This document provides recommendations for system capacity planning for an Oracle database:
- Plan for 1 CPU per 200 concurrent users and prefer medium speed CPUs over fewer faster CPUs.
- Reserve 10% of memory for the operating system and allocate 220 MB for the Oracle SGA and 3 MB per user process.
- Use striped and mirrored or striped with parity RAID for disks. Consider raw devices or SANs if possible.
- Ensure the network capacity is adequate based on site size.
The document describes the Patriot Cluster, a high performance computing system consisting of 13 nodes with a total of 160 CPU cores and 2496 GPU cores. It provides details on the hardware specifications, software, administration, and user experience. The key purpose of the cluster is to enable faster computation through parallel processing across multiple cores. It can speed up simulations that require many computational steps or particles, such as molecular dynamics or weather modeling. Users from physics and other departments can request accounts to utilize the computing power for courses and research.
With the increasing adoption of cloud native technologies and containerization; the gap between Java development and system administration is decreasing. Whether you are using Docker Swarm, Kubernetes or Mesos/Marathon as a container orchestrator; fundamental challenges for running docker in production are common.
In this talk, I would like to share some of the basic linux concepts about CPU scheduling every Java Developer should know to be able to perform effective configuration and troubleshooting for docker containers.
Yes, Docker provides isolation, but only if you know how best to configure it.
Data Science in DevOps/SysOps - Boaz Shuster - DevOpsDays Tel Aviv 2018DevOpsDays Tel Aviv
I will explain how collecting data from your continuous integration and delivery environments can help improve production releases. This talk is inspired by my previous job where I had the chance to boost the release pipeline using metrics and data.
With the increasing adoption of cloud native technologies and containerization; the gap between Java development and system administration is decreasing. Whether you are using Docker Swarm, Kubernetes or Mesos as a container orchestrator; fundamental challenges for running docker in production are common.
In this talk, I would like to share some of the basic linux concepts (like memory management, CPU, IO, sockets, file descriptors, signals, OOM killer) every Java Developer should know to be able to perform effective configuration and troubleshooting for docker containers.
The document describes Linux containerization and virtualization technologies including containers, control groups (cgroups), namespaces, and backups. It discusses:
1) How cgroups isolate and limit system resources for containers through mechanisms like cpuset, cpuacct, cpu, memory, blkio, and freezer.
2) How namespaces isolate processes by ID, mounting, networking, IPC, and other resources to separate environments for containers.
3) The new backup system which uses thin provisioning and snapshotting to efficiently backup container environments to backup servers and restore individual accounts or full servers as needed.
The document discusses the Elastic Processing Unit (EPU) in GigaSpaces, which allows automatic scaling of processing units across containers and machines, managing the entire container lifecycle as well as automatic rebalancing and capacity calculations. It provides instructions on deploying an EPU, scaling it manually or automatically based on resource monitoring, and undeploying it along with automatically terminating associated containers. The EPU also supports automatic provisioning of new machines through custom plugins to dynamically add resources on demand.
This document discusses tuning Linux kernel parameters to optimize performance. It provides examples of modifying parameters related to CPU, memory, storage, networking and security via sysctl, procfs interfaces and configuration files. Parameters control behaviors like TCP settings, file limits, randomization options and overcommit thresholds. Recommendations are given for Red Hat and kernel documentation for further reference.
This document provides recommendations for system capacity planning for an Oracle database:
- Plan for 1 CPU per 200 concurrent users and prefer medium speed CPUs over fewer faster CPUs.
- Reserve 10% of memory for the operating system and allocate 220 MB for the Oracle SGA and 3 MB per user process.
- Use striped and mirrored or striped with parity RAID for disks. Consider raw devices or SANs if possible.
- Ensure the network capacity is adequate based on site size.
The document describes the Patriot Cluster, a high performance computing system consisting of 13 nodes with a total of 160 CPU cores and 2496 GPU cores. It provides details on the hardware specifications, software, administration, and user experience. The key purpose of the cluster is to enable faster computation through parallel processing across multiple cores. It can speed up simulations that require many computational steps or particles, such as molecular dynamics or weather modeling. Users from physics and other departments can request accounts to utilize the computing power for courses and research.
With the increasing adoption of cloud native technologies and containerization; the gap between Java development and system administration is decreasing. Whether you are using Docker Swarm, Kubernetes or Mesos/Marathon as a container orchestrator; fundamental challenges for running docker in production are common.
