n\u0131 Ki\u015fiselle\u015ftirmek<\/h3>\n
Kubernetes'in varsay\u0131lan zamanlay\u0131c\u0131s\u0131 bir\u00e7ok senaryo i\u00e7in yeterli olsa da, bazen \u00e7ok \u00f6zel veya karma\u015f\u0131k zamanlama gereksinimleri ortaya \u00e7\u0131kabilir. Bu durumlarda, \u00f6zel zamanlay\u0131c\u0131lar veya zamanlay\u0131c\u0131 geni\u015fleticileri (scheduler extenders) devreye girer.<\/p>\n
Neden \u00d6zel Zamanlay\u0131c\u0131ya \u0130htiya\u00e7 Duyar\u0131z?<\/h4>\n
* \u00d6zel Donan\u0131m Entegrasyonu:<\/strong> GPU'lar, FPGA'lar veya di\u011fer \u00f6zel donan\u0131mlar i\u00e7in karma\u015f\u0131k kaynak y\u00f6netimi ve zamanlama algoritmalar\u0131.
\n* Maliyet Optimizasyonu:<\/strong> Farkl\u0131 bulut sa\u011flay\u0131c\u0131lar\u0131n\u0131n fiyatland\u0131rma modellerine g\u00f6re podlar\u0131 en uygun maliyetli d\u00fc\u011f\u00fcmlere yerle\u015ftirmek.
\n* Geli\u015fmi\u015f Kaynak Y\u00f6netimi:<\/strong> K\u00fcme d\u0131\u015f\u0131ndaki kaynaklar\u0131 (\u00f6rne\u011fin, d\u0131\u015f veritabanlar\u0131 veya lisans sunucular\u0131) dikkate alarak zamanlama yapmak.
\n* Geli\u015fmi\u015f Topoloji Fark\u0131ndal\u0131\u011f\u0131:<\/strong> Varsay\u0131lan topologyKey<\/code>'lerin \u00f6tesinde, \u00f6zel topoloji kurallar\u0131 uygulamak.
\n* Veri Yerelli\u011fi:<\/strong> Verilerin bulundu\u011fu depolama birimlerine yak\u0131n d\u00fc\u011f\u00fcmlere podlar\u0131 yerle\u015ftirmek.<\/p>\nScheduler Extender'lar<\/h4>\n
Scheduler Extender'lar, Kubernetes'in varsay\u0131lan zamanlay\u0131c\u0131s\u0131n\u0131 de\u011fi\u015ftirmeden, zamanlama s\u00fcrecine harici bir mant\u0131k eklemenin bir yoludur. Zamanlay\u0131c\u0131, bir podu zamanlamadan \u00f6nce veya sonra, harici bir HTTP servisine (extender) bir istek g\u00f6nderir. Extender, kendi filtreleme, \u00f6nceliklendirme veya ba\u011flama mant\u0131\u011f\u0131n\u0131 uygulayabilir ve zamanlay\u0131c\u0131ya geri bildirimde bulunabilir.<\/p>\n
\napiVersion: kubescheduler.config.k8s.io\/v1beta2\nkind: KubeSchedulerConfiguration\nextenders:\n - urlPrefix: \"http:\/\/my-scheduler-extender:8080\"\n filterVerb: \"filter\"\n prioritizeVerb: \"prioritize\"\n weight: 1\n enableHttps: false\n<\/pre>\n<\/code>
\nBu konfig\u00fcrasyon, zamanlay\u0131c\u0131n\u0131n belirli bir URL'deki harici bir servisi filtreleme ve \u00f6nceliklendirme ad\u0131mlar\u0131nda kullanmas\u0131n\u0131 sa\u011flar.<\/p>\n
Kendi Zamanlay\u0131c\u0131n\u0131z\u0131 Geli\u015ftirmek<\/h4>\n
En esnek se\u00e7enek, s\u0131f\u0131rdan kendi Kubernetes zamanlay\u0131c\u0131n\u0131z\u0131 geli\u015ftirmektir. Bu, Kubernetes API'si ile etkile\u015fime giren ve podlar\u0131 d\u00fc\u011f\u00fcmlere atayan \u00f6zel bir kontrol\u00f6r yazmak anlam\u0131na gelir. Kendi zamanlay\u0131c\u0131n\u0131z\u0131 kullanmak i\u00e7in pod tan\u0131m\u0131n\u0131za schedulerName<\/code> alan\u0131n\u0131 eklersiniz.<\/p>\n\napiVersion: v1\nkind: Pod\nmetadata:\n name: my-custom-scheduled-pod\nspec:\n schedulerName: my-custom-scheduler\n containers:\n - name: my-container\n image: nginx\n<\/pre>\n<\/code>
