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Large-scale cluster management at Google with Borg

Apollo: Scalable and Coordinated Scheduling for Cloud-Scale Computing

Apache Hadoop YARN: Yet Another Resource Negotiator

Omega: flexible, scalable schedulers for large compute clusters

Mesos: A Platform for Fine-Grained Resource Sharing in the Data Center

Paxos Made Simple

Abstract
The Paxos algorithm, when presented in plain English, is very simple

Large-scale cluster management at Google with Borg

假期里读的一篇paper,描述了Borg怎么做集群管理。
简单易懂,但又没什么干货,不知道怎么做到支持10K这个数量级,这算是一个相当大的集群了。也不知道单点故障怎么转移,尤其是有状态服务。
调度器选择节点时,不会检查所有节点,先随机的选择一些节点,再在这个集合里面找满足条件的节点去 分配任务。
任务是用Paxos一致性存储算法,维护一个任务队列。
Borg是Master/Slave,要支持大量节点,Master的资源需求很大,10~14个cpu core, up to 50 GiB RAM。Master有五个副本,里面会选举leader。

Mesos: A Platform for Fine-Grained Resource Sharing in the Data Center

《容器即服务》中关于mesos的部分:
两级调度器,主从架构,Zookeeper做存储(Paxos算法,这个应该相当etcd),
虽然很早就支持了容器,但是之前的设计并未如容器那样独立(磁盘共享)
Marathon是其上一种流行服务框架。
服务框架指“运行在Mesos内核上的一种程序”。
看起来就像是两级调度的第二级,第一级是Mesos master。
可以自己编写服务框架,大体上有调度器和执行器就可以,但它也可以有web界面等一切其他组件。
Marathon读取外部配置,执行任务。(有点像kubelet)
Spark、Kafka、Aurora、Swan都可以作为服务框架使用。

相对来说,Mesos比较笨重,不够容器化,部署也烦琐。
但发展比较久,能统一多种计算框架。

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