You are probably thinking that CITEIS is a typo – but it’s not. In fact, CITEIS stands for Cisco IT Elastic Infrastructure Services and it’s the name that Cisco’s IT department coined for our internal private cloud.
You can read more about CITEIS here, including an explanation of the two options: CITEIS “Express” for on-demand access to virtual compute resources from a shared pool of resources; and CITEIS “VDC” (Virtual Data Center) to provision your own virtual data center with a reserved pool of compute, storage, and network capacity.
We recently recorded a brief demo video of the Express version so you can see how it works:
They say that data about your data is more important than the data itself. Having the right data in the data warehouse at the right time or loaded up for Hadoop Analysis is critical. I have heard of stories where the wrong product was sent to the wrong store for sale due to incorrect conclusions on what was selling best. This was due to reports and decisions being made on the wrong data. This can be a resume impacting decision in this modern world of data driven product placements around the globe. In previous blog about Enterprise Job Scheduling (aka Workload Automation) http://blogs.cisco.com/datacenter/workload-automation-job-scheduling-applications-and-the-move-to-cloud/ I discussed the basic uses of automating and scheduling batch workloads. Business intelligent, data warehousing and Big Data initiatives need to aggregate data from different sources and load them into very large data warehouses.
Let’s look into the life of the administrator and operations of a workload automation tool. The typical Enterprise may have thousands if not ten thousands of job definitions. Those are individual jobs that get run: look for this file in a drop box, FTP data from that location, extract this specific set of data from an Oracle database, connect to that windows server and launch this process, load this data into a datawarehouse using Informatica PowerCenter, run this process chain in SAP BW and take that information to this location. All this occurs to get the right data in the right place at the right time. These jobs are then strung together in a sequences we in the Intelligent Automation Solutions Business Unit at Cisco call Job Groups. These groups can represent business processes that are automated. They many have 10’s to hundreds of steps. Each job may have dependency on other jobs for completion. The jobs may be waiting for resources to become available. This all leads to a very complex execution sequence. These jobs groups run every day; some run multiple times a day, some only run at the end of the quarter.
The typical IT operations team has a group of people that design, test and implement these job groups by working with people in business IT that design and implement business processes. Often times these job groups need to finish by a certain time to meet the needs of the business. If you are a stock exchange some job groups have to finish say in so many hours after the market closes. If you have to get your data to a downstream business partner (or customer) by a certain time you become very attached to watching those jobs execute. No pun intended, your job may be on the line.
A new technology has hit the scene for our customers of the Cisco Tidal Enterprise Scheduler. It is called JAWS Historical and Predictive Analytics. http://www.termalabs.com/products/cisco-tidal-enterprise-scheduler.html . These modules takes all historical and real time performance data information from the Scheduler and through a set of algorithms produce historical, real-time, predictive, and business analytics historical and predictive analytics. This is the data about the data I mentioned previously. Our customers can do what if analyses as well as get early indication that a particular job group is not able to finish in time. The administrators can take action before it is too late. This is critical in getting the data in the right place so that analytics can be performed correctly and therefore not sending 1000 of the wrong product to the wrong store location. Thanks to our partners from Terma Software Labs http://info.termalabs.com/cisco-systems-and-terma-software-labs-to-join-forces-for-more-sla-aware-workload-processing/ .
IT shops deploying clouds over the past year have been focused on Infrastructure as a Service ( http://en.wikipedia.org/wiki/Infrastructure_as_a_service#Infrastructure ) as a way to drive speed in virtual and physical server provisioning, cost savings in operations, proactive service level agreements, and increased control and governance. In one of my blogs I introduced our Cisco Intelligent Automation for Cloud http://blogs.cisco.com/datacenter/the-secret-is-now-out-you-can-simplify-cloud-deployments-with-cisco-unified-management/ and how that addresses both private, hybrid and public clouds IaaS. Key to this is the service catalog and self service portal. Moving to cloud is NOT about taking hundreds of server configuration templates and moving to them immediate self service. All you are doing in that model is automating VM sprawl. They key is defining a limited set of services and options that your end users such as application owners and technical folks can order through a self service portal and manage their life-cycle.
Cloud Connect is (as you might expect) all about cloud. At VMware Partner Exchange, you’ll learn about virtual desktops, virtual workspaces, and VXI with Cisco and VMware View. But you can also learn about more about Cisco and VMware’s complementary cloud management solutions – to help our customers accelerate their journey to cloud computing.
You may have heard about the Cisco IT internal private cloud (CITEIS – Cisco IT Elastic Infrastructure Services) by now. At VMworld last fall, our IT team gave a presentation on how they deployed Cisco Intelligent Automation for Cloud together with VMware vSphere and vCloud Director for this successful initiative:
If you’re at VMware Partner Exchange this year, you’ll have an opportunity to learn how we did it, how it works, why it delivered great results, and how you can deploy a similar solution. Just make sure you visit Cisco in booth 308 to see a demo of cloud management with Intelligent Automation – and attend our sessions below.
In an earlier part of my career I learned the extreme importance of Workload Automation, aka Job Scheduling. Workload automation is the oldest IT technology on the planet coming from the need to schedule jobs on an IBM Mainframe. Job Scheduling has evolved from driving JCL (Job Control Language) to Workload Automation where the Scheduler stitches together batch and real time activities across mainframes, proprietary OS systems, x86 systems, applications (both packages and commercial off the shelf such as SAP or Oracle or Informatica) and now web service enabled applications whether they be onsite or in the cloud. Walk into the operations center of any data driven company and you will see multiple screens where operations are monitoring the state of these jobs. Why are they so critical? Over 50% of all transactions that occur on this planet are batch in nature. They are scheduled based upon specific times or based upon dependencies being met. These workloads can be a complex and interrelated set of activities. Effectively these job streams are the business processes that drive modern enterprises.
Without these jobs companies don’t get information (and large amounts of it) in the right place at the right time. Most companies today could not close out their financial quarters without enterprise schedulers to move data from their disparate systems into a consolidate place for either the general ledger to close out or for a critical Business Intelligence report to run to drive placement of the correct product into the specific physical location to serve the global economy. Workload automation tools open and close stock exchanges and process all the transaction data from trades. They also drive compliance checks. This is important stuff for the global economy! This was my realization in touring key operations centers and realizing that half of the big monitors were covering the movement of batch data in the enterprise.