Resource orchestration optimization of kubernetes cluster in large scale Patrickxie ( 谢谆志） Background Cloud has been the general trend. How to manage so many clusters ,resources and businesses and each HPA is individually configurable HPA2 HPAn CronHPA 07-28 10:00 07-28 11:00 Scale up to 100 Scale down to 3 How do CronHPA work with HPA 07-28 10:00 07-28 11:00 CronHPA takes over the takes over the work If workload still high load and HPA maxReplicas > CronHPA replicas CronHPA scale up How to solve the problem of improper resource requests Node Resource Oversold Node resource

Story Requirements Do not reinvent the wheel Not modify app under test Cross-platform tests Scale Appium Open source mobile testing framework Not modify app under test Use language of choice

Receive Senders Senders Receive Receivers Perf Gaps: CAS(Compare And Swap) can't perfectly scale with core count. Tokio Channel • Each worker has own run queue • Steal when own run queue is

users is the force to drive this network to evolve. ➔ So what we should keep doing is to develop the scale of the end users. ➔ More users, more data, more meanings. ➔ The incoming depends on consumer traffics

Stack) Kafka Do it yourself © 2018 Percona. 11 Founder and CEO of HoneyComb Operations at Scale Expert The Biggest “Observability through Event Analyzes Cheerleader” Meet Charity Majors © 2018

• Avoids potential confusion41 Continuous Integration • Scripts prepare Azure Virtual Machine Scale Sets • Currently up to 12 VMs, each with 16 cores • VMs install VS (with Clang, CMake, Ninja), Python