opvizor-performance-analyzer-part-14-performance-in-real-time-for-red-hat-enterprise-linux

There is a performance tuning guide available for Red Hat Enterprise Linux 7.Please find the complete white paper here.

Abstract

The Performance Tuning Guide describes how to optimize the performance of a system running Red Hat Enterprise Linux 6. It also documents performance-related upgrades in Red Hat Enterprise Linux 6.

While this guide contains procedures that are field-tested and proven, Red Hat recommends that you properly test all planned configurations in a testing environment before applying it to a production environment. You should also back up all your data and pre-tuning configurations.

There is also a Youtube Video available from the Red Hat Summit, please find here.

Performance analysis and tuning of Red Hat Enterprise Linux

by Larry Woodman, Red Hat and D. John Shakshober, Red Hat

In this 2-hour session, we’ll share how to configure and tune Red Hat Enterprise Linux versions 6 & 7 for optimal performance while running a variety of common applications. You’ll also learn how to evaluate and analyze the performance of heavily loaded systems and how to tune them to maximize performance on bare-metal x86 systems, and how it applies to tuning both Linux containers and clouds virtualized with KVM. Part 1: We’ll share the internals of Linux virtual memory and how to tune for NonUniform memory (Numa). We’ll share tools like "numastat" and techniques that are used to identify and resolve performance issues in a number of combinations of systems and applications including database servers, Internet servers, various financial applications on bare metal and using Linux containers. Part 2: We’ll extend the performance discussion to disk and network IO. We’ll take a deep dive into some examples to illustrate the latest performance analysis tools and techniques, like perf, tuna, and performance copilot, to identify performance bottlenecks impacting system and application performance.

IBM also created a white paper

by Barry Arndt, Linux Technology Center, IBM 

Here is the overview:

Every time you display an internet website in a web browser, a web server is used. A web server is the hardware and software combination that delivers web content, typically a website and associated data, over the internet to the requesting client browser. Websites served by web servers can be simple static pages, such as images or instruction manuals. They can also be complex dynamic pages requiring back end processing, such as retail product pages complete with current pricing, reviews, and social media approvals. The explosion of internet use and the incredible amount of content available by way of the internet have caused a corresponding explosion in the number of web servers required to deliver that content. If the web servers are poorly configured and perform sluggishly, the lag time for displaying the page in the browser increases. Lag times that are too great can lead to fewer returns to the website, and for online retailers, revenue loss. Thus, properly configured, high performing web servers are critical for successful online experiences and transactions. Few companies today have the luxury of owning, housing, and maintaining an excess of equipment. Having many underutilized servers creates unnecessary expense. Server consolidation through virtualization provides a means for eliminating unneeded servers and their associated costs. This paper examines best practices for configuring and tuning a web server on a virtualized system to achieve sufficient performance for successful transactions while providing the opportunity to cut costs.

The New Way

If you’re looking for a very modern way to check and monitor performance, you should give Performance Analyzer a try

Monitor and Analyze Red Hat Enterprise Linux (RHEL) configuration and performance metrics. Correlate events and metrics from applications and OS inside the guest with our RHEL OS metrics. If running virtual, combine them with VMware vSphere or OpenStack metrics. Troubleshoot issues using our efficient data crawler and preconfigured dashboards.

Red Hat Enterprise Linux

Some of our RHEL OS integration features are:

  • Get System overall status (across multiple systems)
  • Find Disk I/O bottlenecks
  • Full insights into Disk Latency and VM Disk IOps
  • See Memory Issues and Network Issues (Packet Loss) instantly
  • Get all networking details
  • Combine with applications running on top of the OS

Red Hat Enterprise Linux

Sign up for Performance Analyzer today and start 30 days for free.

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Metrics and Logs

(formerly, Opvizor Performance Analyzer)

VMware vSphere & Cloud
PERFORMANCE MONITORING, LOG ANALYSIS, LICENSE COMPLIANCE!

Monitor and Analyze Performance and Log files:
Performance monitoring for your systems and applications with log analysis (tamperproof using immudb) and license compliance (RedHat, Oracle, SAP and more) in one virtual appliance!

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Use Case - Tamper-resistant Clinical Trials

Goal:

Blockchain PoCs were unsuccessful due to complexity and lack of developers.

Still the goal of data immutability as well as client verification is a crucial. Furthermore, the system needs to be easy to use and operate (allowing backup, maintenance windows aso.).

Implementation:

immudb is running in different datacenters across the globe. All clinical trial information is stored in immudb either as transactions or the pdf documents as a whole.

Having that single source of truth with versioned, timestamped, and cryptographically verifiable records, enables a whole new way of transparency and trust.

Use Case - Finance

Goal:

Store the source data, the decision and the rule base for financial support from governments timestamped, verifiable.

A very important functionality is the ability to compare the historic decision (based on the past rulebase) with the rulebase at a different date. Fully cryptographic verifiable Time Travel queries are required to be able to achieve that comparison.

Implementation:

While the source data, rulebase and the documented decision are stored in verifiable Blobs in immudb, the transaction is stored using the relational layer of immudb.

That allows the use of immudb’s time travel capabilities to retrieve verified historic data and recalculate with the most recent rulebase.

Use Case - eCommerce and NFT marketplace

Goal:

No matter if it’s an eCommerce platform or NFT marketplace, the goals are similar:

  • High amount of transactions (potentially millions a second)
  • Ability to read and write multiple records within one transaction
  • prevent overwrite or updates on transactions
  • comply with regulations (PCI, GDPR, …)


Implementation:

immudb is typically scaled out using Hyperscaler (i. e. AWS, Google Cloud, Microsoft Azure) distributed across the Globe. Auditors are also distributed to track the verification proof over time. Additionally, the shop or marketplace applications store immudb cryptographic state information. That high level of integrity and tamper-evidence while maintaining a very high transaction speed is key for companies to chose immudb.

Use Case - IoT Sensor Data

Goal:

IoT sensor data received by devices collecting environment data needs to be stored locally in a cryptographically verifiable manner until the data is transferred to a central datacenter. The data integrity needs to be verifiable at any given point in time and while in transit.

Implementation:

immudb runs embedded on the IoT device itself and is consistently audited by external probes. The data transfer to audit is minimal and works even with minimum bandwidth and unreliable connections.

Whenever the IoT devices are connected to a high bandwidth, the data transfer happens to a data center (large immudb deployment) and the source and destination date integrity is fully verified.

Use Case - DevOps Evidence

Goal:

CI/CD and application build logs need to be stored auditable and tamper-evident.
A very high Performance is required as the system should not slow down any build process.
Scalability is key as billions of artifacts are expected within the next years.
Next to a possibility of integrity validation, data needs to be retrievable by pipeline job id or digital asset checksum.

Implementation:

As part of the CI/CD audit functionality, data is stored within immudb using the Key/Value functionality. Key is either the CI/CD job id (i. e. Jenkins or GitLab) or the checksum of the resulting build or container image.

White Paper — Registration

We will also send you the research paper
via email.

CodeNotary — Webinar

White Paper — Registration

Please let us know where we can send the whitepaper on CodeNotary Trusted Software Supply Chain. 

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