In this talk, I would like to share some of the basic linux concepts about CPU scheduling every Java Developer should know to be able to perform effective configuration and troubleshooting for docker containers.
Yes, Docker provides isolation, but only if you know how best to configure it.
Data Science in DevOps/SysOps - Boaz Shuster - DevOpsDays Tel Aviv 2018DevOpsDays Tel Aviv
I will explain how collecting data from your continuous integration and delivery environments can help improve production releases. This talk is inspired by my previous job where I had the chance to boost the release pipeline using metrics and data.
With the increasing adoption of cloud native technologies and containerization; the gap between Java development and system administration is decreasing. Whether you are using Docker Swarm, Kubernetes or Mesos as a container orchestrator; fundamental challenges for running docker in production are common.
In this talk, I would like to share some of the basic linux concepts (like memory management, CPU, IO, sockets, file descriptors, signals, OOM killer) every Java Developer should know to be able to perform effective configuration and troubleshooting for docker containers.
The document describes Linux containerization and virtualization technologies including containers, control groups (cgroups), namespaces, and backups. It discusses:
1) How cgroups isolate and limit system resources for containers through mechanisms like cpuset, cpuacct, cpu, memory, blkio, and freezer.
2) How namespaces isolate processes by ID, mounting, networking, IPC, and other resources to separate environments for containers.
3) The new backup system which uses thin provisioning and snapshotting to efficiently backup container environments to backup servers and restore individual accounts or full servers as needed.
The document discusses the Elastic Processing Unit (EPU) in GigaSpaces, which allows automatic scaling of processing units across containers and machines, managing the entire container lifecycle as well as automatic rebalancing and capacity calculations. It provides instructions on deploying an EPU, scaling it manually or automatically based on resource monitoring, and undeploying it along with automatically terminating associated containers. The EPU also supports automatic provisioning of new machines through custom plugins to dynamically add resources on demand.
This document discusses tuning Linux kernel parameters to optimize performance. It provides examples of modifying parameters related to CPU, memory, storage, networking and security via sysctl, procfs interfaces and configuration files. Parameters control behaviors like TCP settings, file limits, randomization options and overcommit thresholds. Recommendations are given for Red Hat and kernel documentation for further reference.
cudaMemcpyAsync allows asynchronous transfer of memory between the CPU and GPU over a non-default stream, enabling overlap of memory copies and computation so that copies do not block the CPU or GPU. This can improve performance by hiding memory transfer latency with useful work.
A Buffering Approach to Manage I/O in a Normalized Cross-Correlation Earthqua...Dawei Mu
CUDA based software designed to calculate the normalized cross- correlation coefficient (NCC) between a collection of selected template waveforms and the continuous waveform recordings of seismic instruments to evaluate the waveform similarity among the waveforms and/or the relative travel-time differences.
- Hardware such as DRAM and NAND flash are facing scaling challenges as density increases, which could impact performance and cost. New non-volatile memory (NVM) technologies may provide opportunities to address these challenges but require software and system architecture changes to realize their full potential. Key considerations include persistence, performance, and programming models.
This document discusses various tools for monitoring and analyzing metaspace and class metadata in the Java virtual machine. It describes using -XX:+PrintGCDetails to print details of full GC collections including metaspace usage. It also discusses using MBeans, jstat -gc, and VisualVM to monitor memory pools like metaspace and class space. The document further explains using jmap -clstats to view statistics per class loader and GC.class_stats to view statistics on Java class metadata, which both require unlocking diagnostic VM options.
Ceph is evolving its network stack to improve performance. It is moving from AsyncMessenger to using RDMA for better scalability and lower latency. RDMA support is now built into Ceph and provides native RDMA using verbs or RDMA-CM. This allows using InfiniBand or RoCE networks with Ceph. Work continues to fully leverage RDMA for features like zero-copy replication and erasure coding offload.
Skypicker is a flight ticket search and booking engine that processes hundreds of terabytes of airline data monthly and sells thousands of tickets daily in Europe, Russia, and China. It uses PostgreSQL extensively to power its API and databases. The document discusses Skypicker's PostgreSQL infrastructure, which includes 5 database clusters with over 0.5TB of memory each that handle 20 million updates per hour. It also covers optimizations made to scale the databases, such as table partitioning, cascading replication, and query tuning. Skypicker is currently preparing for a large increase in data and is hiring database masters and developers.
