Are Your Capacity Management Processes Fit For The Cloud Era?

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1 Are Your Capacity Management Processes Fit For The Cloud Era? An Intelligent Roadmap for Capacity Planning

2 BACKGROUND In any utility environment (electricity, gas, water, mobile telecoms ) ensuring there is enough resource or capacity available to meet demands is critical in satisfying the needs of the consumer. At the same time, it is important to ensure that there is not an over supply of capacity in a market, as this will ultimately impact profitability and viability of the suppliers business model. These rules not only apply to traditional utilities, but also to the provisioning of IT infrastructure used to host applications that support the business. Virtualization accelerated this concept through its ability to share compute resources amongst multiple application workloads, and the agility it provides to rapidly provision and reconfigure compute resources through software. Industrialization of capacity management processes in virtualized data centers presents a significant opportunity to optimize ongoing CAPEX and OPEX, whilst assuring consistent delivery of application performance. It also ensures greater business agility by reducing the lead-time to stand up new application services. Whilst virtualization is a great enabler to get a better return on investment from virtualized IT infrastructure, many Enterprise IT departments and Service Providers have relatively immature capacity management processes and are not exploiting the latest innovations that would enable them to transform their situation. 2

3 ARE YOUR CAPACITY MANAGEMENT PROCESSES FIT FOR THE CLOUD ERA? TODAY S CAPACITY MANAGEMENT PARADIGM AND ITS PITFALLS Today, many organizations apply very simple principles to determine their requirements for compute capacity in their virtualized data centers. This is typically based on a resource allocation model which takes the total amount of memory and CPU capacity allocated to all virtual machines in a compute cluster and a level of over provisioning (e.g. 2:1, 4:1, 8:1, 12:1 ) is assumed in order to calculate the requirement for physical resources. The level of over provisioning is often directly related to different tiers of infrastructure and the service levels offered by these. For example, a Platinum Service level may be offered on a Compute Cluster that has a conservative overprovision ratio of 2:1, and a Bronze Service might be offered on a Compute Cluster with a more aggressive overprovision ratio of 12:1. The application owner will make a decision about the tier of infrastructure on which they want their application to run based on a trade off between the level of risk they are willing to assume and the cost. This process of capacity management is typically managed in spreadsheets or in simple databases that do not take into account the actual resource consumption driven by each application workload running in the operational environment. Spreadsheet planning is often augmented with simple monitoring tools that send alerts to Operations when resources within the virtual infrastructure cross predetermined thresholds, indicating the risk of performance issues. In such circumstances, Operations will be notified of performance risks and mobilize their technical resources to investigate potential issues and devise remediation plans. A capacity management strategy based on resource allocation and over provisioning ratios is further floored because application owners Figure 1: The average utilization in the cluster is less than 30%. typically over specify the amount of CPU and Memory resources their application is going to need when they request virtual machines. This invariably results in larger virtual machine configurations than are actually required to run the applications reliably, but because this capacity management strategy is based on allocation and not on actual utilization, it inherently corrodes the level of efficiency that can be driven from the underlying infrastructure. 3

4 Figure 1 shows a compute cluster implemented with a ratio of 4:1 over provisioning. Utilization levels are very low relative to the point where there would be any risk of resource contention that would impact application performance. The result is a highly inefficient use of resources. In compute clusters with higher ratios of over provisioning, there is greater risk of performance issues, because hypervisor schedulers focus on balancing resources based on the deviation of memory or CPU utilization across hosts within a cluster. They do not look at the problem from the perspective of how to best meet the current and historical resource requirements of the actual application workloads. In addition to this, they do not consider key resources such as CPU scheduling, Memory Ballooning and Swapping, I/O, and network resources in their decision of where to place workloads. In brief, they are simple much to the detriment of efficiency. The results of hypervisor scheduling can be seen in Figure 2, on a compute cluster with an over provisioning ratio of 10:1. These images were recorded after the failure of one of the hosts in a cluster running DRS, and demonstrate Figure 2: Hypervisor scheduling has created unnecessary risk and inefficiencies across the cluster. how a simple hardware failure can introduce business risk from the hypervisor scheduler s inability to accommodate application workloads. The result is far from perfect, as certain hosts are running at very high levels of utilization, and there is no contingency on these hosts to support dynamic changes in application workload demand, whilst others are underutilized. VMTurbo has benchmarked hundreds of virtualized data centers and commissioned independent surveys of 150 enterprise IT organizations by TechValidate. In sum, these studies reveal that in more than 80% of situations there are significant opportunities to drive much greater levels of infrastructure efficiency, drive down the unit cost of compute services, and improve the level of service delivered through the industrialization of capacity management. 4

