Tag Archives: Enterprise

The Looming Skills Crisis in the Epicenter of Your Enterprise | IT Infrastructure Advice, Discussion, Community

We often hear about skills shortages in “hot” fields like security or cloud or artificial intelligence — the roles that make flashy headlines. But there is another massive skills gap being largely overlooked, that if not addressed, could have extraordinary consequences on the success of businesses. That skills gap lies in the very heart of your enterprise: in the data center.

Every digital transformation effort runs through the data center. Modern enterprises need a modern data center. But despite being the lifeblood of the business, the data center hasn’t evolved at the same pace as the rest of the enterprise. Technology alone won’t modernize the data center though – it takes people.   

According to a report from the Uptime Institute, many data center staff simply don’t have the skills needed to modernize the data center. They lack experience in hybrid environments, software, and automation. Data center staff are also getting older, and businesses are struggling to fill open positions. Meanwhile, the people that do have those “newer” skills aren’t joining data center teams. See above: they’re probably being recruited to security, cloud, or AI teams! 

This has left enterprises vulnerable in one of the most important technical functions in the business. To mitigate this skills gap, enterprises need a two-pronged approach: invest in automation  and double-down on training and retaining data center staff.

Automation is not a four-letter word

Embracing more automation in the enterprise may change jobs and roles, but it won’t replace the need for IT staff. Rather, it will augment and assist humans. And ultimately, automation could be the thing to make the data center “cool” again. Because the job won’t be about memorizing CLI commands or IP addresses, which feels old and archaic. Instead, automation takes the mundanity out of the equation, and it will be about streamlining the provisioning and management of the data center. With automation, data center professionals could potentially run the data center on an app on their phone, or literally, use their voice to tell Slack to provision a new server and alert you when it’s done. Automation also removes the time-consuming bottleneck often involved in the change control process, which occurs when there is a request made for a new application or a change to something existing. These changes often turn into laborious processes involving multiple steps, documentation, and approvals, but automation is able to eliminate the manual work and expedite the time it takes to make the necessary change.

Most importantly, automation empowers data center professionals to be proactive and build skills by focusing on more strategic initiatives. It gives them the tools to transform what’s often seen as a cost center into a powerful asset that drives business outcomes. And beyond the satisfaction and day-to-day output of data center professionals, automation will allow organizations to be more agile and forward-looking.

Prioritize training and broadening skillsets

As much as automation will mitigate the skills gap in the data center, it’s not a silver bullet. The success of digital transformation and data center modernization entirely depends on the strength and intellect of the people within the walls of these enterprises. Which is exactly why organizations, large and small, need to up-level and broaden the range of training in the data center.

Training needs to focus on skills development for existing professionals – they need to learn new tools (i.e., software, automation, performance management, analytics) to help enrich their knowledge and extend their capabilities across functions. Data center professionals don’t need to become programmers (most won’t and don’t want to). But the vertical silos within the data center are shifting to a horizontal focus with greater attention to how all the pieces tie together. Think of it as a college major in networking, with minors in software, servers, security, virtualization, and storage.

In addition to providing more in-depth training to existing staff, organizations should also aim to recruit IT professionals with specialized knowledge of software and automation. Those workers may not automatically consider data center jobs, but if businesses can create additional incentives, those skills could greatly augment current teams.

Solving the skills crisis requires both technology and people

Digital transformation is a blessing and a curse. As many doors as it has opened, it’s created legitimate challenges for organizations bold enough to take these projects on. As of today, among the greatest limiting factors in technology-driven initiatives truly taking off is skills. A data center managed by teams with traditional skills will remain traditional, a legacy. A data center managed by teams with modern skills will become a more strategic asset, automating and empowering a modern business and providing a critical foundation for enabling an autonomous enterprise.

Through a combination of smart use of automation and a focus on people, organizations can begin to address the skills shortage and drive their businesses into the future.