PCIe peer-to-peer communication can reduce bottlenecks between high-performance I/O devices like SSDs and networking cards by allowing them to transfer data directly without going through the CPU. PMC is developing an NVM Express NVRAM card using DRAM cache that is accessible via the NVMe block driver or custom character driver, and can achieve almost 1 million 4KB IOPS or 10 million 64B IOPS. The company has set up a test hardware and software environment using PCIe devices connected directly to CPU lanes running Debian Linux with custom kernel patches to demonstrate peer-to-peer capabilities.
Kernels are functions that run on GPUs in parallel across multiple threads and blocks. Kernels are launched with an execution configuration that defines the number of blocks in the grid and the number of threads in each block, allowing thousands of threads to run simultaneously and take advantage of the GPU's parallel architecture.
This document provides an overview of common commands, configurations, and tools used for deploying projects on Linux servers including CentOS. It covers setting up networking, remote access, domains, Java, MySQL, web containers like Tomcat and GlassFish, web servers Apache and Nginx, time synchronization with NTP, automatic startup of services, and firewall management.
This document summarizes Marian Marinov's testing and experience with different distributed filesystems at his company SiteGround. He tested CephFS, GlusterFS, MooseFS, OrangeFS, and BeeGFS. CephFS required a lot of resources but lacked redundancy. GlusterFS was relatively easy to set up but had high CPU usage. MooseFS and OrangeFS were also easy to set up. Ultimately, they settled on Ceph RBD with NFS and caching for performance and simplicity. File creation performance tests showed MooseFS and NFS+Ceph RBD outperformed OrangeFS and GlusterFS. Tuning settings like MTU, congestion control, and caching helped optimize performance.
This technical report discusses configuration of the Performance Schema in MySQL 5.6. It describes configuration tables for setting monitoring targets, consumers, instruments, and objects. It shows commands for checking default settings and updating configurations. Benchmarks with different Performance Schema settings show throughput decreased when instruments were enabled but wait events only configuration had less impact than fully enabling instruments.
The document contains log entries from Nero burning software. It lists the software and hardware versions used, details about the burning process such as settings and tracks, and indicates that the burn was aborted by the user due to insufficient free space on the disc.
This document discusses several common problems encountered with TCP sockets, connections, and memory usage. Solutions provided include increasing socket backlog limits, connection tracking limits, adjusting TIME_WAIT settings, and scaling memcached with multiple processes instead of threads.
The document provides guidelines and commands for analyzing z/VM performance. It discusses processor, storage, paging, and server machine guidelines. Key performance metrics like CPU utilization, response time, and throughput are defined. Commands for monitoring load, queues, paging, storage, and more are listed. REORDMON is introduced as a tool for diagnosing reorder processing overhead.
The document tests the minimum memory requirements for Java Virtual Machines (JVMs) on different operating systems and processor architectures. It finds that for x86 32-bit systems, the minimum memory requirement is 1MB. For x86-64 systems, the minimum is 2MB on Linux but 1MB on Solaris. The minimum for Sparc64 and Aix64 systems is also 2MB. The conclusion is that the minimum JVM memory requirement depends on the specific OS and processor architecture.
This document discusses overclocking hardware components like CPUs, RAM, and GPUs by an overclocking team in Afghanistan. It lists reasons for overclocking like cost savings by getting more performance from existing hardware without upgrading. Tables show potential savings from overclocking various Intel Core i7 and Core 2 Duo CPUs. The document expresses the team's confidence in their ability to overclock and break limits through dedication to optimizing hardware performance.
The document introduces AMD's new six-core Opteron EE processor targeted at energy efficient servers and workloads. It provides up to 30% higher performance than the quad-core Opteron at the same 40W power envelope. The low power processor allows for greater server density in cloud computing environments without compromising features like virtualization. It aims to deliver both top-line performance and bottom-line efficiency for customers with large scale-out deployments.
The Economics of Scaling Cassandra - By Alex Bordei, Techie Product Manager at Bigstep
This presentation was made during the "Cassandra Summit 2014" Event, in London.
We benchmarked Cassandra on a number of configurations and we show what's the scaling profile. We test Cassandra on Docker as well as Cassandra's In-memory feature.
Follow Alex on Twitter: @alexandrubordei
Bigstep on Twitter: @BigStepInc
If you have any questions, let us know at hello@bigstep.com and we'll do our best to answer.
Stay informed: http://blog.bigstep.com/
cudaMemcpyAsync allows asynchronous transfer of memory between the CPU and GPU over a non-default stream, enabling overlap of memory copies and computation so that copies do not block the CPU or GPU. This can improve performance by hiding memory transfer latency with useful work.
