Performance Tuning EC2 Instances
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Talk for AWS re:Invent 2014. Video: https://www.youtube.com/watch?v=7Cyd22kOqWc . Netflix tunes Amazon EC2 instances for maximum performance. In this session, you learn how Netflix configures the fastest possible EC2 instances, while reducing latency outliers. This session explores the various Xen modes (e.g., HVM, PV, etc.) and how they are optimized for different workloads. Hear how Netflix chooses Linux kernel versions based on desired performance characteristics and receive a firsthand look at how they set kernel tunables, including hugepages. You also hear about Netflix’s use of SR-IOV to enable enhanced networking and their approach to observability, which can exonerate EC2 issues and direct attention back to application performance.
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![$ sar -n TCP,ETCP,DEV 1
Linux 3.2.55 (test-e4f1a80b) 08/18/2014 _x86_64_ (8 CPU)
09:10:43 PM IFACE rxpck/s txpck/s rxkB/s txkB/s rxcmp/s txcmp/s rxmcst/s
09:10:44 PM lo 14.00 14.00 1.34 1.34 0.00 0.00 0.00
09:10:44 PM eth0 4114.00 4186.00 4537.46 28513.24 0.00 0.00 0.00
09:10:43 PM active/s passive/s iseg/s oseg/s
09:10:44 PM 21.00 4.00 4107.00 22511.00
09:10:43 PM atmptf/s estres/s retrans/s isegerr/s orsts/s
09:10:44 PM 0.00 0.00 36.00 0.00 1.00
[…]](https://image.slidesharecdn.com/awsreinvent2014perftuningec2-141112191859-conversion-gate02/85/Performance-Tuning-EC2-Instances-54-320.jpg)
![# ./iosnoop –ts
Tracing block I/O. Ctrl-C to end.
STARTs ENDs COMM PID TYPE DEV BLOCK BYTES LATms
5982800.302061 5982800.302679 supervise 1809 W 202,1 17039600 4096 0.62
5982800.302423 5982800.302842 supervise 1809 W 202,1 17039608 4096 0.42
5982800.304962 5982800.305446 supervise 1801 W 202,1 17039616 4096 0.48
5982800.305250 5982800.305676 supervise 1801 W 202,1 17039624 4096 0.43
[…]
# ./iosnoop –h
USAGE: iosnoop [-hQst] [-d device] [-i iotype] [-p PID] [-n name] [duration]
-d device # device string (eg, "202,1)
-i iotype # match type (eg, '*R*' for all reads)
-n name # process name to match on I/O issue
-p PID # PID to match on I/O issue
-Q # include queueing time in LATms
-s # include start time of I/O (s)
-t # include completion time of I/O (s)
[…]](https://image.slidesharecdn.com/awsreinvent2014perftuningec2-141112191859-conversion-gate02/85/Performance-Tuning-EC2-Instances-65-320.jpg)
![# perf record –e skb:consume_skb –ag -- sleep 10
# perf report
[...]
74.42% swapper [kernel.kallsyms] [k] consume_skb
|
--- consume_skb
arp_process
arp_rcv
__netif_receive_skb_core
__netif_receive_skb
netif_receive_skb
virtnet_poll
net_rx_action
__do_softirq
irq_exit
do_IRQ
ret_from_intr
[…]
Summarizing stack traces for a
tracepoint
perf_events can do many things,
it is hard to pick just one example](https://image.slidesharecdn.com/awsreinvent2014perftuningec2-141112191859-conversion-gate02/85/Performance-Tuning-EC2-Instances-67-320.jpg)
![ec2-guest# ./showboost
CPU MHz : 2500
Turbo MHz : 2900 (10 active)
Turbo Ratio : 116% (10 active)
CPU 0 summary every 5 seconds...
Real CPU MHz
TIME C0_MCYC C0_ACYC UTIL RATIO MHz
06:11:35 6428553166 7457384521 51% 116% 2900
06:11:40 6349881107 7365764152 50% 115% 2899
06:11:45 6240610655 7239046277 49% 115% 2899
[...]](https://image.slidesharecdn.com/awsreinvent2014perftuningec2-141112191859-conversion-gate02/85/Performance-Tuning-EC2-Instances-69-320.jpg)
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Performance Wins with eBPF: Getting Started (2021)
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Linux Performance Analysis: New Tools and Old Secrets
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The linux networking architecture
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It first describes the basic structure and layers of the Linux networking stack including the network device interface, network layer protocols like IP, transport layer, and sockets. It then discusses how network packets are managed in Linux through the use of socket buffers and associated functions. The document also provides an overview of the data link layer and protocols like Ethernet, PPP, and how they are implemented in Linux.
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Linux Networking ExplainedThomas Graf Linux offers an extensive selection of programmable and configurable networking components from traditional bridges, encryption, to container optimized layer 2/3 devices, link aggregation, tunneling, several classification and filtering languages all the way up to full SDN components. This talk will provide an overview of many Linux networking components covering the Linux bridge, IPVLAN, MACVLAN, MACVTAP, Bonding/Team, OVS, classification & queueing, tunnel types, hidden routing tricks, IPSec, VTI, VRF and many others.
LinuxCon 2015 Linux Kernel Networking Walkthrough
LinuxCon 2015 Linux Kernel Networking WalkthroughThomas Graf This presentation features a walk through the Linux kernel networking stack for users and developers. It will cover insights into both, existing essential networking features and recent developments and will show how to use them properly. Our starting point is the network card driver as it feeds a packet into the stack. We will follow the packet as it traverses through various subsystems such as packet filtering, routing, protocol stacks, and the socket layer. We will pause here and there to look into concepts such as networking namespaces, segmentation offloading, TCP small queues, and low latency polling and will discuss how to configure them.