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Automating the Enterprise Network: Why Scripting is No Longer the Answer | IT Infrastructure Advice, Discussion, Community

Numerous open-source scripting approaches to network management are currently available in the market. While promising, they may prove to be a high-risk trap for enterprises looking to automate, remove complexity, and make changes to their networks quickly. While instances of expensive network outages are usually kept under wraps, enterprises must be aware of these hidden issues and look away from traditional scripting to achieve an automated network foundation that ensures business continuity and innovation.

Reducing complexity and improving agility: Drivers for automation

Enterprises are trying to reduce complexity – including lengthy lab testing and implementation cycles – in their networks to improve agility. The end goal is a platform for competitive business innovation with policy-driven, intent-based principles. In addition, network virtualization, SD-WAN, and other new shifts in networking mean the network-as-a-service is no longer predictable.

These dynamics are beginning to obsolete scripting and home-grown coding because both are still locked into a static model of the network rather than maintaining the stability of core business while evolving the network as new initiatives are added dynamically. It’s the network itself that represents the living, evolving business – not the static-scripted or manually- configured model. Months of learning, customizing, and testing not only can’t keep pace but are actually no longer needed. Rather, enterprises need a dynamic knowledge base of the network that can deliver automated remedies, updates, and alerts for configuration and ongoing maintenance and management. This is why intent-based networking is resonating in the industry; validation of business intent, automated implementation, awareness of network state, assurance, optimization, and remediation are all required for the modern network. The question is how to get there fast and efficiently.

Why scripting isn’t the answer

There are several reasons why writing scripts is not the answer for enterprises looking to automate their networks:

  • While python scripting is a compelling upgrade to slow, manual processes, unlike telecom protocols, scripts are not standardized and typically don’t use best practices nor scale in a multi-vendor network. As business intent evolves from new initiatives or acquisitions, scaling the network becomes critical. Scripts are notoriously difficult to adapt to new vendor systems and may inhibit cost-savings.

  • Home-grown scripting, unlike code, cannot self-adapt to new environments, be programmed to interact with network state, nor operate as a machine-learning platform. At best, home-grown scripts provide a one-off, static network configurator for a fixed point in time. As the network changes, the scripts must be updated and re-tested to manage any underlying knowledge base while polling the changing state of network resources. Even without the training, script testing, bug fixing, and maintenance, the user is left with an approach that is static and must be re-scripted manually and continually. If a user wants to make the network policy-driven, he or she must hire or contract further scarce resources to write, test, and maintain custom software.

  • DIY scripting from generic templates or playbooks is another approach that seems promising. However, it requires customizing integrity tests, introduces the same high-risk maintenance issues and testing delays, is unresponsive to policy change, and still requires trained skills and customization. Unlike open source web platforms, these templates are not backed by massive communities and have the potential to damage the enterprise operation.

  • With scripting, the enterprise user is left to build compliance testing software to minimize enterprise risk. Compliance automation requires ongoing audit and action to validate actual network state, ensuring compliance to policy. Even after updating and re-testing scripts, there is no guarantee that problems have been fixed – or that new problems haven’t been introduced.

  • Scripting can be problematic when there are staffing changes in the enterprise. As staff change, the cost of either repeated training or poorly documented scripts creates a cycle of re-creation. Scripts not well understood by new staff tend to be disposable and are replaced, introducing additional testing and ultimately, more risk.

  • Interpreted scripts are slow and inefficient when compared to compiled, optimized code. In large configurations, this can impact availability and maintenance periods as the scripts update networks and are subsequently tested. Enterprises are looking to speed operations and dynamic, automated changes may make the concept of large-scale network maintenance almost disappear.

To operate a responsive, automated network, the existing model of static scripting, monolithic testing, and training maintenance does not support the type of fast-moving intent-based, networking that is becoming the goal of the modern enterprise. To build a foundation that keeps the business evolving and competitive, enterprises need to move away from traditional scripting and towards more intent-based automation.

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Oracle Releases Linux 4.14 Based “Unbreakable Enterprise Kernel R5 U2”


Oracle today announced the general availability release of their Unbreakable Enterprise Kernel Release 5 Update 2 that pairs with their RHEL-derived Oracle Linux for offering a Linux 4.14 based kernel with various features on top.