A Buffering Approach to Manage I/O in a Normalized Cross-Correlation Earthqua...Dawei Mu
CUDA based software designed to calculate the normalized cross- correlation coefficient (NCC) between a collection of selected template waveforms and the continuous waveform recordings of seismic instruments to evaluate the waveform similarity among the waveforms and/or the relative travel-time differences.
- Hardware such as DRAM and NAND flash are facing scaling challenges as density increases, which could impact performance and cost. New non-volatile memory (NVM) technologies may provide opportunities to address these challenges but require software and system architecture changes to realize their full potential. Key considerations include persistence, performance, and programming models.
This document discusses various tools for monitoring and analyzing metaspace and class metadata in the Java virtual machine. It describes using -XX:+PrintGCDetails to print details of full GC collections including metaspace usage. It also discusses using MBeans, jstat -gc, and VisualVM to monitor memory pools like metaspace and class space. The document further explains using jmap -clstats to view statistics per class loader and GC.class_stats to view statistics on Java class metadata, which both require unlocking diagnostic VM options.
Ceph is evolving its network stack to improve performance. It is moving from AsyncMessenger to using RDMA for better scalability and lower latency. RDMA support is now built into Ceph and provides native RDMA using verbs or RDMA-CM. This allows using InfiniBand or RoCE networks with Ceph. Work continues to fully leverage RDMA for features like zero-copy replication and erasure coding offload.
Skypicker is a flight ticket search and booking engine that processes hundreds of terabytes of airline data monthly and sells thousands of tickets daily in Europe, Russia, and China. It uses PostgreSQL extensively to power its API and databases. The document discusses Skypicker's PostgreSQL infrastructure, which includes 5 database clusters with over 0.5TB of memory each that handle 20 million updates per hour. It also covers optimizations made to scale the databases, such as table partitioning, cascading replication, and query tuning. Skypicker is currently preparing for a large increase in data and is hiring database masters and developers.
PCIe peer-to-peer communication can reduce bottlenecks between high-performance I/O devices like SSDs and networking cards by allowing them to transfer data directly without going through the CPU. PMC is developing an NVM Express NVRAM card using DRAM cache that is accessible via the NVMe block driver or custom character driver, and can achieve almost 1 million 4KB IOPS or 10 million 64B IOPS. The company has set up a test hardware and software environment using PCIe devices connected directly to CPU lanes running Debian Linux with custom kernel patches to demonstrate peer-to-peer capabilities.
Kernels are functions that run on GPUs in parallel across multiple threads and blocks. Kernels are launched with an execution configuration that defines the number of blocks in the grid and the number of threads in each block, allowing thousands of threads to run simultaneously and take advantage of the GPU's parallel architecture.
This document provides an overview of common commands, configurations, and tools used for deploying projects on Linux servers including CentOS. It covers setting up networking, remote access, domains, Java, MySQL, web containers like Tomcat and GlassFish, web servers Apache and Nginx, time synchronization with NTP, automatic startup of services, and firewall management.
This document summarizes Marian Marinov's testing and experience with different distributed filesystems at his company SiteGround. He tested CephFS, GlusterFS, MooseFS, OrangeFS, and BeeGFS. CephFS required a lot of resources but lacked redundancy. GlusterFS was relatively easy to set up but had high CPU usage. MooseFS and OrangeFS were also easy to set up. Ultimately, they settled on Ceph RBD with NFS and caching for performance and simplicity. File creation performance tests showed MooseFS and NFS+Ceph RBD outperformed OrangeFS and GlusterFS. Tuning settings like MTU, congestion control, and caching helped optimize performance.
This technical report discusses configuration of the Performance Schema in MySQL 5.6. It describes configuration tables for setting monitoring targets, consumers, instruments, and objects. It shows commands for checking default settings and updating configurations. Benchmarks with different Performance Schema settings show throughput decreased when instruments were enabled but wait events only configuration had less impact than fully enabling instruments.
The document contains log entries from Nero burning software. It lists the software and hardware versions used, details about the burning process such as settings and tracks, and indicates that the burn was aborted by the user due to insufficient free space on the disc.
This document discusses several common problems encountered with TCP sockets, connections, and memory usage. Solutions provided include increasing socket backlog limits, connection tracking limits, adjusting TIME_WAIT settings, and scaling memcached with multiple processes instead of threads.