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BPF - in-kernel virtual machineAlexei Starovoitov BPF of Berkeley Packet Filter mechanism was first introduced in linux in 1997 in version 2.1.75. It has seen a number of extensions of the years. Recently in versions 3.15 - 3.19 it received a major overhaul which drastically expanded it's applicability. This talk will cover how the instruction set looks today and why. It's architecture, capabilities, interface, just-in-time compilers. We will also talk about how it's being used in different areas of the kernel like tracing and networking and future plans.
BPF Internals (eBPF)
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[OpenInfra Days Korea 2018] (Track 4) - Grafana를 이용한 OpenStack 클라우드 성능 모니터링
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- 자료: https://www.slideshare.net/openstack_kr/openinfra-days-korea-2018-track-4-grafana-openstack
Linux Profiling at Netflix
Linux Profiling at NetflixBrendan Gregg This talk discusses Linux profiling using perf_events (also called "perf") based on Netflix's use of it. It covers how to use perf to get CPU profiling working and overcome common issues. The speaker will give a tour of perf_events features and show how Netflix uses it to analyze performance across their massive Amazon EC2 Linux cloud. They rely on tools like perf for customer satisfaction, cost optimization, and developing open source tools like NetflixOSS. Key aspects covered include why profiling is needed, a crash course on perf, CPU profiling workflows, and common "gotchas" to address like missing stacks, symbols, or profiling certain languages and events.
Linux Instrumentation
Linux InstrumentationDarkStarSword Numerous technologies exist for profiling and tracing live Linux systems - from the traditional and straight forward gProf and strace to the more elaborate SystemTap, oProfile and the Linux Trace Toolkit. Very recently some new technologies, perf events and ftrace, have appeared that can already largely take the place of these traditional tools and have gained mainline acceptance in the Linux community - meaning that they will become more and more relevant in the future and are already being used to shed light on real world performance issues.
This presentation provides an overview of a number of the more noteworthy instrumentation tools available for Linux and the technologies that they build upon. Some examples of using perf events to analyse a running system to help track down real world performance problems are demonstrated.
[KubeCon NA 2020] containerd: Rootless Containers 2020
[KubeCon NA 2020] containerd: Rootless Containers 2020Akihiro Suda Rootless Containers means running the container runtimes (e.g. runc, containerd, and kubelet) as well as the containers without the host root privileges. The most significant advantage of Rootless Containers is that it can mitigate potential container-breakout vulnerability of the runtimes, but it is also useful for isolating multi-user environments on HPC hosts. This talk will contain the introduction to rootless containers and deep-dive topics about the recent updates such as Seccomp User Notification. The main focus will be on containerd (CNCF Graduated Project) and its consumer projects including Kubernetes and Docker/Moby, but topics about other runtimes will be discussed as well.
https://sched.co/fGWc
Container Performance Analysis
Container Performance AnalysisBrendan Gregg The document summarizes a talk on container performance analysis. It discusses identifying bottlenecks at the host, container, and kernel level using various Linux performance tools. It then provides an overview of how containers work in Linux using namespaces and control groups (cgroups). Finally, it demonstrates some example commands like docker stats, systemd-cgtop, and bcc/BPF tools that can be used to analyze containers and cgroups from the host system.
Replacing iptables with eBPF in Kubernetes with Cilium
Replacing iptables with eBPF in Kubernetes with CiliumMichal Rostecki Cilium is an open source project which provides networking, security and load balancing for application services that are deployed using Linux container technologies by using the native eBPF technology in the Linux kernel. In this presentation we talked about:
- The evolution of the BPF filters and explained the advantages of eBPF Filters and its use cases today in Linux especially on how Cilium networking utilizes the eBPF Filters to secure the Kubernetes workload with increased performance when compared to legacy iptables.
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ネットワークの自動化・監視の取り組みについて #netopscoding #npstudy
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DPDK performance
How to not just do a demo with DPDK
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[OpenInfra Days Korea 2018] (Track 4) - Grafana를 이용한 OpenStack 클라우드 성능 모니터링
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Velocity 2015 linux perf tools
Velocity 2015 linux perf toolsBrendan Gregg Video: https://www.youtube.com/watch?v=FJW8nGV4jxY and https://www.youtube.com/watch?v=zrr2nUln9Kk . Tutorial slides for O'Reilly Velocity SC 2015, by Brendan Gregg.
There are many performance tools nowadays for Linux, but how do they all fit together, and when do we use them? This tutorial explains methodologies for using these tools, and provides a tour of four tool types: observability, benchmarking, tuning, and static tuning. Many tools will be discussed, including top, iostat, tcpdump, sar, perf_events, ftrace, SystemTap, sysdig, and others, as well observability frameworks in the Linux kernel: PMCs, tracepoints, kprobes, and uprobes.
This tutorial is updated and extended on an earlier talk that summarizes the Linux performance tool landscape. The value of this tutorial is not just learning that these tools exist and what they do, but hearing when and how they are used by a performance engineer to solve real world problems — important context that is typically not included in the standard documentation.
Velocity 2017 Performance analysis superpowers with Linux eBPF
Velocity 2017 Performance analysis superpowers with Linux eBPFBrendan Gregg Talk by for Velocity 2017 by Brendan Gregg: Performance analysis superpowers with Linux eBPF.
