The Journey Toward the Software-Defined Data Center - Cognizant

Standard Data Center AttributesDesigned to LastHeterogeneousCompute FlavorsSilo ManagementToolsMix Physical andVirtual ComputeLegacy ComputeMultitude of StoragePlatformsNetworkedto FunctionFigure 1perform monitoring and management functionsacross server farms. Moreover, many use aseparate set of tools for managing network andstorage performance. This results in enterprisedata centers that lack a single, unified view ofresource availability across infrastructure, to theapplication and database layers.The infrastructure is designed and provisionedconsidering the specific volumetric for supportingthe business applications and considering thepeak load transaction in jobs per second, availabilityand scalability requirements. When volumetricand projected growth do not manifest asenvisaged, this method of sizing infrastructurecompute and storage could lead to either undersizingor oversizing the footprint. Often, havingsuch islands of infrastructure compute andstorage leads to underutilization of resources.This has a cascading effect on investment andthe effort expended toward energy consumption,management overheads, software licenses anddata center costs.The shortcomings of this model led many enterprisesto the next wave of infrastructure design— utilizing shared infrastructure services andvirtualized compute to increase efficiency inresource utilization and ensure that infrastructureis designed and fit for the purpose, and notover-engineered. Figure 2 depicts how a sharedinfrastructure delivery design is leveraged, consideringguidelines such as grouping of applicationswith similar workload and grouping ofline-of-business applications, with virtualizedcompute resources. In this model, applicationsreap the benefit of virtualization, with hypervisorsenabled to programmatically allocate and deallocatecompute resources for applications. Thebare metal hardware controlled by the hypervisorsoftware can be further partitioned and guestoperating systems with logically separate instancescan be instantiated, resulting in increased utilization.This model has its advantages in terms of howresources are efficiently utilized in ideal applicationworkloads. However, when one or more applicationworkloads begin to consume more resourcesthan expected, scenarios could arise whereseveral guest operating systems are short ofcompute resources, thereby impacting businessapplication service level agreements.While this approach brought holistic capacitymanagement, monitoring and tools capabilities,it also provided evidence that infrastructurecompute and server resources were truly benefitingfrom improved resource utilization andautomation. This was brought about, to a certainextent, by programmatically controlling theresources provided to guest instances. However,new thinking about solutions was still needed tomeet the challenges of dynamic workloads of runthe-businessapplications and compute-intensiveenterprise applications.With the emergence of the cloud, the new age“mantra” and infrastructure as a service (IaaS) asa delivery model (as illustrated in Figure 3), thechallenges of processing demands from dynamicworkloads is being addressed. Designing highavailabilityclusters and scalable solutions can bearchitected based on nonfunctional requirements.cognizant 20-20 insights2