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Bringing Order to Chaos through Data Center Automation


When Henry Ford installed the first conveyor belt-based assembly line in 1913, the car industry was forever changed. Enabled by this streamlined process, Ford workers could assemble the famous Model T in just 93 minutes - a dramatic improvement over previous production methods, which took almost 3 days to assemble one car. In addition, Ford only offered the Model T in one color - black, invoking a theme of consistency and standardization very early on in the industrial era.


Nearly 100 years later, enterprise data centers are on the verge of experiencing their own version of the automotive revolution. IT managers are tasked with effectively managing the infrastructure while addressing several operational challenges that contribute to inefficiencies in the data center. Many of these challenges can be traced to the following three factors confronting IT today:

  • Stringent availability requirements overlaid with “always-on” expectations. The bulk of the business world is carrying around a BlackBerry, Treo or some other handheld device that gives them the ability to respond to requests instantaneously. This created an expectation that everything must be available all the time, and that expectation has been passed on to IT infrastructure availability requirements. It’s the pace of the world that we live in today. More importantly, the value of information to the business has increased dramatically, as well as the sheer number of transactions processed. Consequently, the cost of downtime has skyrocketed and service level agreements (SLAs) have risen in stride to keep pace. This has further pressured IT to minimize downtime and drive efficiency wherever possible.
  • An increase in data center complexity. Over the years, the IT environment has become more complex due to the growing number of multitier applications, heterogeneous platform environments and the introduction of virtualization. Simply put, there are more moving parts in the data center, and it’s become increasingly difficult for IT managers to get their arms around the complexity and manage it effectively. They lack the visibility of what is running in the environment and how it’s all tied together. This lack of visibility has also increased change-related downtime and overall business risk and has decreased IT’s ability to implement changes rapidly and respond to the business.
  • Tight resource constraints that hold IT budgets and headcount flat. Large-scale enterprises have demanded that IT evolve from a traditional cost center to a source of strategic and competitive advantage that is cost-efficient and dynamic. This “do more with less” mandate has forced IT to rethink its approach to management and look to methods for optimizing its operations across the infrastructure.

Within these facts lies the challenge of managing today’s enterprise data centers, which is driving IT organizations to look to the assembly lines of traditional manufacturing to apply three key principles: first, keep the assembly line running at all costs; second, identify and automate high-volume repetitive tasks; and third, leverage the resulting innovation to increase productivity. In short, drive higher availability, automate IT processes, and leverage the staff and infrastructure utilization. Data center automation enables IT management to automate labor-intensive data center tasks, with the goal to enhance visibility and control, reduce errors, increase service levels and drive down cost. IT departments that do not take advantage of innovative data center automation (DCA) solutions will continue to struggle with operational inefficiency and find themselves on a collision course with chaos.


Strategic Challenges in Addressing Data Center Complexity


As data centers have coped with intensifying pressure to manage more infrastructure with less resources, a number of potential, nonautomated strategies have arisen delivering some significant benefits, but they are not without costs.


Many enterprises have chosen to go down the path of server, application and data center consolidation to achieve operational efficiencies and streamline management tasks. In fact, much of the rise of virtualization can be attributed to the desire to drive up server utilization and enhance the flexibility of the infrastructure.


While consolidation and virtualization both have their merits, these technologies also introduce a fair amount of risk to the IT infrastructure given today’s complexities. For example, many enterprises have experienced outages during their application migration or consolidation efforts due to unknown dependencies across the infrastructure. Virtualization drives up utilization and improves dynamic mobility, but also increases the number of users per server, potentially creating broad-scale single points of failure.


Add heterogeneous platform OSes and a lack of visibility into the mix, and the data center becomes a murky witches’ brew of management tools with limited scope to control the enterprise. The challenges become more apparent as enterprises throw more server horsepower at the problem, creating scale-out server models or virtual machine sprawl to accommodate business demands.


To solve these issues, some organizations have chosen to leverage customized management solutions to rein in data center complexity. However, these toolsets require dedicated development cycles and often do not account for next-generation data center architecture, leaving them subject to obsolescence or redesign, or limiting IT’s choices of future technologies. Other IT shops select vendor-specific framework tools, which lock the enterprises into inflexible processes based on the provider’s technology and create additional costs downstream.


Data Center Automation: A Strategy is Required


As technology options improve, the most forward-thinking companies are carefully assessing these options and are increasingly taking a strategic approach to data center automation (DCA). This approach entails looking at the challenges that need to be addressed today from an operational efficiency standpoint as well as planning for tomorrow’s architecture and the management toolset that will be required.


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