\nKendi zamanlay\u0131c\u0131n\u0131z\u0131 geli\u015ftirmek, y\u00fcksek d\u00fczeyde kontrol ve \u00f6zelle\u015ftirme sa\u011flar, ancak ayn\u0131 zamanda daha fazla geli\u015ftirme ve bak\u0131m \u00e7abas\u0131 gerektirir.<\/p>\n
Descheduler: K\u00fcme Kaynaklar\u0131n\u0131 Optimize Etmek<\/h3>\n
Kubernetes zamanlay\u0131c\u0131s\u0131, podlar\u0131 d\u00fc\u011f\u00fcmlere yerle\u015ftirme konusunda m\u00fckemmeldir, ancak podlar bir kez yerle\u015ftirildikten sonra genellikle orada kal\u0131rlar. D\u00fc\u011f\u00fcm durumu de\u011fi\u015ftik\u00e7e (\u00f6rne\u011fin, yeni d\u00fc\u011f\u00fcmler eklendi\u011finde, kaynak kullan\u0131m\u0131 dengesizle\u015fti\u011finde), k\u00fcme optimum da\u011f\u0131t\u0131m\u0131n\u0131 kaybedebilir. Descheduler, bu sorunu \u00e7\u00f6zmek i\u00e7in tasarlanm\u0131\u015f bir ara\u00e7t\u0131r.<\/p>\n
Descheduler Nedir ve Nas\u0131l \u00c7al\u0131\u015f\u0131r?<\/h4>\n
Descheduler, halihaz\u0131rda \u00e7al\u0131\u015fan podlar\u0131 analiz eder ve belirli kurallara g\u00f6re optimum olmayan yerle\u015fimleri tespit eder. Ard\u0131ndan, bu podlar\u0131 tahliye ederek (evict ederek) Kubernetes zamanlay\u0131c\u0131s\u0131n\u0131n onlar\u0131 daha uygun bir d\u00fc\u011f\u00fcme yeniden zamanlamas\u0131n\u0131 sa\u011flar. Descheduler, zamanlay\u0131c\u0131 gibi s\u00fcrekli \u00e7al\u0131\u015fmak yerine, genellikle periyodik olarak \u00e7al\u0131\u015ft\u0131r\u0131lan bir kontrol\u00f6rd\u00fcr.<\/p>\n
Descheduler, genellikle bir Deployment olarak k\u00fcmede \u00e7al\u0131\u015f\u0131r ve belirli aral\u0131klarla tetiklenir.<\/p>\n
Stratejiler ve Konfig\u00fcrasyon<\/h4>\n
Descheduler, farkl\u0131 tahliye stratejilerini destekler:
\n* RemoveDuplicates<\/code><\/strong>: Ayn\u0131 d\u00fc\u011f\u00fcmde birden fazla replikas\u0131 olan podlar\u0131 tahliye eder.
\n* LowNodeUtilization<\/code><\/strong>: D\u00fc\u015f\u00fck kaynak kullan\u0131m\u0131na sahip d\u00fc\u011f\u00fcmlerdeki podlar\u0131 tahliye ederek bu d\u00fc\u011f\u00fcmlerin bo\u015falt\u0131lmas\u0131n\u0131 ve k\u00fc\u00e7\u00fclt\u00fclmesini sa\u011flar.
\n* HighNodeUtilization<\/code><\/strong>: Y\u00fcksek kaynak kullan\u0131m\u0131na sahip d\u00fc\u011f\u00fcmlerdeki podlar\u0131 tahliye ederek y\u00fck\u00fc dengelemeye \u00e7al\u0131\u015f\u0131r.
\n* PodLifeTime<\/code><\/strong>: Belirli bir \u00f6mr\u00fc a\u015fan podlar\u0131 tahliye eder (\u00f6rne\u011fin, \u00e7ok uzun s\u00fcredir \u00e7al\u0131\u015fan podlar\u0131 yeniden ba\u015flatmak i\u00e7in).