"Advanced performance observability and debugging have arrived built into the Linux 4.x series, thanks to enhancements to Berkeley Packet Filter (BPF, or eBPF) and the repurposing of its sandboxed virtual machine to provide programmatic capabilities to system tracing. Netflix has been investigating its use for new observability tools, monitoring, security uses, and more. This talk will investigate this new technology, which sooner or later will be available to everyone who uses Linux. The talk will dive deep on these new tracing, observability, and debugging capabilities. Whether you’re doing analysis over an ssh session, or via a monitoring GUI, BPF can be used to provide an efficient, custom, and deep level of detail into system and application performance.
This talk will also demonstrate the new open source tools that have been developed, which make use of kernel- and user-level dynamic tracing (kprobes and uprobes), and kernel- and user-level static tracing (tracepoints). These tools provide new insights for file system and storage performance, CPU scheduler performance, TCP performance, and a whole lot more. This is a major turning point for Linux systems engineering, as custom advanced performance instrumentation can be used safely in production environments, powering a new generation of tools and visualizations."
Blazing Performance with Flame Graphs
Blazing Performance with Flame GraphsBrendan Gregg Delivered as plenary at USENIX LISA 2013. video here: https://www.youtube.com/watch?v=nZfNehCzGdw and https://www.usenix.org/conference/lisa13/technical-sessions/plenary/gregg . "How did we ever analyze performance before Flame Graphs?" This new visualization invented by Brendan can help you quickly understand application and kernel performance, especially CPU usage, where stacks (call graphs) can be sampled and then visualized as an interactive flame graph. Flame Graphs are now used for a growing variety of targets: for applications and kernels on Linux, SmartOS, Mac OS X, and Windows; for languages including C, C++, node.js, ruby, and Lua; and in WebKit Web Inspector. This talk will explain them and provide use cases and new visualizations for other event types, including I/O, memory usage, and latency.
SREcon 2016 Performance Checklists for SREs
SREcon 2016 Performance Checklists for SREsBrendan Gregg Talk from SREcon2016 by Brendan Gregg. Video: https://www.usenix.org/conference/srecon16/program/presentation/gregg . "There's limited time for performance analysis in the emergency room. When there is a performance-related site outage, the SRE team must analyze and solve complex performance issues as quickly as possible, and under pressure. Many performance tools and techniques are designed for a different environment: an engineer analyzing their system over the course of hours or days, and given time to try dozens of tools: profilers, tracers, monitoring tools, benchmarks, as well as different tunings and configurations. But when Netflix is down, minutes matter, and there's little time for such traditional systems analysis. As with aviation emergencies, short checklists and quick procedures can be applied by the on-call SRE staff to help solve performance issues as quickly as possible.
In this talk, I'll cover a checklist for Linux performance analysis in 60 seconds, as well as other methodology-derived checklists and procedures for cloud computing, with examples of performance issues for context. Whether you are solving crises in the SRE war room, or just have limited time for performance engineering, these checklists and approaches should help you find some quick performance wins. Safe flying."
ACM Applicative System Methodology 2016
ACM Applicative System Methodology 2016Brendan Gregg Video: https://youtu.be/eO94l0aGLCA?t=3m37s . Talk by Brendan Gregg for ACM Applicative 2016
"System Methodology - Holistic Performance Analysis on Modern Systems
Traditional systems performance engineering makes do with vendor-supplied metrics, often involving interpretation and inference, and with numerous blind spots. Much in the field of systems performance is still living in the past: documentation, procedures, and analysis GUIs built upon the same old metrics. For modern systems, we can choose the metrics, and can choose ones we need to support new holistic performance analysis methodologies. These methodologies provide faster, more accurate, and more complete analysis, and can provide a starting point for unfamiliar systems.
Methodologies are especially helpful for modern applications and their workloads, which can pose extremely complex problems with no obvious starting point. There are also continuous deployment environments such as the Netflix cloud, where these problems must be solved in shorter time frames. Fortunately, with advances in system observability and tracers, we have virtually endless custom metrics to aid performance analysis. The problem becomes which metrics to use, and how to navigate them quickly to locate the root cause of problems.
System methodologies provide a starting point for analysis, as well as guidance for quickly moving through the metrics to root cause. They also pose questions that the existing metrics may not yet answer, which may be critical in solving the toughest problems. System methodologies include the USE method, workload characterization, drill-down analysis, off-CPU analysis, and more.
This talk will discuss various system performance issues, and the methodologies, tools, and processes used to solve them. The focus is on single systems (any operating system), including single cloud instances, and quickly locating performance issues or exonerating the system. Many methodologies will be discussed, along with recommendations for their implementation, which may be as documented checklists of tools, or custom dashboards of supporting metrics. In general, you will learn to think differently about your systems, and how to ask better questions."
Stop the Guessing: Performance Methodologies for Production Systems
Stop the Guessing: Performance Methodologies for Production SystemsBrendan Gregg Talk presented at Velocity 2013. Description: When faced with performance issues on complex production systems and distributed cloud environments, it can be difficult to know where to begin your analysis, or to spend much time on it when it isn’t your day job. This talk covers various methodologies, and anti-methodologies, for systems analysis, which serve as guidance for finding fruitful metrics from your current performance monitoring products. Such methodologies can help check all areas in an efficient manner, and find issues that can be easily overlooked, especially for virtualized environments which impose resource controls. Some of the tools and methodologies covered, including the USE Method, were developed by the speaker and have been used successfully in enterprise and cloud environments.
Netflix: From Clouds to Roots
Netflix: From Clouds to RootsBrendan Gregg Surge 2014: From Clouds to Roots: root cause performance analysis at Netflix. Brendan Gregg.