The document provides guidelines and commands for analyzing z/VM performance. It discusses processor, storage, paging, and server machine guidelines. Key performance metrics like CPU utilization, response time, and throughput are defined. Commands for monitoring load, queues, paging, storage, and more are listed. REORDMON is introduced as a tool for diagnosing reorder processing overhead.
The document tests the minimum memory requirements for Java Virtual Machines (JVMs) on different operating systems and processor architectures. It finds that for x86 32-bit systems, the minimum memory requirement is 1MB. For x86-64 systems, the minimum is 2MB on Linux but 1MB on Solaris. The minimum for Sparc64 and Aix64 systems is also 2MB. The conclusion is that the minimum JVM memory requirement depends on the specific OS and processor architecture.
This document discusses overclocking hardware components like CPUs, RAM, and GPUs by an overclocking team in Afghanistan. It lists reasons for overclocking like cost savings by getting more performance from existing hardware without upgrading. Tables show potential savings from overclocking various Intel Core i7 and Core 2 Duo CPUs. The document expresses the team's confidence in their ability to overclock and break limits through dedication to optimizing hardware performance.
The document introduces AMD's new six-core Opteron EE processor targeted at energy efficient servers and workloads. It provides up to 30% higher performance than the quad-core Opteron at the same 40W power envelope. The low power processor allows for greater server density in cloud computing environments without compromising features like virtualization. It aims to deliver both top-line performance and bottom-line efficiency for customers with large scale-out deployments.
The Economics of Scaling Cassandra - By Alex Bordei, Techie Product Manager at Bigstep
This presentation was made during the "Cassandra Summit 2014" Event, in London.
We benchmarked Cassandra on a number of configurations and we show what's the scaling profile. We test Cassandra on Docker as well as Cassandra's In-memory feature.
Follow Alex on Twitter: @alexandrubordei
Bigstep on Twitter: @BigStepInc
If you have any questions, let us know at hello@bigstep.com and we'll do our best to answer.
Stay informed: http://blog.bigstep.com/
The document discusses diagnosing performance issues on a client's PC. The assistant tested the PC's performance by monitoring CPU and memory usage while opening different applications. Testing showed lower than expected usage. Disk space was also checked but clearing temporary files made little difference. The assistant then analyzed upgrading the RAM, processor, and OS versus replacing the PC. Upgrading would provide better specs than a similar-priced replacement PC, so upgrading was recommended as the more cost-effective option to improve performance.
- The document discusses current R&D work on pre-Exascale HPC systems, including a PRACE 2011 prototype that delivers over 10 TFLOPS in a single rack using heterogeneous hardware with GPUs and achieves over 1.1 TFLOPS/kW efficiency.
- Performance debugging techniques are discussed for multi-socket, multi-chipset, multi-GPU systems to analyze issues like bottlenecks in the cache hierarchy topology and imbalanced I/O. Affinity and memory binding are important to optimize performance.
- Linux and Windows tools like HWLOC can be used to set CPU and GPU affinity as well as memory binding to improve data transfer rates between devices by ensuring local memory access.
The document discusses capacity planning for servers to handle 30-40% yearly growth over 5 years. Currently, production and DR servers use POWER6 processors with 4 cores each. Analysis shows CPU and disk utilization exceed thresholds. Proposed solutions for 30% growth add 2 cores each to new Power8 production and DR servers. For 40% growth, solutions add 3 cores each. The new servers would improve batch processing times and use faster LTO6 tapes compared to the existing environment.
GPU compute has leveraged discrete GPUs for a fairly limited set of academic and supercomputing system workloads until recently. With the increase in performance of integrated GPU inside an Accelerated Processing Unit (APU), introduction of Heterogeneous System Architecture (HSA) devices, and proliferation of programming tools, we are seeing GPU compute make its way into mainstream applications. In this presentation we cover GPU compute and HSA, focusing on the application of GPU compute in the Medical and Print Imaging segments. Examples of performance data are reviewed and the case is made for how GPU compute can deliver tangible benefits.
Large-Scale Optimization Strategies for Typical HPC Workloadsinside-BigData.com
Large-scale optimization strategies for typical HPC workloads include:
1) Building a powerful profiling tool to analyze application performance and identify bottlenecks like inefficient instructions, memory bandwidth, and network utilization.
2) Harnessing state-of-the-art hardware like new CPU architectures, instruction sets, and accelerators to maximize application performance.
3) Leveraging the latest algorithms and computational models that are better suited for large-scale parallelization and new hardware.