The Unbreakable Enterprise Kernel Release 5 Update 2 is based on the upstream Linux 4.14.35 kernel while adding in Pressure Stall Information patches, the KTask framework for helping with parallelizing CPU-intensive kernel work, DTrace support for libpcap packet capture, a variety of file-system driver fixes, various virtualization updates back-ported from Linux 4.19, various hardware driver updates, Arm platform tuning, and NVMe driver updates back-ported from earlier versions of the Linux kernel.

Those wanting to learn more about today’s Oracle Unbreakable Enterprise Kernel Release 5 Update 2 can learn more at the Oracle Linux blog.

Red Hat Enterprise Linux 8.0 Benchmarks On AMD EPYC – Big Speed-Ups Over RHEL7

Since the release of Red Hat Enterprise Linux 8.0 at the start of May we’ve been running various benchmarks of this latest enterprise Linux platform. Our tests to date have been with Intel Xeon hardware where it’s been performing well and a nice speed-up over RHEL 7 with modern Xeon Scalable CPUs. Similarly, AMD EPYC is also much faster with RHEL 8.0 thanks to the much newer Linux kernel, compiler, and other software updates.

AMD EPYC screams on Red Hat Enterprise Linux 8.0 compared to RHEL 7.6. The modern AMD server platform performs much better thanks to the GCC 8.2 compiler replacing the older GCC 4.8 compiler that came well before any Zen support. The Linux 4.18 kernel is also a blessing for newer AMD (and Intel/IBM/ARM) hardware compared to the heavily-patched Linux 3.10 kernel of RHEL7. RHEL 8.0 also shifted over to the MQ-Deadline scheduler for SATA SSDs compared to the non-MQ deadline scheduler and the plethora of upgraded packages compared to RHEL7 also means a big deal for performance at large.

For those wondering about the performance of AMD EPYC on RHEL 8.0, I recently ran some benchmarks on the Dell PowerEdge R7425 server with dual EPYC 7601 processors, 512GB of DDR4 ECC Registered memory, and was using a 500GB Samsung SSD 860 SATA 3.0 SSD during testing.

In this round of benchmarking, Clear Linux, RHEL 7.6, RHEL 8.0, Ubuntu 18.04.2 LTS, and Ubuntu 19.04 were used for comparison points while RHEL 7.6 vs. RHEL 8.0 was the main focus. Coming up soon from this same platform will also be openSUSE Leap 15.1 benchmarks. Via the Phoronix Test Suite a variety of benchmarks were carried out in looking at the RHEL8 performance for EPYC.

Red Hat Enterprise Linux 8 Available » Linux Magazine

At the Red Hat Summit, 2019 the company announced the availability of Red Hat Enterprise Linux 8.

According to Red Hat, RHEL Linux 8 is redesigned for the hybrid cloud era and built to support the workloads and operations that vary from enterprise datacenters to multiple public clouds.

One of the goals of modern tech companies is to simply the consumption of tech. Red Hat Enterprise Linux 8 abstracts many of the deep complexities of granular sysadmin tasks behind the Red Hat Enterprise Linux web console. The console provides an intuitive, consistent graphical interface for managing and monitoring Red Hat Enterprise Linux system, from the health of virtual machines to overall system performance. To further improve ease of use, Red Hat Enterprise Linux supports in-place upgrades, providing a more streamlined, efficient, and timely path for users to convert Red Hat Enterprise Linux 7 instances to Red Hat Enterprise Linux 8 systems.

Red Hat Enterprise Linux 8 also includes Red Hat Enterprise Linux System Roles, which automate many of the more complex tasks around managing and configuring Linux in production. Powered by Red Hat Ansible Automation, System Roles are pre-configured Ansible modules that enable ready-made automated workflows for handling common, complex sysadmin tasks. This automation makes it easier for new systems administrators to adopt Linux protocols and helps to eliminate human error.

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