At Netflix, high scale and fast deployment rule. The possibilities for failure are endless, and the environment excels at handling this, regularly tested and exercised by the simian army. But, when this environment automatically works around systemic issues that aren’t root-caused, they can grow over time. This talk describes the challenge of not just handling failures of scale on the Netflix cloud, but also new approaches and tools for quickly diagnosing their root cause in an ever changing environment.
Linux BPF Superpowers
Linux BPF SuperpowersBrendan Gregg Video: https://www.facebook.com/atscaleevents/videos/1693888610884236/ . Talk by Brendan Gregg from Facebook's Performance @Scale: "Linux performance analysis has been the domain of ancient tools and metrics, but that's now changing in the Linux 4.x series. A new tracer is available in the mainline kernel, built from dynamic tracing (kprobes, uprobes) and enhanced BPF (Berkeley Packet Filter), aka, eBPF. It allows us to measure latency distributions for file system I/O and run queue latency, print details of storage device I/O and TCP retransmits, investigate blocked stack traces and memory leaks, and a whole lot more. These lead to performance wins large and small, especially when instrumenting areas that previously had zero visibility. This talk will summarize this new technology and some long-standing issues that it can solve, and how we intend to use it at Netflix."
Kernel Recipes 2017: Using Linux perf at Netflix
Kernel Recipes 2017: Using Linux perf at NetflixBrendan Gregg This document discusses using the Linux perf profiling tool at Netflix. It begins with an overview of why Netflix needs Linux profiling to understand CPU usage quickly and completely. It then provides an introduction to the perf tool, covering its basic workflow and commands. The document discusses profiling CPU usage with perf, including potential issues like JIT runtimes and missing symbols. It provides several examples of perf commands for listing, counting, and recording events. The overall summary is that perf allows Netflix to quickly and accurately profile CPU usage across the entire software stack, from applications to libraries to the kernel, to optimize performance.
Broken Linux Performance Tools 2016
Broken Linux Performance Tools 2016Brendan Gregg Broken benchmarks, misleading metrics, and terrible tools. This talk will help you navigate the treacherous waters of Linux performance tools, touring common problems with system tools, metrics, statistics, visualizations, measurement overhead, and benchmarks. You might discover that tools you have been using for years, are in fact, misleading, dangerous, or broken.
The speaker, Brendan Gregg, has given many talks on tools that work, including giving the Linux PerformanceTools talk originally at SCALE. This is an anti-version of that talk, to focus on broken tools and metrics instead of the working ones. Metrics can be misleading, and counters can be counter-intuitive! This talk will include advice for verifying new performance tools, understanding how they work, and using them successfully.
No data loss pipeline with apache kafka
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RxNetty vs Tomcat Performance Results
RxNetty vs Tomcat Performance ResultsBrendan Gregg A 2015 performance study by Brendan Gregg, Nitesh Kant, and Ben Christensen. Original is in https://github.com/Netflix-Skunkworks/WSPerfLab/tree/master/test-results
G1 Garbage Collector: Details and Tuning
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Am I reading GC logs Correctly?
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Troubleshooting PostgreSQL Streaming Replication
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Designing Tracing Tools
Designing Tracing ToolsBrendan Gregg Video: https://www.youtube.com/watch?v=uibLwoVKjec . Talk by Brendan Gregg for Sysdig CCWFS 2016. Abstract:
"You have a system with an advanced programmatic tracer: do you know what to do with it? Brendan has used numerous tracers in production environments, and has published hundreds of tracing-based tools. In this talk he will share tips and know-how for creating CLI tracing tools and GUI visualizations, to solve real problems effectively. Programmatic tracing is an amazing superpower, and this talk will show you how to wield it!"
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Linux 4.x Tracing Tools: Using BPF SuperpowersBrendan Gregg Talk for USENIX LISA 2016 by Brendan Gregg.
"Linux 4.x Tracing Tools: Using BPF Superpowers
The Linux 4.x series heralds a new era of Linux performance analysis, with the long-awaited integration of a programmable tracer: Enhanced BPF (eBPF). Formally the Berkeley Packet Filter, BPF has been enhanced in Linux to provide system tracing capabilities, and integrates with dynamic tracing (kprobes and uprobes) and static tracing (tracepoints and USDT). This has allowed dozens of new observability tools to be developed so far: for example, measuring latency distributions for file system I/O and run queue latency, printing details of storage device I/O and TCP retransmits, investigating blocked stack traces and memory leaks, and a whole lot more. These lead to performance wins large and small, especially when instrumenting areas that previously had zero visibility. Tracing superpowers have finally arrived.
In this talk I'll show you how to use BPF in the Linux 4.x series, and I'll summarize the different tools and front ends available, with a focus on iovisor bcc. bcc is an open source project to provide a Python front end for BPF, and comes with dozens of new observability tools (many of which I developed). These tools include new BPF versions of old classics, and many new tools, including: execsnoop, opensnoop, funccount, trace, biosnoop, bitesize, ext4slower, ext4dist, tcpconnect, tcpretrans, runqlat, offcputime, offwaketime, and many more. I'll also summarize use cases and some long-standing issues that can now be solved, and how we are using these capabilities at Netflix."
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YOW2021 Computing PerformanceBrendan Gregg This document provides a performance engineer's predictions for computing performance trends in 2021 and beyond. The engineer discusses trends in processors, memory, disks, networking, runtimes, kernels, hypervisors, and observability. For processors, predictions include multi-socket systems becoming less common, the future of simultaneous multithreading being unclear, practical core count limits being reached in the 2030s, and more processor vendors including ARM-based and RISC-V options. Memory predictions focus on many workloads being memory-bound currently.