This document discusses using SystemD and cgroups to manage resources for Rails applications running on Linux servers. It recommends reading about SystemD and cgroups, creating service files, and using SSH to set them up on servers. It then introduces the capistrano-systemd-core gem, which provides a simple configuration and automation for setting up SystemD services with Capistrano deployments. Questions are welcomed on GitHub, Twitter, Facebook, or the company website.
Microsoft Azure intro - common information and blah blah blah about cloud computing, virtual machines - comparing A and D series by numbers ( performance CPU, RAM, storage ) and variability, Web apps ( ex-Web sites ).
This document summarizes performance tests run on a computer to determine if it needs upgrading. The tests measured CPU and memory usage under different tasks like browsing the internet, using Photoshop, and playing videos. The results showed the CPU and memory handling the tasks well within expected limits. Disk space was also analyzed before and after cleanup, showing the operating system used nearly half the space while cleanup freed up under 500MB. Finally, potential upgrade options are presented and analyzed, with a recommended upgrade of the CPU, RAM, and Windows for a cost of around £614 to extend the life of the existing computer parts.
Aerospike TCO Vs memory-first architecturesAerospike
See how Aerospike compares to in-memory or caching technologies as you scale. Aerospike TCO is very low in comparison as Flash/SSD technology is used to persist the data.
Speedrunning the Open Street Map osm2pgsql LoaderGregSmith458515
The Open Street Map project provides invaluable data that keeps driving users toward the PostGIS and PostgreSQL stacks. Loading today’s full Planet data set takes a 120GB XML file and unrolls it into over a terabyte of database data. Crunchy’s benchmark labs have followed the expansion of that Planet data over the last six database releases, as the re-ignition of the CPU wars combined with parallel execution features landing in the database. We’ll take a look at that data evolution, which server configurations worked, and which metrics techniques still matter in the all SSD era.
Ceph Day Beijing - Ceph all-flash array design based on NUMA architectureCeph Community
This document discusses an all-flash Ceph array design from QCT based on NUMA architecture. It provides an agenda that covers all-flash Ceph and use cases, QCT's all-flash Ceph solution for IOPS, an overview of QCT's lab environment and detailed architecture, and the importance of NUMA. It also includes sections on why all-flash storage is used, different all-flash Ceph use cases, QCT's IOPS-optimized all-flash Ceph solution, benefits of using NVMe storage, and techniques for configuring and optimizing all-flash Ceph performance.
Ceph Day Beijing - Ceph All-Flash Array Design Based on NUMA ArchitectureDanielle Womboldt
This document discusses an all-flash Ceph array design from QCT based on NUMA architecture. It provides an agenda that covers all-flash Ceph and use cases, QCT's all-flash Ceph solution for IOPS, an overview of QCT's lab environment and detailed architecture, and the importance of NUMA. It also includes sections on why all-flash storage is used, different all-flash Ceph use cases, QCT's IOPS-optimized all-flash Ceph solution, benefits of using NVMe storage, QCT's lab test environment, Ceph tuning recommendations, and benefits of using multi-partitioned NVMe SSDs for Ceph OSDs.
The document discusses AMD's technologies for improving energy efficiency and performance in servers and graphics processors. It highlights AMD's Opteron and FireStream processors, which provide high performance while using less power than competitors. AMD is focusing on innovations like its CoolCore technology, 45nm manufacturing process, and support for OpenCL to enhance efficiency and acceleration capabilities across its product lines.
Amazon EC2 provides a broad selection of instance types to accommodate a diverse mix of workloads. In this session, we provide an overview of the Amazon EC2 instance platform, key platform features, and the concept of instance generations. We dive into the current generation design choices of the different instance families, including the General Purpose, Compute Optimized, Storage Optimized, Memory Optimized, and GPU instance families. We also detail best practices and share performance tips for getting the most out of your Amazon EC2 instances.
A way to visual the best storage media for an applicationTony Roug
This document analyzes storage solutions for a server configuration based on capacity, performance, price, and power requirements. It compares the performance and cost of HDDs, SSDs, PCIe SSDs, and DDR storage. The analysis shows that SATA SSDs have improved over the last 3 years and can now meet the performance needs of most common usage models at a lower cost and power than other options. PCIe SSDs remain beneficial only for workloads with very high write percentages. Overall, SATA SSDs provide the best balance of performance, price, and low power for most server storage needs.