\n* NodeAffinity<\/code> \/ Taints<\/code> \/ Labels<\/code><\/strong>: Art\u0131k node affinity kurallar\u0131na uymayan, taints'i tolere etmeyen veya etiketleri de\u011fi\u015fen d\u00fc\u011f\u00fcmlerdeki podlar\u0131 tahliye eder.<\/p>\nDescheduler'\u0131n konfig\u00fcrasyonu, hangi stratejilerin kullan\u0131laca\u011f\u0131n\u0131 ve hangi podlar\u0131n tahliye edilece\u011fini belirler.<\/p>\n
\napiVersion: apps\/v1\nkind: Deployment\nmetadata:\n name: descheduler\nspec:\n template:\n spec:\n containers:\n - name: descheduler\n image: registry.k8s.io\/descheduler\/descheduler:v0.29.0\n args:\n - --policy-config-file=\/policy\/config.yaml\n - --v=3\n volumeMounts:\n - name: policy-volume\n mountPath: \/policy\n volumes:\n - name: policy-volume\n configMap:\n name: descheduler-policy-config\n---\napiVersion: v1\nkind: ConfigMap\nmetadata:\n name: descheduler-policy-config\ndata:\n config.yaml: |\n apiVersion: descheduler\/v1alpha1\n kind: DeschedulerPolicy\n strategies:\n \"RemoveDuplicates\":\n enabled: true\n \"LowNodeUtilization\":\n enabled: true\n params:\n nodeResourceUtilizationThreshold: 20\n targetThresholds:\n cpu: 50\n memory: 50\n<\/pre>\n<\/code><\/p>\n
K\u00fcme Dengesi \u0130\u00e7in \u00d6nemi<\/h4>\n
Descheduler, k\u00fcme kaynaklar\u0131n\u0131n zaman i\u00e7inde optimum \u015fekilde kullan\u0131lmas\u0131n\u0131 sa\u011flayarak maliyetleri d\u00fc\u015f\u00fcrebilir ve performans\u0131 art\u0131rabilir. \u00d6zellikle dinamik ve de\u011fi\u015fken i\u015f y\u00fcklerine sahip k\u00fcmelerde, d\u00fc\u011f\u00fcmlerin a\u015f\u0131r\u0131 y\u00fcklenmesini veya yetersiz kullan\u0131lmas\u0131n\u0131 \u00f6nlemek i\u00e7in vazge\u00e7ilmez bir ara\u00e7t\u0131r.<\/p>\n
Sonu\u00e7<\/h3>\n
Kubernetes'in varsay\u0131lan zamanlay\u0131c\u0131s\u0131, \u00e7o\u011fu senaryo i\u00e7in harika bir ba\u015flang\u0131\u00e7 noktas\u0131 sunsa da, ileri seviye zamanlama \u00f6zellikleri uygulamalar\u0131n\u0131z\u0131n da\u011f\u0131t\u0131m\u0131n\u0131 ve performans\u0131n\u0131 bir sonraki seviyeye ta\u015f\u0131yabilir. Node Affinity, Taints ve Tolerations, Pod Affinity\/Anti-Affinity, Topology Spread Constraints, Priority ve Preemption gibi mekanizmalar, pod yerle\u015fimini hassas bir \u015fekilde kontrol etmenizi sa\u011flar. Daha da \u00f6tesi, \u00f6zel zamanlay\u0131c\u0131lar ve Descheduler gibi ara\u00e7lar, en karma\u015f\u0131k senaryolar i\u00e7in bile esneklik ve optimizasyon sunar. Bu \"gizli cevherleri\" ustaca kullanarak, Kubernetes k\u00fcmenizin potansiyelini tam olarak ortaya \u00e7\u0131karabilir, uygulamalar\u0131n\u0131z\u0131n y\u00fcksek eri\u015filebilirli\u011fini, performans\u0131n\u0131 ve maliyet etkinli\u011fini art\u0131rabilirsiniz.<\/p>\n
SSS (S\u0131k Sorulan Sorular)<\/h3>\nNeden varsay\u0131lan Kubernetes zamanlay\u0131c\u0131s\u0131 her zaman yeterli de\u011fildir?<\/h4>\n
Varsay\u0131lan zamanlay\u0131c\u0131, genel ama\u00e7l\u0131 ve adil bir da\u011f\u0131t\u0131m sa\u011flamaya odaklan\u0131r. Ancak, belirli donan\u0131m gereksinimleri, uygulama bile\u015fenleri aras\u0131ndaki s\u0131k\u0131 ba\u011flant\u0131lar, maliyet optimizasyonu, y\u00fcksek eri\u015filebilirlik i\u00e7in \u00f6zel da\u011f\u0131t\u0131m desenleri veya karma\u015f\u0131k kurumsal politikalar gibi \u00f6zel durumlar i\u00e7in daha ince ayarl\u0131 kontrollere ihtiya\u00e7 duyulabilir.<\/p>\n