IntelON 2021 Processor Benchmarking
IntelON 2021 Processor BenchmarkingBrendan Gregg The document discusses challenges with processor benchmarking and provides recommendations. It summarizes a case study where a popular CPU benchmark claimed a new processor was 2.6x faster than Intel, but detailed analysis found the benchmark was testing division speed, which accounted for only 0.1% of cycles on Netflix servers. The document advocates for low-level, active benchmarking and profiling over statistical analysis. It also provides a checklist for evaluating benchmarks and cautions that increased processor complexity and cloud environments make accurate benchmarking more difficult.
Computing Performance: On the Horizon (2021)
Computing Performance: On the Horizon (2021)Brendan Gregg Talk by Brendan Gregg for USENIX LISA 2021. https://www.youtube.com/watch?v=5nN1wjA_S30 . "The future of computer performance involves clouds with hardware hypervisors and custom processors, servers running a new type of BPF software to allow high-speed applications and kernel customizations, observability of everything in production, new Linux kernel technologies, and more. This talk covers interesting developments in systems and computing performance, their challenges, and where things are headed."
LPC2019 BPF Tracing Tools
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LSFMM 2019 BPF Observability
LSFMM 2019 BPF ObservabilityBrendan Gregg Here is a bpftrace program to measure scheduler latency for ICMP echo requests:
#!/usr/local/bin/bpftrace
kprobe:icmp_send {
@start[tid] = nsecs;
}
kprobe:__netif_receive_skb_core {
@diff[tid] = hist(nsecs - @start[tid]);
delete(@start[tid]);
}
END {
print(@diff);
clear(@diff);
}
This traces the time between the icmp_send kernel function (when the packet is queued for transmit) and the __netif_receive_skb_core function (when the response packet is received). The
YOW2018 CTO Summit: Working at netflix
YOW2018 CTO Summit: Working at netflixBrendan Gregg This document summarizes Brendan Gregg's experiences working at Netflix for over 4.5 years. Some key points include:
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- Gregg's first meeting involved an expected "intense debate" but was actually professional and respectful.
- Netflix values judgment, communication, curiosity, courage, and other traits that allow the culture and architecture to complement each other.
- The cloud architecture is designed to be resilient through practices like chaos engineering and rapid deployments without approvals, in line with the culture of freedom and responsibility.
eBPF Perf Tools 2019
eBPF Perf Tools 2019Brendan Gregg The document describes a biolatency tool that traces block device I/O latency using eBPF. It discusses how the tool was originally written in the bcc framework using C/BPF, but has since been rewritten in the bpftrace framework using a simpler one-liner script. It provides examples of the bcc and bpftrace implementations of biolatency.
YOW2018 Cloud Performance Root Cause Analysis at Netflix
YOW2018 Cloud Performance Root Cause Analysis at NetflixBrendan Gregg Keynote by Brendan Gregg for YOW! 2018. Video: https://www.youtube.com/watch?v=03EC8uA30Pw . Description: "At Netflix, improving the performance of our cloud means happier customers and lower costs, and involves root cause
analysis of applications, runtimes, operating systems, and hypervisors, in an environment of 150k cloud instances
that undergo numerous production changes each week. Apart from the developers who regularly optimize their own code
, we also have a dedicated performance team to help with any issue across the cloud, and to build tooling to aid in
this analysis. In this session we will summarize the Netflix environment, procedures, and tools we use and build t
o do root cause analysis on cloud performance issues. The analysis performed may be cloud-wide, using self-service
GUIs such as our open source Atlas tool, or focused on individual instances, and use our open source Vector tool, f
lame graphs, Java debuggers, and tooling that uses Linux perf, ftrace, and bcc/eBPF. You can use these open source
tools in the same way to find performance wins in your own environment."
BPF Tools 2017
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NetConf 2018 BPF ObservabilityBrendan Gregg Talk/discussion for NetConf 2018 on BPF Observability by Brendan Gregg. http://vger.kernel.org/netconf2018.html
FlameScope 2018
FlameScope 2018Brendan Gregg Talk by Brendan Gregg and Martin Spier for the Linkedin Performance Engineering meetup on Nov 8, 2018. FlameScope is a visualization for performance profiles that helps you study periodic activity, variance, and perturbations, with a heat map for navigation and flame graphs for code analysis.
Linux Performance 2018 (PerconaLive keynote)
Linux Performance 2018 (PerconaLive keynote)Brendan Gregg Keynote for PerconaLive 2018 by Brendan Gregg. Video: https://youtu.be/sV3XfrfjrPo?t=30m51s . "At over one thousand code commits per week, it's hard to keep up with Linux developments. This keynote will summarize recent Linux performance features, for a wide audience: the KPTI patches for Meltdown, eBPF for performance observability, Kyber for disk I/O scheduling, BBR for TCP congestion control, and more. This is about exposure: knowing what exists, so you can learn and use it later when needed. Get the most out of your systems, whether they are databases or application servers, with the latest Linux kernels and exciting features."
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How Netflix Tunes EC2 Instances for PerformanceBrendan Gregg CMP325 talk for AWS re:Invent 2017, by Brendan Gregg. "
At Netflix we make the best use of AWS EC2 instance types and features to create a high performance cloud, achieving near bare metal speed for our workloads. This session will summarize the configuration, tuning, and activities for delivering the fastest possible EC2 instances, and will help other EC2 users improve performance, reduce latency outliers, and make better use of EC2 features. We'll show how we choose EC2 instance types, how we choose between EC2 Xen modes: HVM, PV, and PVHVM, and the importance of EC2 features such SR-IOV for bare-metal performance. SR-IOV is used by EC2 enhanced networking, and recently for the new i3 instance type for enhanced disk performance as well. We'll also cover kernel tuning and observability tools, from basic to advanced. Advanced performance analysis includes the use of Java and Node.js flame graphs, and the new EC2 Performance Monitoring Counter (PMC) feature released this year."