Similar to Case 4 mdm system change report - a car maker (20)
OnTune provides real-time system performance monitoring down to the second, allowing it to detect issues that other solutions miss. It collects a wide range of system data without customization and stores historical data to help analyze long-term trends and troubleshoot intermittent problems. Case studies demonstrate how OnTune's high-granularity data helped users identify specific processes causing performance issues and crashes.
- onTune is a system analysis and performance monitoring tool that provides real-time data collection and monitoring of key system metrics like CPU, memory, disk, network usage, and applications every 2 seconds with low overhead.
- It allows visibility into performance across on-premise and cloud environments from a single view and helps identify issues through deep diagnostic reports and root cause analysis.
- onTune's simple interface makes it fast and easy to implement and use to optimize system performance, reduce costs, and ensure service quality and SLAs.
Case 3 inspecting cause of failure - a car maker plmTeemStone Pty Ltd
Three Oracle Java processes on the PLM DB#2 server requested 3GB of memory at 13:26, causing high CPU I/O wait times and paging as the server struggled to allocate memory. This overwhelmed the server and caused it to temporarily hang. Inspecting the server's CPU usage, paging activity, and memory usage graphs during the hang showed abnormal peaks at 13:26 that identified the memory allocation request as the likely root cause of the problem. Stopping the EM monitoring agent on the troubled server reduced memory usage and prevented further hangs.
George, a system administrator, was having trouble completing a deployment that was taking all morning. He had contacted colleagues, experts, and developers for help but was still unable to resolve the issue. System administrators play a critical role in maintaining computer infrastructure but troubleshooting problems can be challenging and time-consuming. Expert performance analysis tools like onTune that collect real-time system data and monitor all processes can help administrators identify issues faster and save organizations time and money compared to general system management solutions.
This document discusses the System Analysis & Performance Instrument tool from TeemStone Corp. It outlines some of the key benefits of their onTune product over competitors like IBM Tivoli, HP OpenView, and BMC Patrol. Specifically, it notes that onTune allows for real-time monitoring at the second level, collects abundant performance data, and has intuitive functions for problem and performance analysis. Case studies and customer testimonials are also provided showing how onTune helped customers identify issues that other tools could not detect.
An internet shopping mall was experiencing frequent system errors and crashes from a new interactive flash program. The system operator could not determine the root cause of the issues through standard Windows performance monitoring tools. After installing OnTune to monitor system and process resource usage, the operator discovered that the virtual memory usage of the flash program was steadily increasing until it caused the system to crash. This identified a memory leak in the flash program code. The developer was notified and fixed the problem, resolving the system crashes.
onTune is a next generation system management solution that can quickly analyze, identify, and offer solutions to any performance problems for the system administrator in real-time. It can be installed through a simple installation and automatically monitors the system without additional configuration changes. onTune also creates reports like PowerPoint to analyze historical system performance and status.
onTune is a next generation system management solution from TeemStone that allows real-time monitoring and analysis of system performance. It collects system data without customization and allows administrators to monitor CPU, memory, I/O, and other essential elements. onTune supports monitoring in virtualization environments from vendors like IBM, HP, and Oracle. It is currently used by IBM for virtualization monitoring and supports various monitoring and analysis scenarios for ensuring system performance.
onTune is a next generation performance monitoring and analysis solution that allows real-time monitoring of systems to maximize analytic capacity and understand the reasons for any errors. It collects performance data every 2 seconds to provide more detailed insight compared to traditional SMS tools with collection intervals of 1 minute or more. System administrators can monitor CPU, memory, I/O and other metrics for overall systems or individual processes.
onTune provides powerful virtualization monitoring and analysis capabilities. It can monitor virtual resources on servers from various hardware vendors that use technologies like VMware, Xen, and Oracle's LDOM. onTune collects structure information and monitors both logical virtualized resources and physical server resources. It presents this information visually to help administrators respond to issues. onTune also helps analyze the root causes of errors in virtualized systems through real-time performance monitoring and analysis.
Unveiling the Advantages of Agile Software Development.pdfbrainerhub1
Learn about Agile Software Development's advantages. Simplify your workflow to spur quicker innovation. Jump right in! We have also discussed the advantages.
UI5con 2024 - Boost Your Development Experience with UI5 Tooling ExtensionsPeter Muessig
The UI5 tooling is the development and build tooling of UI5. It is built in a modular and extensible way so that it can be easily extended by your needs. This session will showcase various tooling extensions which can boost your development experience by far so that you can really work offline, transpile your code in your project to use even newer versions of EcmaScript (than 2022 which is supported right now by the UI5 tooling), consume any npm package of your choice in your project, using different kind of proxies, and even stitching UI5 projects during development together to mimic your target environment.