5 ARE YOUR CAPACITY MANAGEMENT PROCESSES FIT FOR THE CLOUD ERA? COMPLEXITIES OF ACHIEVING A MORE DESIRABLE OUTCOME In order to achieve the Desired outcome of an effective capacity management strategy one where infrastructure efficiency is fully optimized whilst the risk of application performance issues is traded off with the level of investment in infrastructure there is a complex problem to be solved. At VMTurbo we refer to this problem as the Intelligent Workload Management Problem. This is a complex problem to solve because it involves holistically controlling resource allocation decisions by considering many different dimensions of resources, configuration and business constraints across the virtualized IT stack, on an ongoing basis. These dimensions include the following: Multiple Infrastructure Resources Virtualized application workloads depend on multiple resources in the infrastructure and they must be able to get the resources they need to operate reliably. This includes resources such as allocated vmem, vcpu, and disk space, as well as physical compute and storage resources like memory, ballooning, swapping, CPU capacity, CPU scheduling, storage I/O, disc space, and network resources. All of these and more must be considered holistically when making resource allocation decisions in order to avoid the pitfalls of overprovision-ratio strategies. These misguided approaches optimize for one type of resource only to have a detrimental impact on other resources. Infrastructure Constraints The way that networks and storage are provisioned constrains where specific workloads can be placed and moved within the environment. These constraints should be taken into account when making resource allocation decisions to reflect what is really possible in the real world. Business Constraints Organizations typically have business policies that also restrict where workloads can run within the environment. These constraints may assist business continuity by ensuring that redundant application instances do not co-exist on the same physical resource. They may also support software-licensing requirements or adhere to security policies. These rules, while essential, constrain how the available compute resources can be exploited. Time When making resource allocation decisions, you need to consider the time dimension of the past, because application workloads fluctuate and peak at different times, driven by fluctuations in the business process they support. They may also grow or contract over time in their consumption of resources. These characteristics should be factored into any workload placement and resource allocation decisions. The Future - Most organizations are constantly changing their virtualized data center, driven by the need to deliver new projects, execute ongoing tech refreshes, and/or support organic growth in demand of existing applications. Having the ability to look at the past and accurately predict the impact of changes in the future is critical in planning what resource allocation decisions should be taken to accommodate planned changes. 5

6 Complex Puzzle What resource allocation decisions should be taken on an ongoing basis to accommodate application workload demand? The net reality is that in an environment with just a few hundred virtualized application workloads, thousands of variables need to be continually analyzed to derive the decisions that must be taken on an ongoing basis to maintain the environment in the Desired State. This puzzle is not one that can be solved by human brainpower at least not within the necessary time constraint and therefore most organizations accept the costly tradeoff of over investing in infrastructure capacity to try and mitigate risk. Even when organizations do this, they may still experience quality of service issues due to complexity of the Intelligent Workload Management Problem. The alternative to overinvestment is to employ state of the art capacity management software that employs a mathematical approach to solve the Intelligent Workload Management Problem. DEFINING FUNCTIONAL REQUIREMENTS FOR YOUR PERFORMANCE & CAPACITY MANAGEMENT STRATEGY When architecting your performance and capacity management strategy, it is important to differentiate between the monitoring/reporting functions and other resource management functions that solve the Intelligent Workload Management problem. Both have valid use cases, but provide a different outcome. MONITORING AND REPORTING There are literally hundreds of toolsets in the marketplace today that provide monitoring and reporting capabilities, and they are a necessity in all mission-critical IT environments. Many of the solutions available today have embedded analytical capabilities to assist users in turning large volumes of monitoring data into information that can be consumed by IT Operations and Engineering. Examples of data reduction techniques include threshold alerting based on real time exceptions, or trending in resource consumption to predict when resources will run out in the future. More recently, solutions in this space have embedded analytics that focus on anomaly detection. These solutions seek abnormal patterns in IT monitoring data, and infer pending issues that could impact service delivery. These types of analytics can be classified as Data Analysis. 6