LISA17 Container Performance Analysis
LISA17 Container Performance AnalysisBrendan Gregg Talk for USENIX LISA17: "Containers pose interesting challenges for performance monitoring and analysis, requiring new analysis methodologies and tooling. Resource-oriented analysis, as is common with systems performance tools and GUIs, must now account for both hardware limits and soft limits, as implemented using cgroups. A reverse diagnosis methodology can be applied to identify whether a container is resource constrained, and by which hard or soft resource. The interaction between the host and containers can also be examined, and noisy neighbors identified or exonerated. Performance tooling can need special usage or workarounds to function properly from within a container or on the host, to deal with different privilege levels and name spaces. At Netflix, we're using containers for some microservices, and care very much about analyzing and tuning our containers to be as fast and efficient as possible. This talk will show you how to identify bottlenecks in the host or container configuration, in the applications by profiling in a container environment, and how to dig deeper into kernel and container internals."
Kernel Recipes 2017: Performance Analysis with BPF
Kernel Recipes 2017: Performance Analysis with BPFBrendan Gregg Talk by Brendan Gregg at Kernel Recipes 2017 (Paris): "The in-kernel Berkeley Packet Filter (BPF) has been enhanced in recent kernels to do much more than just filtering packets. It can now run user-defined programs on events, such as on tracepoints, kprobes, uprobes, and perf_events, allowing advanced performance analysis tools to be created. These can be used in production as the BPF virtual machine is sandboxed and will reject unsafe code, and are already in use at Netflix.
Beginning with the bpf() syscall in 3.18, enhancements have been added in many kernel versions since, with major features for BPF analysis landing in Linux 4.1, 4.4, 4.7, and 4.9. Specific capabilities these provide include custom in-kernel summaries of metrics, custom latency measurements, and frequency counting kernel and user stack traces on events. One interesting case involves saving stack traces on wake up events, and associating them with the blocked stack trace: so that we can see the blocking stack trace and the waker together, merged in kernel by a BPF program (that particular example is in the kernel as samples/bpf/offwaketime).
This talk will discuss the new BPF capabilities for performance analysis and debugging, and demonstrate the new open source tools that have been developed to use it, many of which are in the Linux Foundation iovisor bcc (BPF Compiler Collection) project. These include tools to analyze the CPU scheduler, TCP performance, file system performance, block I/O, and more."
EuroBSDcon 2017 System Performance Analysis Methodologies
EuroBSDcon 2017 System Performance Analysis MethodologiesBrendan Gregg keynote by Brendan Gregg. "Traditional performance monitoring makes do with vendor-supplied metrics, often involving interpretation and inference, and with numerous blind spots. Much in the field of systems performance is still living in the past: documentation, procedures, and analysis GUIs built upon the same old metrics. Modern BSD has advanced tracers and PMC tools, providing virtually endless metrics to aid performance analysis. It's time we really used them, but the problem becomes which metrics to use, and how to navigate them quickly to locate the root cause of problems.
There's a new way to approach performance analysis that can guide you through the metrics. Instead of starting with traditional metrics and figuring out their use, you start with the questions you want answered then look for metrics to answer them. Methodologies can provide these questions, as well as a starting point for analysis and guidance for locating the root cause. They also pose questions that the existing metrics may not yet answer, which may be critical in solving the toughest problems. System methodologies include the USE method, workload characterization, drill-down analysis, off-CPU analysis, chain graphs, and more.
This talk will discuss various system performance issues, and the methodologies, tools, and processes used to solve them. Many methodologies will be discussed, from the production proven to the cutting edge, along with recommendations for their implementation on BSD systems. In general, you will learn to think differently about analyzing your systems, and make better use of the modern tools that BSD provides."
OSSNA 2017 Performance Analysis Superpowers with Linux BPF
OSSNA 2017 Performance Analysis Superpowers with Linux BPFBrendan Gregg Talk by Brendan Gregg for OSSNA 2017. "Advanced performance observability and debugging have arrived built into the Linux 4.x series, thanks to enhancements to Berkeley Packet Filter (BPF, or eBPF) and the repurposing of its sandboxed virtual machine to provide programmatic capabilities to system tracing. Netflix has been investigating its use for new observability tools, monitoring, security uses, and more. This talk will be a dive deep on these new tracing, observability, and debugging capabilities, which sooner or later will be available to everyone who uses Linux. Whether you’re doing analysis over an ssh session, or via a monitoring GUI, BPF can be used to provide an efficient, custom, and deep level of detail into system and application performance.
This talk will also demonstrate the new open source tools that have been developed, which make use of kernel- and user-level dynamic tracing (kprobes and uprobes), and kernel- and user-level static tracing (tracepoints). These tools provide new insights for file system and storage performance, CPU scheduler performance, TCP performance, and a whole lot more. This is a major turning point for Linux systems engineering, as custom advanced performance instrumentation can be used safely in production environments, powering a new generation of tools and visualizations."
USENIX ATC 2017 Performance Superpowers with Enhanced BPF
USENIX ATC 2017 Performance Superpowers with Enhanced BPFBrendan Gregg Talk for USENIX ATC 2017 by Brendan Gregg
"The Berkeley Packet Filter (BPF) in Linux has been enhanced in very recent versions to do much more than just filter packets, and has become a hot area of operating systems innovation, with much more yet to be discovered. BPF is a sandboxed virtual machine that runs user-level defined programs in kernel context, and is part of many kernels. The Linux enhancements allow it to run custom programs on other events, including kernel- and user-level dynamic tracing (kprobes and uprobes), static tracing (tracepoints), and hardware events. This is finding uses for the generation of new performance analysis tools, network acceleration technologies, and security intrusion detection systems.
