## VMware SIG Deep Dive into Kubernetes Scheduling Performance and high availability options for vSphere Steve Wong, Michael Gasch KubeCon North America December 13, 2018 ## Presenter Bios ## Steve options, for both control plane and worker nodes. 2 levels of scheduling and resource management are active. Currently no automatic scheduling integration occurs, that is, Kubernetes is not aware of the distribution running on the vSphere stack. Kubernetes default scheduling How it works ## Agenda ## Utilizing Zones to improve scheduling Using vSphere tags to define regions and zones – add cloud provider

# 动态资源管理和容器技术 在金融行业的架构探索和明天 ## Resource Scheduling & Container Technology for Financial Service 余军 Gopher China 2015 ## 关于我 ☐ ~19y+ Linux & opensource experience ☐ Co-founder of Shanghai 成时间的主机，不同的是Max-Min算法从所有任务的最小完成时间中选取一个最大值，然后进行相应任务。主机映射，之后重复此过程直至待调度任务集合为空。 ⑥ 轮询调度（Round Robin Scheduling）算法就是以轮询的方式依次将请求调度不同的服务器，即每次调度执行 $ i=(i+1)\mod n $ ，并选出第i台服务器。轮叫调度算法假设所有服务器处理性能均相同，不管服务器的当前连接数和

### TiDB 2.1 ## PD/TiKV (1/2) • Version Control • PreVote ○ PD ○ TiKV • Raft Learner • Hotspot scheduling • Asynchronous log ## PD/TiKV (2/2) • Fix DST issue • Batch Split, Fast Split ## SQL Layer /p12_2.jpg) 2.1.0-RC5 is shipped. 2.1.0-GA is not far away. ## What's Next? ## PD • Hotspot Scheduling ☐ Collecting Information ☐ Auto balance/split hot region • Powerful replica strategy ☐

catch_up to False. This will make those schedules run only once when the cluster starts and normal scheduling resumes. Defaults to True. #### 1.2.12 redis Connection settings for Redis. Defaults: # redis

catch_up to False. This will make those schedules run only once when the cluster starts and normal scheduling resumes. Defaults to True. #### 1.2.12 redis Connection settings for Redis. Defaults: # redis

actors (2) libprocess (3) revisiting the problem (4) evolution of libprocess (5) eventuals (6) scheduling (7) streams (8) type erasure ## chapters (1) motivating futures/promises + actors (2) libprocess libprocess (3) revisiting the problem (4) evolution of libprocess (5) eventuals (6) scheduling (7) streams (8) type erasure key challenges we’ve faced building distributed systems with good performance actors (2) libprocess (3) revisiting the problem (4) evolution of libprocess (5) eventuals (6) scheduling (7) streams (8) type erasure ## chapters (1) motivating futures/promises + actors (2) libprocess

Resources 87 6.2.2. Creating Email Content by Using a Templating Library 89 7. Task Execution and Scheduling 90 7.1. The Spring TaskExecutor Abstraction 90 7.1.1. TaskExecutor Types 90 7.1.2. Using 3. TaskScheduler implementations 94 7.3. Annotation Support for Scheduling and Asynchronous Execution 95 7.3.1. Enable Scheduling Annotations 95 7.3.2. The @Scheduled annotation 96 7.3.3. The @Async quite easy to do. ### Chapter 7. Task Execution and Scheduling The Spring Framework provides abstractions for the asynchronous execution and scheduling of tasks with the TaskExecutor and TaskScheduler interfaces

Experimental Features ..... 57 2.4.1 Performance ..... 57 2.4.2 Stability ..... 57 2.4.3 Scheduling ..... 57 2.4.4 SQL ..... 57 2.4.5 Configuration management ..... 58 2.4.6 Data sharing and examples 456 8.6.8 Fields description 458 8.7 Troubleshoot Hotspot Issues.....461 8.7.1 Common hotspots.....461 8.7.2 Identify hotspot issues.....462 8.7.3 Use SHARD_ROW_ID_BITS to process hotspots hotspots.....466 8.7.4 Handle auto-increment primary key hotspot tables using AUTO_RANDOM.....469 8.7.5 Optimization of small table hotspots.....472 8.8 Troubleshoot Increased Read and Write Latency

tools 48 2.4 TiDB Experimental Features 49 2.4.1 Performance 49 2.4.2 Stability 49 2.4.3 Scheduling 49 2.4.4 SQL 50 2.4.5 Configuration management 50 2.4.6 Data sharing and subscription 50 421 8.6.8 Fields description ..... 422 8.7 Troubleshoot Hotspot Issues ..... 425 8.7.1 Common hotspots ..... 425 8.7.2 Identify hotspot issues ..... 426 8.7.3 Use SHARD_ROW_ID_BITS to process hotspots hotspots ..... 430 8.7.4 Handle auto-increment primary key hotspot tables using AUTO_RANDOM ..... 433 8.7.5 Optimization of small table hotspots ..... 436 8.8 Troubleshoot Increased Read and Write

Experimental Features ..... 48 2.4.1 Performance ..... 48 2.4.2 Stability ..... 48 2.4.3 Scheduling ..... 48 2.4.4 SQL ..... 48 2.4.5 Configuration management ..... 49 2.4.6 Data sharing and Troubleshoot Hotspot Issues 441 8.7.1 Common hotspots 441 8.7.2 Identify hotspot issues 442 8.7.3 Use SHARD_ROW_ID_BITS to process hotspots 446 8.7.4 Handle auto-increment primary key hotspot tables Practices ..... 742 10.5.5 Best Practices for Monitoring TiDB Using Grafana ..... 753 10.5.6 PD Scheduling Best Practices ..... 764 10.5.7 Best Practices for TiKV Performance Tuning with Massive Regions