Particularly with the introduction of internal clouds and self-service portals, IT organizations are faced with a new challenge of managing the pipeline of demand requests for workloads to be placed into virtual and cloud infrastructure.
Increasing demands from application owners for faster response times and readily available capacity, coupled with the threat of taking their apps "elsewhere" (to external clouds), have placed enough pressure on IT teams that they purposely over-provision infrastructure to stave off potential shortfalls.
In order to create an agile, efficient operation, IT teams need to abandon their spreadsheets and patchwork processes and adopt a new approach that enables them to systematically process requests for capacity and reserve it for that booking.
Why IT Needs a Capacity Reservations System
Andrew Hillier, CiRBA CTO & Co-Founder
Control the Demand Pipeline and
Reserve Capacity to Increase Agility
CiRBA's Control Console enables you to manage the demand pipeline through its Bookings Management System giving you greater visibility into coming demand to increase capacity forecasting accuracy, and as importantly, increase responsiveness with timely, accurate answers on what is going to fit and where.
CiRBA enables you to:
- Profile all inbound workload placement requests (self-service portals, transformation projects, etc.) and determine their policy and capacity requirements
- Determine where the workload should go and whether there is capacity available to fulfill the request on the planned deployment date
- Reserve or hold the capacity for that workload
- If there isn't enough capacity available, determine what action is required to fulfill the request such as adding a new host, increasing memory or CPU
- Factor bookings into forecasting so you have an accurate view into future requirements
Manage New Workload Placements to
Reduce Volatility and Increase Efficiency
Key to the ability to respond to requests for capacity is the ability to place workloads when they are ready to deploy. Most requests come with some duration of lead time. This means that a reservation needs to take a forward looking view and hold the capacity for that timeframe. It also means that the optimal workload placement might change from the time the request is made and capacity is held within a certain environment and when the deployment needs to be acted on. CiRBA's analytics enable you to hold capacity for the appropriate timeframe and then determine the placement that makes the best use of infrastructure while ensuring all policy and utilization requirements are met. Placement recommendations by CiRBA are also forward-looking to minimize placement volatility and motioning in an environment.
Leverage CiRBA's Control Console as a Fuel Gauge to
Reduce Costs and Risk
Many organizations are building new internal cloud infrastructure and filling these environments with a combination of already virtualized workloads and remaining physical workloads that are being virtualized through additional transformation initiatives. CiRBA helps organizations control the on-boarding process by helping organizations transform, size and place workloads, and by acting as a fuel gauge for these environments. Leveraging CiRBA in this way reduces risk by enabling you to make adjustments after workloads are in the environment in the case of unforeseen events or behaviors with newly virtualized workloads. It also ensures that the environment can remain optimally balanced as it "fills up" and organizations can increase agility in their purchasing cycles by moving from just-in-case provisioning (buying hardware based on guesstimates of when it will be required), to just-in-time provisioning (buying hardware with an accurate view of precisely when it will be required). For many organizations this results in significant savings.
Avoid Capacity Shortfalls by Incorporating Bookings
CiRBA enables you to incorporate both trends in your environment and bookings for individual workload requests as well as major transformations into capacity forecasts to increase accuracy. For more information, see the CiRBA for Capacity Forecasting.