UI5con 2024 - Keynote: Latest News about UI5 and it’s EcosystemPeter Muessig
Learn about the latest innovations in and around OpenUI5/SAPUI5: UI5 Tooling, UI5 linter, UI5 Web Components, Web Components Integration, UI5 2.x, UI5 GenAI.
Recording:
https://www.youtube.com/live/MSdGLG2zLy8?si=INxBHTqkwHhxV5Ta&t=0
Microservice Teams - How the cloud changes the way we workSven Peters
A lot of technical challenges and complexity come with building a cloud-native and distributed architecture. The way we develop backend software has fundamentally changed in the last ten years. Managing a microservices architecture demands a lot of us to ensure observability and operational resiliency. But did you also change the way you run your development teams?
Sven will talk about Atlassian’s journey from a monolith to a multi-tenanted architecture and how it affected the way the engineering teams work. You will learn how we shifted to service ownership, moved to more autonomous teams (and its challenges), and established platform and enablement teams.
The Key to Digital Success_ A Comprehensive Guide to Continuous Testing Integ...kalichargn70th171
In today's business landscape, digital integration is ubiquitous, demanding swift innovation as a necessity rather than a luxury. In a fiercely competitive market with heightened customer expectations, the timely launch of flawless digital products is crucial for both acquisition and retention—any delay risks ceding market share to competitors.
Most important New features of Oracle 23c for DBAs and Developers. You can get more idea from my youtube channel video from https://youtu.be/XvL5WtaC20A
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2. 1
Comparing CPU utilization
Server Avg Avg usage Max Max usage
mdmdbp00 53% 16core 95% 30core
mdmbdp00 8% 5core 40% 23core
mdmmdp00 5% 1core 47% 8core
CPU usage had shown high prior to system
upgrade, while that of post upgrade –
separating prd and vprd – are stable.
The maximum value of CPU usage
dramatically decreased after system changes.
The CPU utilization peak at 09:00 7. Nov on mdmbdp00
server was intentionally caused by administrator for the
testing purpose.
Pre
Post
3. 2
Comparing Memory utilization
Server Avg Avg usage Max Max ussag
mdmdbp00 62% 69GB 66% 73GB
mdmbdp00 36% 81GB 41% 92GB
mdmmdp00 44% 26GB 50% 30GB
Usually memory usage of Oracle DB server is
highly dependent upon Oracle SGA setting values.
DB Administrator needs to check the SGA values.
Pre
Post
4. 3
Comparing Disk IO throughput
Server Avg Throughput
Max
Throughput
mdmdbp00 97 MB/s 189 MB/s
mdmbdp00 43 MB/s 236 MB/s
mdmmdp00 24 MB/s 121 MB/s
In general, Disk IO throughput of a DB server
means the volume of data processing.
Prior to upgrading, peak Disk IO throughput was
less than 200MB/s, while peak throughput
reached almost 300MB/s after applying change.
This means that Disk IO throughput was
improved by eliminating CPU bottleneck
dramatically.
Pre
Post
5. 4
Comparing CPU utilization by business process
Biz
Avg CPU
Util
Avg CPU
#
Max CPU
Util
Max CPU
#
Pre
oraprd 30% 9core 71% 22core
oravin 6% 2core 31% 10core
Post
oraprd 8% 5core 40% 23core
oravin 5% 1core 52% 8core
CPU utilization by process is changed similar to
that of server total.
This result was predicted.
Pre
Post
6. 5
Summary
Server Model
Biz
(Instance)
CPU #
(core)
Memory(GB)
CPU Util Memory Util Disk Throughput
Avg Max Avg Max Avg Max
Pre (mdmdbp00) rx8640 prd+vprd 30 112GB 53% 95% 62% 66% 97MB/s 189MB/s
Post (mdmbdp00) 32s prd 56 224GB 8% 32% 44% 50% 43MB/s 236MB/s
Post (mdmmdp00) rx8640 vprd 15 60GB 5% 47% 36% 41% 24MB/s 121MB/s
The result is a good example of H/W upgrade effectiveness.
From the point of CPU usage, prd and vprd compete to allocate resources before upgrading, and this
competing might cause CPU bottleneck often. By eliminating CPU bottleneck peak throughput is
improved dramatically. The increasing of maximum Disk IO throughput by 50% is a good evidence of
this improvement.