Capacity Monitor

based on the xPactor Frameworks functions

Capacity Management – what is it?

Capacity management analyses capacity requirements. It plans and manages resource usage whilst always taking into account profitability and performance requirements of services. It provides methods for the monitoring of service capacity and performance.

The Capacity Management process must operate at three levels: on business, service and resource level. Performance and capacity requirements can are derived from the requirements business expects of services, and transferr these into the resources required.

If demand fluctuates, business and service capacity management must detect risks or changes early. At the resource level, capacity management the needs for efficiency and service performance must be balanced.

Capacity management at the business level: provides capacity planning based on resource requirements forecasts, including a defined level of security (Planning) provides models for scenario analysis (modeling) and determines the capacity necessary to support the business processes. (Source: Federal Office for Security in Information Technology)

Our Capacity Monitor correlates and monitores resource requirements, allocation and planning. Impact in case of deviations from plan or other risks are presented on different levels (business, service, resource) .

  • in the case of highly automated service providers, such as in the telco industry, a set of decisions must be made during the sales process
    • are the required resources available?
    • will a given sale result in additional procurement?
    • can I assure the agreed installation and commissioning time?
  • the input for such decisions are usually stored in various operational tools (ERP systems, CRM, inventory / CMDB, Forecast and Demand tools, etc.) and complicated to condense on the required granularity
  • for each of these questions the effective situation at installation date must be considered if the answer is to be reliabl:
    • what is my resource pool in the future?
    • what resources do I need in the future when taking into account existing contracts and procurement?
    • which resources are released when (eg by other customers cancelling services)?
  • in many cases, the needed data is collected manually with great overhead
    • the process is error-prone
    • the resulting data is incomplete
  • Available tools are mostly based on manually filled spreadsheets, sometimes technologies and products based on infrastructure monitoring
    • these follow the buttom-up approach
    • have inherently complicated rule definitions and resource allocation
    • are expensive (manual), repetitive and error-prone
    • always show a technology perspective
    • dependencies are not or insufficiently shown
    • lack automation
  • missing functionality
    • top down approach (from the customer to the infrastructure)
    • calculating the last possible order dates for critical resourcesbased on lead times
    • automated alerting / information on the need for capacity expansion
    • illustration of dependencies between resources that are possibly tracked in different systems

critical success factors for Capacity Management

As service provider you must always be answer to answer the following questions:

WHO uses WHAT?

which service uses which and how many resources?

which amount of resources are used by which service type?

WHAT has WHICH limitation?

What are the rules to governing resource usage and allocation?

WHAT is the current resource situation?

Which resources SHOULD be available where?

Which resources ARE effectively available where?

What is the delta between SHOULD and ARE, between theory and reality?

WHAT will be needed WHEN and WHERE?

Which resources MUST be available at time X in the future?

  • based on past trends
  • based on effective order intake
WHAT is agreed?

What are the contractual obligations governing capacity availability and extension?

WHAT will be available WHEN?

Which capacity changes are planned and being processed?

When will additional capacity be provided where?

Solution must be robust

  • No data loss
  • short maintainance times
  • data security

Solution must perform

  • quick loading of new service or capacity models: minutes for models with 50.000 or more elements
  • data aggregation in big models should be in “near real time”: aggregation and propagation in range of seconds for big models

Solution must be easy to use

  • a few clics to reach relevant data
  • results must be comprehensible, the calculation understandable
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Solution must scale

  • from little to big trees and data volumes

Solution is based on open Interfaces

  • Usage of data communication standards
  • minimal amount of interfaces