Capacity Planning Plans Capacity Planning Operational Laws

Building an Efficient Capacity Plan

Steps to Create a Capacity Plan Define the Business Problem Define the Service Level Define the Current Capacity: Workload Characterization Plan the Future

Define the business problem Service Provider Vice and Data Drop Calls Rate =5% Service Level Acceptable Drop calls < 1%

More Business Data 55,000,000 subscribers Lagos issue with drop calls 1 Erlang= 60 minutes of talk BHT Busy Hour Traffic 5 pm 4 calls in average 5 minutes 1000 subscribers Erlang for the BTS = 1000 x 4 x 5 /60=333.333

Business problem Congestions in the core network Congested networks lead to packet loss, jitter and packet delays SLA to customer is 70% of guaranteed rate Issue: at peak time some customers experience less than 70% of guaranteed rate

More data Peak hour: 10 pm, 1000 customers connected 5 packages

Capacity Planning Input and Output Variables Processors, disks, network Max no of connections RT <= 2 sec Throughput >10 tps Basic Service Parameters Workload Evolution Desired Service Levels Capacity Planning Saturation Points Cost-effective Alternatives When are Service Level violated?

Business Problem Response time of CRM is too slow ~5 s SLA: 0.5 s – 1 s is acceptable

Application Server(s) End-Users Web Server(s) Application Server(s) Storage SAN Database Server(s)

Workload Characterization CRM: create customers, look up for customers, delete customers, search customers, analytical reports (data mining, big data, Hadoop) Classify workloads:

MTN Nigeria’s Capacity Plan Increasing the Performance and Efficiency of Your Capacity Planning

Capacity Planning Framework for Application X Workload From Application X System X Parameters Desired service Levels for Application X Saturation Points for system X Cost-effective Alternatives for System X

Business Models Resource Model

Application Y

Application Z

Business Problem X

Business Problem Y

Business Problem Z

Performance 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 Management A 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 can’t 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. It’s rare today to find a data center that’s all in with one vendor; it’s usually a mix of technologies from multiple different providers. Maybe a business used to be a VMware shop for its virtualization but it’s 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 management Another 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 Utilization Law The utilization (Ui ) of resource i is the fraction of time that the resource is busy. Ui = Xi * Si = i * Si Or Xi = i 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 The Forced Flow Law Xi = Vi * Xo XO = Xo/ Vi B is the length of time that the resource was observed to be BUSY. C is the number of request DEPARTURES observed. D Service Demand is the sum of all service times for a request at resource i N Q Queuing Time is the sum of all waiting times for a request at resource i R a system response Time or a Device Residence time S service time; period of time a request is receiving service from resource i, such as CPU or disk T is the length of TIME we observed the system. Vk the 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. X throughput of a device or a system Z is the think time of a terminal user Service Demand Law Di = Vi * Si = (Xi/Xo)(Ui/Xi) = Ui / Xo Dcpu = Vcpu * Scpu = Ucpu / Xserver Little’s Law X N Residence Time (R’i) at resource i is the sum of service demand plus queuing time. R’i = Qi + Di Response time (Rr) of a request r is the sum of that request’s residence time at all resources. Rserver = R’cpu + R’disk R N = R * X