This talk will explain the BPF enhancements, then discuss the new performance observability tools that are in use and being created, especially from the BPF compiler collection (bcc) open source project. These tools provide new insights for file system and storage performance, CPU scheduler performance, TCP performance, and much more. This is a major turning point for Linux systems engineering, as custom advanced performance instrumentation can be used safely in production environments, powering a new generation of tools and visualizations.
Because these BPF enhancements are only in very recent Linux (such as Linux 4.9), most companies are not yet running new enough kernels to be exploring BPF yet. This will change in the next year or two, as companies including Netflix upgrade their kernels. This talk will give you a head start on this growing technology, and also discuss areas of future work and unsolved problems."
USENIX ATC 2017: Visualizing Performance with Flame Graphs
USENIX ATC 2017: Visualizing Performance with Flame GraphsBrendan Gregg Talk by Brendan Gregg for USENIX ATC 2017.
"Flame graphs are a simple stack trace visualization that helps answer an everyday problem: how is software consuming resources, especially CPUs, and how did this change since the last software version? Flame graphs have been adopted by many languages, products, and companies, including Netflix, and have become a standard tool for performance analysis. They were published in "The Flame Graph" article in the June 2016 issue of Communications of the ACM, by their creator, Brendan Gregg.
This talk describes the background for this work, and the challenges encountered when profiling stack traces and resolving symbols for different languages, including for just-in-time compiler runtimes. Instructions will be included generating mixed-mode flame graphs on Linux, and examples from our use at Netflix with Java. Advanced flame graph types will be described, including differential, off-CPU, chain graphs, memory, and TCP events. Finally, future work and unsolved problems in this area will be discussed."
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https://alandix.com/academic/talks/CMet2025-AI-Changes-Everything/
Is AI just another technology, or does it fundamentally change the way we live and think?
Every technology has a direct impact with micro-ethical consequences, some good, some bad. However more profound are the ways in which some technologies reshape the very fabric of society with macro-ethical impacts. The invention of the stirrup revolutionised mounted combat, but as a side effect gave rise to the feudal system, which still shapes politics today. The internal combustion engine offers personal freedom and creates pollution, but has also transformed the nature of urban planning and international trade. When we look at AI the micro-ethical issues, such as bias, are most obvious, but the macro-ethical challenges may be greater.
At a micro-ethical level AI has the potential to deepen social, ethnic and gender bias, issues I have warned about since the early 1990s! It is also being used increasingly on the battlefield. However, it also offers amazing opportunities in health and educations, as the recent Nobel prizes for the developers of AlphaFold illustrate. More radically, the need to encode ethics acts as a mirror to surface essential ethical problems and conflicts.
At the macro-ethical level, by the early 2000s digital technology had already begun to undermine sovereignty (e.g. gambling), market economics (through network effects and emergent monopolies), and the very meaning of money. Modern AI is the child of big data, big computation and ultimately big business, intensifying the inherent tendency of digital technology to concentrate power. AI is already unravelling the fundamentals of the social, political and economic world around us, but this is a world that needs radical reimagining to overcome the global environmental and human challenges that confront us. Our challenge is whether to let the threads fall as they may, or to use them to weave a better future.
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Mobile App Development Company in Saudi ArabiaSteve Jonas EmizenTech is a globally recognized software development company, proudly serving businesses since 2013. With over 11+ years of industry experience and a team of 200+ skilled professionals, we have successfully delivered 1200+ projects across various sectors. As a leading Mobile App Development Company In Saudi Arabia we offer end-to-end solutions for iOS, Android, and cross-platform applications. Our apps are known for their user-friendly interfaces, scalability, high performance, and strong security features. We tailor each mobile application to meet the unique needs of different industries, ensuring a seamless user experience. EmizenTech is committed to turning your vision into a powerful digital product that drives growth, innovation, and long-term success in the competitive mobile landscape of Saudi Arabia.
Heap, Types of Heap, Insertion and Deletion
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What is Model Context Protocol(MCP) - The new technology for communication bw...
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Performance Tuning EC2 Instances
- 1. PFC306 Brendan Gregg, Performance Engineering, Netflix November 12, 2014 | Las Vegas, NV
- 9. S3 EC2 Cassandra Applications (Services) EVCache ELB Elasticsearch SES SQS
- 13. Start i2 Select memory to cache working set Find best balance
- 14. ASG-v011 … Instance Instance Instance ASG Cluster prod1 ASG-v010 … Instance Instance Instance Canary ELB
- 17. Select instance families Select resources From any desired resource, see types & cost
- 18. eg, 8 vCPU:
- 26. Cost per hour Services
- 36. # schedtool –B PID
- 37. vm.swappiness = 0 # from 60
- 38. # echo never > /sys/kernel/mm/transparent_hugepage/enabled # from madvise
- 39. vm.dirty_ratio = 80 # from 40 vm.dirty_background_ratio = 5 # from 10 vm.dirty_expire_centisecs = 12000 # from 3000 mount -o defaults,noatime,discard,nobarrier …
- 40. /sys/block/*/queue/rq_affinity2 /sys/block/*/queue/scheduler noop /sys/block/*/queue/nr_requests256 /sys/block/*/queue/read_ahead_kb 256 mdadm –chunk=64 ...
