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Capacity Plan

Capacity Planning Plans

Capacity Planning Operational Laws

Building an Efficient Capacity PlanSteps to Create a Capacity PlanDefine the Business ProblemDefine the Service Level Define the Current Capacity: Workload CharacterizationPlan the Future Define the business problemDefine the business problemService ProviderVice and DataDrop Calls Rate =5%Service LevelAcceptable Drop calls < 1%More Business Data55,000,000 subscribersLagos issue with drop calls1 Erlang= 60 minutes of talkBHT Busy Hour Traffic 5 pm4 calls in average5 minutes1000 subscribersErlang for the BTS = 1000 x 4 x 5 /60=333.333Business problemCongestions in the core networkCongested networks lead to packet loss, jitter and packet delaysSLA to customer is 70% of guaranteed rate

Issue: at peak time some customers experience less than 70% of guaranteed rate

More dataPeak hour: 10 pm, 1000 customers connected5 packages

Capacity Planning Input and Output VariablesWorkloadEvolutionBasicServiceParametersDesired Service LevelsCapacity PlanningSaturation PointsCost-effectiveAlternativesWhen are Service Level violated?Processors, disks, networkMax no of connectionsRT 10 tps8Business Problem Response time of CRM is too slow ~5 sSLA: 0.5 s 1 s is acceptable

Web Server(s)Application Server(s)Database Server(s)Storage

SAN

End-UsersWorkload CharacterizationCRM: create customers, look up for customers, delete customers, search customers, analytical reports (data mining, big data, Hadoop)

Classify workloads: MTN Nigerias Capacity Plan Increasing the Performance and Efficiency of Your Capacity PlanningCapacity Planning Framework for Application XCapacity PlanningWorkloadFrom Application XSystem XParametersDesiredserviceLevels for Application XSaturationPoints for system XCost-effectiveAlternatives for System XBusiness ModelsResource Model

Application YApplication ZBusiness Problem XBusiness Problem YBusiness Problem ZPerformance and capacity mgt in a Software Defined Datacentre (SDDC)

Most important, the SDDC enables automated, policy-driven provisioning and management of data center resources.

Program interfaces make it possible for applications to request resources based on clearly defined rules and policies. The result:

A more responsive, agile, secure and high-performing data center that takes full advantage of the underlying hardware

Three keys to having an SDDC

1) Capacity management

2) Multi-virtualization and multi-cloud management platforms

3) Configuration management

Capacity ManagementA SDDC is about rapidly provisioning hardware to users. But, a critical element to that is to ensure there is enough capacity to provision.

One of the first steps in a SDDC migration is to ensure that your data center/IT shop has enough capacity for the needs of the organization, applications and services.

You cant automate the provisioning of resources unless you have enough resources to serve the business.

Level setting the needs of the business and ensuring you have the capacity is an essential first step.

There are a variety of tools that can help with this, including ones from BMC (PractiveNet), CA Performance Management and even smaller providers like VMTurbo. Multi-virtualization and multi-cloud management platforms

Data centers will have complicated architectures. Its rare today to find a data center thats all in with one vendor; its usually a mix of technologies from multiple different providers.

Maybe a business used to be a VMware shop for its virtualization but its recently begun using Microsoft Hyper-V. Maybe it has used Amazon Web Services, but it wants to start using a private cloud from a more niche service provider.

A key to managing this complex, heterogeneous environment is to have a multi-virtualization and multi-cloud management platform

Increasingly, cloud management vendors are embracing a strategy of supporting multiple platforms.

Configuration managementAnother key to a true SDDC approach is to move from a manual to automatic provisioning of resources. This would be exemplified by an operations professional getting the specifications of an application or service, then setting up the hardware on a case by case basis. The more efficient alternative is to instead automatically provision based on the applications need.

This is basically the idea behind a devops mentality, meaning that developers and operations folks work much closer together. Tools like Puppet and Chef help companies achieve automatic provisioning. Whereas capacity management will ensure there are enough resources to provision, configuration management will automatically allocate those resources without the need to have manual scripts.

Overall, the idea of the SDDC is that it provides an additional layer of abstraction above the hardware components, public and private cloud, which empowers applications to define their own environments, based on performance, security, availability and further policy requirements."

Capacity Management and Software-Defined Data Center

Capacity Planning Operational Laws B is the length of time that the resource was observed to be BUSY. C is the number of request DEPARTURES observed. DService Demand is the sum of all service times for a request at resource iNQQueuing Time is the sum of all waiting times for a request at resource i

Ra system response Time or a Device Residence timeSservice time; period of time a request is receiving service from resource i, such as CPU or diskT is the length of TIME we observed the system. Vkthe visit ratio, is the number of times device k is visited per transaction.W is the ACCUMULATED TIME for all requests within the system time spent both waiting for and using resources. Xthroughput of a device or a system Zis the think time of a terminal user UTILIZATION LAW Ui = Xi So

LITTLE'S LAW N = X R

RESPONSE TIME LAW R = N/X Z

THE FORCED FLOW LAW: Xi = Vi Xo

Residence Time (Ri) at resource i is the sum of service demand plus queuing time. Ri = Qi + DiResponse time (Rr) of a request r is the sum of that requests residence time at all resources. Rserver = Rcpu + RdiskUtilization LawThe utilization (Ui ) of resource i is the fraction of time that the resource is busy.

Ui = Xi * Si = i * Si

Or Xi = i The Forced Flow LawXi = Vi * XoXO = Xo/ Vi

Service Demand LawDi = Vi * Si = (Xi/Xo)(Ui/Xi) = Ui / Xo Dcpu = Vcpu * Scpu = Ucpu / XserverXRNN = R * XLittles Law`

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