Hangi durumda \u00f6zel bir zamanlay\u0131c\u0131 kullanmal\u0131y\u0131m?<\/h4>\n
E\u011fer Node Affinity, Taints\/Tolerations, Pod Affinity\/Anti-Affinity, Topology Spread Constraints veya Priority\/Preemption gibi yerle\u015fik mekanizmalar gereksinimlerinizi kar\u015f\u0131lam\u0131yorsa, \u00f6zel bir zamanlay\u0131c\u0131 d\u00fc\u015f\u00fcnmelisiniz. Bu genellikle \u00e7ok spesifik donan\u0131m entegrasyonu, karma\u015f\u0131k d\u0131\u015f sistem ba\u011f\u0131ml\u0131l\u0131klar\u0131 veya benzersiz maliyet\/performans optimizasyon algoritmalar\u0131 gerektiren durumlarda ge\u00e7erlidir.<\/p>\n
Taints ve Tolerations ile Node Affinity aras\u0131ndaki temel fark nedir?<\/h4>\n
Node Affinity, bir pod'un *hangi d\u00fc\u011f\u00fcme* gitmesi gerekti\u011fini belirtir (\u00e7ekme). Taints ve Tolerations ise bir d\u00fc\u011f\u00fcm\u00fcn *hangi podlar\u0131* kabul etmeyece\u011fini belirtir (itme). Node Affinity, pod'u belirli bir d\u00fc\u011f\u00fcm grubuna \u00e7ekerken, Taints d\u00fc\u011f\u00fcm\u00fc belirli pod gruplar\u0131ndan uzak tutar. Genellikle, ayr\u0131lm\u0131\u015f d\u00fc\u011f\u00fcmler olu\u015fturmak i\u00e7in Taints ve Tolerations birlikte kullan\u0131l\u0131r.<\/p>\n
Descheduler'\u0131 ne s\u0131kl\u0131kla \u00e7al\u0131\u015ft\u0131rmal\u0131y\u0131m?<\/h4>\n
Descheduler'\u0131n \u00e7al\u0131\u015fma s\u0131kl\u0131\u011f\u0131, k\u00fcmenizin dinamikli\u011fine ve i\u015f y\u00fcklerinizin de\u011fi\u015fim h\u0131z\u0131na ba\u011fl\u0131d\u0131r. Genellikle, saatte bir veya birka\u00e7 saatte bir \u00e7al\u0131\u015ft\u0131rmak yeterli olabilir. \u00c7ok dinamik k\u00fcmelerde daha s\u0131k (\u00f6rne\u011fin 30 dakikada bir), daha statik k\u00fcmelerde ise daha seyrek (\u00f6rne\u011fin g\u00fcnde bir) \u00e7al\u0131\u015ft\u0131r\u0131labilir. A\u015f\u0131r\u0131 s\u0131k \u00e7al\u0131\u015ft\u0131rmak, gereksiz pod tahliyelerine ve k\u00fcme istikrars\u0131zl\u0131\u011f\u0131na yol a\u00e7abilir.<\/p>\n
Topology Spread Constraints'in performansa etkisi var m\u0131?<\/h4>\n
Evet, Topology Spread Constraints kullanmak, zamanlay\u0131c\u0131n\u0131n daha karma\u015f\u0131k hesaplamalar yapmas\u0131n\u0131 gerektirdi\u011fi i\u00e7in pod zamanlama s\u00fcresini bir miktar art\u0131rabilir. Ancak, bu etki genellikle k\u00fcme boyutuna ve k\u0131s\u0131tlama say\u0131s\u0131na ba\u011fl\u0131d\u0131r ve \u00e7o\u011fu durumda ihmal edilebilir d\u00fczeydedir. As\u0131l faydas\u0131, uygulaman\u0131z\u0131n y\u00fcksek eri\u015filebilirli\u011fini ve hata tolerans\u0131n\u0131 art\u0131rarak genel sistem performans\u0131na ve g\u00fcvenilirli\u011fine olumlu katk\u0131 sa\u011flamas\u0131d\u0131r.<\/p>\n","protected":false},"excerpt":{"rendered":"Kubernetes \u0130leri Seviye Zamanlama: Geli\u015fmi\u015f Pod Da\u011f\u0131t\u0131m\u0131 ve Optimizasyon S\u0131rlar\u0131\nKubernetes, da\u011f\u0131t\u0131k sistemlerin y\u00f6netiminde devrim yaratan bir…","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-39158","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-kubernetes","7":"cs-entry","8":"cs-video-wrap"},"yoast_head":"\n
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