policy 5.5. Producer, consumer, and handlers 5.6. Listeners 6. Clustered resources 7. Cellar groups 7.1. New group 7.2. Clustered Resources and Cluster Groups 7.2.1. Features 7.2.2. Bundles 7.2.3. Configurations commands and MBeans to administrate the cluster, and manipulate the resources on the cluster. It’s also possible to enable local resources listeners: these listeners broadcast local resource changes as cluster stopped, it means that the node will receive the cluster event, but will not update the local resources dealt by the handler. 5.6. Listeners The listeners are listening for local resource change. For

policy 5.5. Producer, consumer, and handlers 5.6. Listeners 6. Clustered resources 7. Cellar groups 7.1. New group 7.2. Clustered Resources and Cluster Groups 7.2.1. Features 7.2.2. Bundles 7.2.3. Configurations commands and JMX MBeans to manage the cluster, and manipulate the resources on the cluster. It’s also possible to enable local resources listeners: these listeners broadcast local resource changes as cluster stopped, it means that the node will receive the cluster event, but will not update the local resources dealt by the handler. karaf@node1()> cluster:consumer-status | Node | Status -----

concept behind quantization. However, what happens if our and were outliers, and the real data was clustered in some smaller concentrated ranges? Quantization will still assign an equal number of precision num_quantization_bits): # num_elements = img.size return (num_elements * num_quantization_bits) / 8.0 def get_clustered_size_bytes(num_elements, num_clusters, floating_point_word_size=4): codebook_size_bytes = num_clusters elements, num_bits) clustered_size_bytes = get_clustered_size_bytes(num_elements, num_clusters) quant_vs_clustering_compression_ratio = (quantized_size_bytes * 1.0 / clustered_size_bytes) origina

Operand or configures off- cluster resources • Operator waits for managed resources to reach a healthy state • Operator conveys readiness of application or managed resources to the user leveraging the status Resource • Operator reconciles configuration and updates to it with the status of the managed resources Upgrade of the managed workload • Operand can be upgraded in the process of upgrading the Operator the Operand • Operator implements fail-over and fail-back of clustered Operands • Operator supports add/removing members to a clustered Operand • Operator enables application-aware scaling of the

Boot Environments Rapidly Portable Ability to Move Containers Freely Lightweight Minimal Resources Needed Application Operating System Physical Infrastructure Containers and VMs - A Practical kube-controller-manager kube-scheduler • Key/Value Store • Leader based clustering • Can be clustered across Master Nodes • Contains all state known about cluster • Kubernetes Front-end Control Plane

MBean 5.23. Working with profiles 5.24. Security & JAAS 5.25. Servlet 5.26. WAR 5.27. HTTP Resources 5.28. REST service 5.29. SOAP service 5.30. Websocket 5.31. Scheduler 5.32. Quick example with OSGi capabilities of a given bundles. bundle:classes Displays a list of classes/resources contained in the bundle bundle:diag Displays diagnostic information why a optionally a set of dependency features When you install a feature, Apache Karaf installs all resources described in the feature. It means that it will automatically resolve and install all bundles,

allows us to do updates with different deployment strategies and operate our edge devices in a clustered fashion. It really does support distributed processing across devices.” Ben Reif Lead Developer

Must-link or cannot-link constraints. Labels can always be converted to pairwise relations. Can be clustered by searching for partitioning that respect the con- straints Recently the trend is toward similarity-based

20-管理容器的计算资源 21-Kubernetes资源分配 22-将Pod分配到Node 23-容忍与污点 24-Secret 25-Pod优先级和抢占 26-Service 27-Ingress Resources 28-动态⽔平扩容 29-实战：使⽤K8s编排Wordpress博客 2 简介 Kubernetes开源书。不啰嗦了，JUST READ IT. GitHub地址：https://github Horizontal Pod Autoscaling Naming and discovering Balancing loads Rolling updates Monitoring resources Accessing and ingesting logs Debugging applications Providing authentication and authorization $GROUP_NAME/$VERSION （例如 apiVersion: batch/v1 ）。 ⽀持的API组的完整列表可详⻅：Kubernetes API reference 。 使⽤ custom resources 扩展API有两个⽀持的路径： 1. CustomResourceDefinition 适⽤于⾮常基本的CRUD需求的⽤户。 2. 即将推出：⽤户需要完整的Kubernetes API语法

long-running for a period, execute user’s queries from clients aperiodically, and the demand for computing resources is not the same for those queries. It is better for Spark to release some executors when either want to use the environment’s computing resources more cost-effectively and efficiently. Cluster managers such as K8S and Yarn manage the cluster compute resources, divided into different queues or namespaces we acquire computing resources from the cluster manager to submit the engines. The engines respond to various types of client requests, some of which consume many computing resources to process, while others