- 41. net.core.somaxconn = 1000 net.core.netdev_max_backlog = 5000 net.core.rmem_max = 16777216 net.core.wmem_max = 16777216 net.ipv4.tcp_wmem = 4096 12582912 16777216 net.ipv4.tcp_rmem = 4096 12582912 16777216 net.ipv4.tcp_max_syn_backlog = 8096 net.ipv4.tcp_slow_start_after_idle = 0 net.ipv4.tcp_tw_reuse = 1 net.ipv4.ip_local_port_range = 10240 65535 net.ipv4.tcp_abort_on_overflow = 1 # maybe
- 42. echo tsc > /sys/devices/system/clocksource/clocksource0/current_clocksource
- 47. Resource Utilization X (%)
- 51. Application System Libraries System Calls Kernel Devices
- 54. $ sar -n TCP,ETCP,DEV 1 Linux 3.2.55 (test-e4f1a80b) 08/18/2014 _x86_64_ (8 CPU) 09:10:43 PM IFACE rxpck/s txpck/s rxkB/s txkB/s rxcmp/s txcmp/s rxmcst/s 09:10:44 PM lo 14.00 14.00 1.34 1.34 0.00 0.00 0.00 09:10:44 PM eth0 4114.00 4186.00 4537.46 28513.24 0.00 0.00 0.00 09:10:43 PM active/s passive/s iseg/s oseg/s 09:10:44 PM 21.00 4.00 4107.00 22511.00 09:10:43 PM atmptf/s estres/s retrans/s isegerr/s orsts/s 09:10:44 PM 0.00 0.00 36.00 0.00 1.00 […]
- 59. Stack frame Mouse-over frames to quantify Ancestry
- 60. # git clone https://github.com/brendangregg/FlameGraph # cd FlameGraph # perf record -F 99 -ag -- sleep 60 # perf script | ./stackcollapse-perf.pl | ./flamegraph.pl > perf.svg
- 62. Broken Java stacks (missing frame pointer) Kernel TCP/IP GC Idle thread Time Locks epoll
- 65. # ./iosnoop –ts Tracing block I/O. Ctrl-C to end. STARTs ENDs COMM PID TYPE DEV BLOCK BYTES LATms 5982800.302061 5982800.302679 supervise 1809 W 202,1 17039600 4096 0.62 5982800.302423 5982800.302842 supervise 1809 W 202,1 17039608 4096 0.42 5982800.304962 5982800.305446 supervise 1801 W 202,1 17039616 4096 0.48 5982800.305250 5982800.305676 supervise 1801 W 202,1 17039624 4096 0.43 […] # ./iosnoop –h USAGE: iosnoop [-hQst] [-d device] [-i iotype] [-p PID] [-n name] [duration] -d device # device string (eg, "202,1) -i iotype # match type (eg, '*R*' for all reads) -n name # process name to match on I/O issue -p PID # PID to match on I/O issue -Q # include queueing time in LATms -s # include start time of I/O (s) -t # include completion time of I/O (s) […]
- 67. # perf record –e skb:consume_skb –ag -- sleep 10 # perf report [...] 74.42% swapper [kernel.kallsyms] [k] consume_skb | --- consume_skb arp_process arp_rcv __netif_receive_skb_core __netif_receive_skb netif_receive_skb virtnet_poll net_rx_action __do_softirq irq_exit do_IRQ ret_from_intr […] Summarizing stack traces for a tracepoint perf_events can do many things, it is hard to pick just one example
- 69. ec2-guest# ./showboost CPU MHz : 2500 Turbo MHz : 2900 (10 active) Turbo Ratio : 116% (10 active) CPU 0 summary every 5 seconds... Real CPU MHz TIME C0_MCYC C0_ACYC UTIL RATIO MHz 06:11:35 6428553166 7457384521 51% 116% 2900 06:11:40 6349881107 7365764152 50% 115% 2899 06:11:45 6240610655 7239046277 49% 115% 2899 [...]
- 72. Region App Breakdowns Metrics Options Interactive Graph Summary Statistics
- 75. Utilization Saturation Errors Per device Breakdowns
- 78. http://aws.amazon.com/ec2/instance-types/ http://docs.aws.amazon.com/AWSEC2/latest/UserGuide/instance-types.html http://docs.aws.amazon.com/AWSEC2/latest/UserGuide/enhanced-networking.html http://www.slideshare.net/cpwatson/cpn302-yourlinuxamioptimizationandperformance http://www.brendangregg.com/blog/2014-09-27/from-clouds-to-roots.html http://www.brendangregg.com/blog/2014-05-07/what-color-is-your-xen.html http://www.brendangregg.com/linuxperf.html http://www.slideshare.net/brendangregg/linux-performance-tools-2014 http://www.brendangregg.com/USEmethod/use-linux.html http://www.brendangregg.com/blog/2014-06-12/java-flame-graphs.html https://github.com/brendangregg/FlameGraph https://github.com/brendangregg/perf-tools
- 80. Talk Time Title PFC-305 Wednesday, 1:15pm Embracing Failure: Fault Injection and Service Reliability BDT-403 Wednesday, 2:15pm Next Generation Big Data Platform at Netflix PFC-306 Wednesday, 3:30pm Performance Tuning EC2 DEV-309 Wednesday, 3:30pm From Asgard to Zuul, How Netflix’s proven Open Source Tools can accelerate and scale your services ARC-317 Wednesday, 4:30pm Maintaining a Resilient Front-Door at Massive Scale PFC-304 Wednesday, 4:30pm Effective Inter-process Communications in the Cloud: The Pros and Cons of Micro Services Architectures ENT-209 Wednesday, 4:30pm Cloud Migration, Dev-Ops and Distributed Systems APP-310 Friday, 9:00am Scheduling using Apache Mesos in the Cloud