 K8S安装部署开放服务spec: volumes: - name: host-time hostPath: path: "/etc/localtime" volumeMounts: - name: host-time mountPath: "/etc/localtime" volumes: - name: host-time hostPath: path: "/etc/localtime" volumeMounts: //注意有多个! - name: host-time mountPath: "/etc/localtime" volumes: - name: host-time hostPath: path: "/etc/localtime" volumeMounts: //注意有 5 处 - name: host-time mountPath: "/etc/localtime"0 码力 | 54 页 | 1.23 MB | 1 年前3 K8S安装部署开放服务spec: volumes: - name: host-time hostPath: path: "/etc/localtime" volumeMounts: - name: host-time mountPath: "/etc/localtime" volumes: - name: host-time hostPath: path: "/etc/localtime" volumeMounts: //注意有多个! - name: host-time mountPath: "/etc/localtime" volumes: - name: host-time hostPath: path: "/etc/localtime" volumeMounts: //注意有 5 处 - name: host-time mountPath: "/etc/localtime"0 码力 | 54 页 | 1.23 MB | 1 年前3
 Advancing the Tactical Edge with K3s and SUSE RGSinnova- tive edge computing solution, SmartEdge, addresses the increasing need to gather data in real time and perform analysis at the point of collection, supplying imme- diate insight which results in faster as battlefields. The an- alytics enabled and performed by Smart- Edge allow battalions to make real-time, data-driven decisions which dramatically improve operational outcomes and in- crease the probability battlefield, “At the tactical edge, time is a weapon. With edge computing and pro- cessing at the point of data collection, we will give warfighters access to real-time, data-driven insights so they can0 码力 | 8 页 | 888.26 KB | 1 年前3 Advancing the Tactical Edge with K3s and SUSE RGSinnova- tive edge computing solution, SmartEdge, addresses the increasing need to gather data in real time and perform analysis at the point of collection, supplying imme- diate insight which results in faster as battlefields. The an- alytics enabled and performed by Smart- Edge allow battalions to make real-time, data-driven decisions which dramatically improve operational outcomes and in- crease the probability battlefield, “At the tactical edge, time is a weapon. With edge computing and pro- cessing at the point of data collection, we will give warfighters access to real-time, data-driven insights so they can0 码力 | 8 页 | 888.26 KB | 1 年前3
 秘钥管理秘钥Turtles all the way down - Securely managing Kubernetes Secretsconfigurations, API keys, and other small bits of information needed by applications at build or run time Why protect secrets? ● Attractive target ○ Controls access or use of sensitive resources ● Common compromised ○ Time available for attempts to penetrate physical, procedural, and logical access ○ Time available for computationally intensive cryptanalytic attacks ● A cryptoperiod is the time during which for keys that have reached the end of their cryptoperiod (for example, after a defined period of time has passed and/or after a certain amount of cipher-text has been produced by a given key) https://www0 码力 | 52 页 | 2.84 MB | 1 年前3 秘钥管理秘钥Turtles all the way down - Securely managing Kubernetes Secretsconfigurations, API keys, and other small bits of information needed by applications at build or run time Why protect secrets? ● Attractive target ○ Controls access or use of sensitive resources ● Common compromised ○ Time available for attempts to penetrate physical, procedural, and logical access ○ Time available for computationally intensive cryptanalytic attacks ● A cryptoperiod is the time during which for keys that have reached the end of their cryptoperiod (for example, after a defined period of time has passed and/or after a certain amount of cipher-text has been produced by a given key) https://www0 码力 | 52 页 | 2.84 MB | 1 年前3
 在大规模Kubernetes集群上实现高SLO的方法which can represent user experience. SLO is the object that try to meets all SLIs in a period of time. SLA = SLO + Punishment. SLA/SLO/SLI What we concern about Large k8s Cluster What happened about unhealthy nodes may not be delivered in time, success rate would decrease consequently. 4. Centralized Components Availability A ratio value indicates the time in which the cluster is available. It is master components. The success standard and reason classification The success standard: Pod Feature Time limit Success condition Pod RestartPolicy=Always 1min (example value) the status of {.Status.Conditions0 码力 | 11 页 | 4.01 MB | 1 年前3 在大规模Kubernetes集群上实现高SLO的方法which can represent user experience. SLO is the object that try to meets all SLIs in a period of time. SLA = SLO + Punishment. SLA/SLO/SLI What we concern about Large k8s Cluster What happened about unhealthy nodes may not be delivered in time, success rate would decrease consequently. 4. Centralized Components Availability A ratio value indicates the time in which the cluster is available. It is master components. The success standard and reason classification The success standard: Pod Feature Time limit Success condition Pod RestartPolicy=Always 1min (example value) the status of {.Status.Conditions0 码力 | 11 页 | 4.01 MB | 1 年前3