7 ARE YOUR CAPACITY MANAGEMENT PROCESSES FIT FOR THE CLOUD ERA? Whilst these solutions may focus Operations on potential issues that could impact service delivery, they do not provide the Decision Analytics required to determine what actions should be taken to prevent and/or resolve resource contention and/or drive greater efficiency from the IT infrastructure. In aggregate, these decisions could range from the hundreds to the thousands for any single point in time. The inability of any human to not only interpret Data Analysis output, but also act on such output highlights an important gap that can be addressed by a new category of solution. RESOURCE ALLOCATION CONTROLLER Virtualization has enabled us to change the paradigm of how we manage IT service delivery through the ability to dynamically allocate resources through software controls. Whilst hypervisor scheduling is marketed as a mechanism to control resource allocation in virtualized data centers, the scope of decision-making and control they provide is very limited. What is required to solve the Intelligent Workload Management Problem as part of a performance and capacity management architecture is a discrete mechanism to perform Decision Analysis and Control, capable of accounting for all of the resources and constraints described earlier, and driving a broad set of actions across the IT stack to continually maintain the environment in the Desired State. These actions should include decisions on where to place application workloads and how to allocate, provision, or decommission resources, all based on the changing picture of application workload demand and priorities. By maintaining the environment in the Desired State, these functions prevent resource contention by sustaining the service levels of virtualized applications, and cut down the number of problems and incidents that Operations must handle. In addition to these benefits, Desired State functionality also drives greater adoption of existing service management and monitoring systems within the Operations community, because it eliminates a significant level of noise within the user communities of these systems. Maintaining the environment in the Desired State also dramatically reduces the cost of delivering compute services, by significantly increasing the efficiency of the provisioned infrastructure. TechValidate s survey of 150 VMTurbo customers revealed more that 80% experienced improvements of 20-40% Figure 3: Reduced risk and greater headroom to provision additional workloads driving higher levels of efficiency. 7

8 in infrastructure efficiency. When contextualized by the growth in the number of virtualized applications workloads experienced in most organizations, and the incremental infrastructure required to support this, efficiency improvements of 20-40% can have a very significant impact on future CAPEX requirements, making the investment required to deliver Decision Analysis and Control functions completely self funding in just months. Figure 3 shows the improvement that intelligent workload placement decisions had on a compute cluster bearing a 12:1 over provisioning strategy. The risks of application performance issues were significantly reduced and a more efficient use of resources was achieved. In a cluster with a 4:1 over provisioning strategy, intelligent workload placement decisions enabled the application workloads to run on a significantly smaller hardware footprint whilst mitigating the risk of performance issues (Figure 4). Figures 5 and 6 illustrate examples of the types of resource allocation Figure 4: Existing workload can be accommodated on a much smaller hardware footprint without impacting service. decisions that can be driven through Decision Analysis & Control functions to control the environment in the "Desired State", assuring applications get the resources they need to operate reliably whilst maximizing infrastructure efficiency. Figure 5: The resource management decisions that need to be taken to address existing and future performance risks and inefficiencies. 8

9 ARE YOUR CAPACITY MANAGEMENT PROCESSES FIT FOR THE CLOUD ERA? Figure 6: Consolidate workloads on fewer physical resources and reclaim unused virtual resources. SUMMARY 1. Review your organization s current capacity management strategy, and identify opportunities to transform the approach by better aligning resource supply and demand. 2. When assessing your performance and capacity management capabilities as part of systems management strategy, make sure you differentiate between monitoring and reporting functions from the Decision Analysis and Control functions required to proactively maintain your environment in the Desired State on an ongoing basis. 3. Deploy a Proof of Concept in an operational environment to benchmark the business benefit that can be realistically derived by maintaining your environment in the Desired State, reducing risk and driving greater operational and infrastructure efficiency. 9

10 ABOUT VMTURBO Founded in 2009, VMTurbo is a company founded on the belief that IT operations management needs to be fundamentally changed to allow your organization to unlock the full value of today s virtualized infrastructure and cloud services. Our charter is to transform IT operations in cloud and virtualized environments from a complex, labor intensive, and volatile process to one that is simple, automated and predictable delivering greater control in maintaining a healthy state and consistent service delivery. VMTurbo offers an innovative control system for virtualized data centers. By leveraging the dynamic resource allocation abilities of virtualization and automating decisions for resource allocation and workload placement in software, our solution ensures applications get the resources required while maximizing utilization of IT assets. Over 9,000 enterprises worldwide have selected VMTurbo, including British Telecom, Colgate, CSC and the London School of Economics. VMTurbo is headquartered in Massachusetts, with offices in New York, California, United Kingdom and Israel. 10

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