Craig Huitema blogged about Cisco’s SDN strategy and one of the key pillars is programmable networks. Cisco’s programmable networks is based on Nexus operating system NX-OS and our Robb Boyd from TechWiseTV covers it here and goes in more depth about NX-API REST (Object model) here and here.
Also go here if you missed our September 25th SDxCentral DemoFriday where we looked at use cases and demos related to NX-Toolkit and NX-API REST. Bottom line is to drive operational agility in the data center by enabling IT admins to manage Nexus switches as a Linux server with open interfaces and integrating DevOps tools.
One of the DevOps tools is Puppet. Integrating Puppet Enterprise agent is an integral part of programmable networks as I touched on it in my previous blog.
As we break lifecycle management into Day 0, 1, 2 and N to install, configure, optimize and upgrade the network to meet application and user requirements, Puppet plays a key role in each step.
Come and visit Cisco’s booth at PuppetConf October 7 – 9 to see demos and learn more about the integration of Puppet and its benefits on Day 0, 1, 2, and N. Also, visit our sponsor theater on Thursday, Oct 8 at 12:10 PM in the main exhibit hall as well as our breakout session Friday, October 9 at 2:30 PM. We will share how Cisco’s strategy of openness has helped the developer community.
To stay up to date on the latest version of the CiscoPuppet Module source code, visit this GitHub repository that allows network administrators to manage Cisco Network Elements using Puppet.
Tags: Cisco Nexus Switches, Cisco SDN, data center switching, devops, github, NX-API, NX-API REST, NX-OS, programmable networks, Puppet Labs
We made it…another successful Strata-Hadoop World show for Cisco’s Big Data & Analytics team. This year we had a few unique challenges – the Pope was leaving town when we arrived; then the UN General Assembly made traffic a bit more difficult than normal; finally towards week’s end the threat of ‘The Hurricane’ for the East Coast…
Cisco had an active presence at Strata this year with several newsworthy and interesting highlights:
• Introducing Cisco Data Preparation. Cisco Data Preparation (Data Prep) makes it easy for non-technical business analysts to gather, explore, cleanse, combine and enrich the data that fuels analytics. Read Kevin Ott’s Data Prep blog here. Read More »
Tags: Big Data, Cisco, Cisco Data Preparation, Cisco Data virtualization, Cisco UCS, Cloudera, Hortonworks, IBM, IBM BigInsights, Integrated infrastructure, Intel, MapR, nexus, Splunk, Splunk Enterprise, versastack
In part 1, part 2 and part 3 of my blogs, I talked about ACI differentiators compared to other network virtualization solutions.
For more tangible benefits on savings, customers can turn to this calculator.
We take a close look at the cost of acquisition. Meaning we calculate the amount of money customers have to spend on building their networks and servers.
Simple to use, this calculator requires only a few entries to describe your environment, and you’ll immediately be able to view the savings.
If you want to learn more about the different elements that went into the calculation, check out the ZK Research paper. ZK Research looks at the architectures of both ACI and NSX and how this impacts the cost of acquisition for networks (including licensing) and compute. The research compares large enterprise and midsize business.
And to get a better understanding on the traction Cisco ACI continues to have with customers, please take a look at the recent blog by Soni Jiandani, Sr VP, Marketing for the Insieme Business Unit at Cisco.
Tags: ACI, application networking services, data center, products, SDN, technology, virtualization
The Cisco UCS® C220 M4 Rack Server continues its tradition of Industry leadership with the latest announcement of the first top 2-socket performance for max- jOPS in a multiple–Java virtual machine (JVM) environment ahead of other vendors.
Some of the key highlights of Cisco’s new SPECjbb2015 benchmark results are:
- Cisco UCS C220 M4 server delivered a performance record of 92,463 SPECjbb2015 MultiJVM max-jOPS with 31,654 SPECjbb2015 MultiJVM critical-jOPS
- Cisco UCS C220 M4 Rack Server posted the best 2-Socket SPECjbb2015 Benchmark Result Ever for Multi-JVM max-jOPs.
- Cisco posted the first top 2-socket Multi-JVM max-jOPs performance result on the new SPECjbb2015 benchmark ahead of other vendors.
The benchmark configuration consisted of the benchmark controller, back-end, and transaction injector functions, each running on its own JVM. The JVM instances ran on a Cisco UCS C220 M4 Rack Server powered by two 18- core Intel Xeon processor E5-2699 v3 CPUs running a single instance of Red Hat Enterprise Linux (RHEL) Server 6.5 and 64-bit Oracle Java HotSpot Server Virtual Machine (VM) 1.8.0_60. Check out the Performance Brief for additional information on the benchmark configuration. The detailed official benchmark disclosure report is available at the SPECjbb2015 Website.
The new SPECjbb2015 benchmark has enhancements that align with the changes that you are experiencing in your own IT organization—thus giving you a more accurate capacity measurement than previous versions of the benchmark.
Let’s take a look at what this UCS C220 M4 SPECjbb2015 performance result means for the end users and customers…
When you choose Cisco UCS servers, customers benefit from the performance and rapid deployment capabilities of Cisco UCS.
Additionally you gain business advantages such as:
Accelerated response: Cisco tunes the chip sets and servers for specific workloads. With high-performance processors, large and fast memory configurations, and efficient use of Intel Turbo Boost Technology, the Cisco UCS C220 M4 delivers low latency and server optimization to JVMs.
Increased scalability: The benchmark results show that the Cisco UCS C220 M4 delivers excellent scalability to JVMs and applications.
Data center simplification: Cisco UCS delivers the scalability needed for large Java application deployments. The dramatic reduction in the number of physical components results in a system that makes effective use of limited space, power, and cooling resources by deploying less infrastructure to perform the same, or even more work.
Although all vendors have access to same Intel processors, only Cisco UCS unleashes their power to deliver high performance to applications through the power of unification. The unique, fabric-centric architecture of Cisco UCS integrates the Intel Xeon processors into a system with a better balance of resources that brings processor power to life. . For additional information on Cisco UCS and Cisco UCS Integrated Infrastructure solutions please visit Cisco Unified Computing & Servers web page.
SPEC and SPECjbb are registered trademarks of Standard Performance Evaluation Corporation. The performance record described in this document was valid based on results posted at http://www.spec.org as of September 23, 2015.
Tags: Cisco UCS, Cisco UCS Performance, SPECjbb2015
2nd Guest Blog by Ron Graham
Ron Graham had served as a Data Center Architect and Systems Engineer for some of the largest IT companies in the U.S. including Cisco Systems, NetApp, Sun Microsystems, and Oracle. He is currently working for Cisco Systems as a Big Data Analytics Engineer.
What I mean is, is your data not being used that much or is the temperature of the data going from hot to cold? Hot data is being used a lot and cold data is being used sparingly. I think every one runs into this problem at some point where they are store cold or frozen data on high performance compute resources. Does it make sense to move unused data to an archive directory as long as it is still in the same cluster and can still be accessed? In the majority of cases this makes sense.
We have hot data and cold data, so what about warm data? Warm data is giving off a moderate degree of heat and data is used less frequently than hot and more than cold. Take a look at the graph below. I interpolated the graph based on tech posting from Ebay and interviews with a former Disney admin.
On the business side, my analysis proved a 15.9% saving in CAPEX for a 1 petabyte (PB) Hadoop cluster. With a hot and cold storage ratio of 4:1, which means that 80% of my data will be on high performance storage platforms and 20% of my data on storage optimized platforms.
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