 绕过conntrack,使用eBPF增强 IPVS优化K8s网络性能mode • Services are organized in hash table • IPVS DNAT • conntrack/iptables SNAT • Pros • O(1) time complexity in control/data plane • Stably runs for two decades • Support rich scheduling algorithm differ • Performance of a cluster in different time slot may differ • Due to CPU oversold • Suggestion: • Run the test against the same cluster during near time • Make CPU the bottleneck • 1 CPU handles0 码力 | 24 页 | 1.90 MB | 1 年前3 绕过conntrack,使用eBPF增强 IPVS优化K8s网络性能mode • Services are organized in hash table • IPVS DNAT • conntrack/iptables SNAT • Pros • O(1) time complexity in control/data plane • Stably runs for two decades • Support rich scheduling algorithm differ • Performance of a cluster in different time slot may differ • Due to CPU oversold • Suggestion: • Run the test against the same cluster during near time • Make CPU the bottleneck • 1 CPU handles0 码力 | 24 页 | 1.90 MB | 1 年前3
 vmware组Kubernetes on vSphere Deep Dive KubeCon China VMware SIGQuotas Prioritization Isolation 18 Kubernetes built-in resource management Enforcement Run time enforcement at worker node level CPU “Compressible” = violation results in throttling Memory “Uncompressible” “Uncompressible” = violation triggers “death penalty” of Pod hosting container Scheduling time enforcement ResourceQuota admission controller will refuse to schedule a Pod that would violate limit After (Master) (Master) (Workers) (Worker) Thank You Questions? 22 remaining slides not presented to meet time constraints - included in published deck for reference 23 Configuring VM affinity rules Quorum dictates0 码力 | 25 页 | 2.22 MB | 1 年前3 vmware组Kubernetes on vSphere Deep Dive KubeCon China VMware SIGQuotas Prioritization Isolation 18 Kubernetes built-in resource management Enforcement Run time enforcement at worker node level CPU “Compressible” = violation results in throttling Memory “Uncompressible” “Uncompressible” = violation triggers “death penalty” of Pod hosting container Scheduling time enforcement ResourceQuota admission controller will refuse to schedule a Pod that would violate limit After (Master) (Master) (Workers) (Worker) Thank You Questions? 22 remaining slides not presented to meet time constraints - included in published deck for reference 23 Configuring VM affinity rules Quorum dictates0 码力 | 25 页 | 2.22 MB | 1 年前3
 VMware SIG Deep Dive into Kubernetes SchedulingQuotas Prioritization Isolation 18 Kubernetes built-in resource management Enforcement Run time enforcement at worker node level CPU “Compressible” = violation results in throttling Memory “Uncompressible” “Uncompressible” = violation triggers “death penalty” of Pod hosting container Scheduling time enforcement ResourceQuota admission controller will refuse to schedule a Pod that would violate limit After (Master) (Master) (Workers) (Worker) Thank You Questions? 22 remaining slides not presented to meet time constraints - included in published deck for reference 23 Open Issues (WIP) vSphere Cloud Provider0 码力 | 28 页 | 1.85 MB | 1 年前3 VMware SIG Deep Dive into Kubernetes SchedulingQuotas Prioritization Isolation 18 Kubernetes built-in resource management Enforcement Run time enforcement at worker node level CPU “Compressible” = violation results in throttling Memory “Uncompressible” “Uncompressible” = violation triggers “death penalty” of Pod hosting container Scheduling time enforcement ResourceQuota admission controller will refuse to schedule a Pod that would violate limit After (Master) (Master) (Workers) (Worker) Thank You Questions? 22 remaining slides not presented to meet time constraints - included in published deck for reference 23 Open Issues (WIP) vSphere Cloud Provider0 码力 | 28 页 | 1.85 MB | 1 年前3
 Jib Kubecon 2018 Talkbuild layer 1 layer 2 layer 3 total time push layer 4 github.com/GoogleContainerTools/jib Containerizing with Jib layer 1 layer 2 layer 3 build push total time layer 4 github.com/GoogleContainerTools/jib com/GoogleContainerTools/jib Containerizing with Jib (cached) layer 1 layer 2 layer 3 cached total time layer 4 cached cached github.com/GoogleContainerTools/jib Jib vs Docker github.com/GoogleContainerTools/jib0 码力 | 90 页 | 2.84 MB | 1 年前3 Jib Kubecon 2018 Talkbuild layer 1 layer 2 layer 3 total time push layer 4 github.com/GoogleContainerTools/jib Containerizing with Jib layer 1 layer 2 layer 3 build push total time layer 4 github.com/GoogleContainerTools/jib com/GoogleContainerTools/jib Containerizing with Jib (cached) layer 1 layer 2 layer 3 cached total time layer 4 cached cached github.com/GoogleContainerTools/jib Jib vs Docker github.com/GoogleContainerTools/jib0 码力 | 90 页 | 2.84 MB | 1 年前3
 Using Kubernetes for handling second screen experience of european tv showsigning up during commercial break. Show-time !! First row winner Second row winner Final winner Tease before commercial break End of last show Time 1 week - 1 hour 8 min 15 min 15 min 20min0 码力 | 28 页 | 3.86 MB | 1 年前3 Using Kubernetes for handling second screen experience of european tv showsigning up during commercial break. Show-time !! First row winner Second row winner Final winner Tease before commercial break End of last show Time 1 week - 1 hour 8 min 15 min 15 min 20min0 码力 | 28 页 | 3.86 MB | 1 年前3
 Kubernetes & YARN: a hybrid container cloud
spark, flink Latency Sensitive Insensitive Priority high low Traffic pattern Peak at day time Peak at night time Fault tolerance should not fail Fail and retry Complementary ! ���� ��������� ��������0 码力 | 42 页 | 25.48 MB | 1 年前3 Kubernetes & YARN: a hybrid container cloud
spark, flink Latency Sensitive Insensitive Priority high low Traffic pattern Peak at day time Peak at night time Fault tolerance should not fail Fail and retry Complementary ! ���� ��������� ��������0 码力 | 42 页 | 25.48 MB | 1 年前3
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