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Datacentre consolidation

The need to reduce IT running costs has placed datacentres at the forefront of consolidation and migration initiatives.

Central to any datacentre optimisation strategy is the issue of risk and the assurance that any planned change does not subject the business to undue disruption. When assessing requirements, a combination of performance, capacity and technology options will need to be taken into consideration and each has a host of challenges.

Traditional approaches of sizing requirements tend to be based on basic calculations and typically lack accuracy around forecasting user perceived application performance, latency and throughput, post the change. Therefore these approaches carry a high element of risk and usually require a phased implementation to ensure they are performing to expected results.

Why Sumerian helps

In contrast to traditional approaches, Sumerian IT Analytics provides a robust and fact-based framework on which to base your datacentre strategy requirements. Because IT Analytics provides quantifiable metrics by combining data-driven analytics with powerful scenario modelling, it enables an accurate prediction of the impact of change and supplies precise performance, cost and capacity quantification.

Baselining the IT estate

We capture and combine performance data from both the IT estate and end-user community to create a baseline "big picture" model of the current datacentre environment, its associated services and applications. This baseline model exposes the correlations between performance, capacity, technology and cost to indicate where applied changes can bring the most benefit from both an operational efficiency and cost saving standpoint.

Scenario modelling

From building a baseline view of the current position, IT Analytics enables an understanding of the before and after picture through the use of powerful scenario modelling and change analysis. The benefit of this is that IT organisations can effectively model the likely outcome of change before applying it, enabling objectives to be based on precise quantification of the impact of change, and providing clear answers that would be difficult to deduce using traditional sizing methods.

Sumerian scenario modelling for dataccentre consolidation covers three key areas:

Modelling performance

IT Analytics baseline view establishes the current latency and throughput performance of applications, based on current capacity requirements and technology platforms. By capturing existing usage at the packet level and applying cluster analysis, IT Analytics identifies common interactions and end-to-end behaviours that provide a precise understanding on whether applications will be impacted by additional network delay post change, and hence if there is any expected impact on end-user perceived performance.

Modelling capacity

In determining accurate future capacity, forecasting business growth and disaster recovery requirements, IT Analytics baselines the existing capacity situation by taking precise utilisation measurements from the datacentre infrastructure (network, servers, storage, HVAC) and their associated running costs. The captured data covers time of day variations, with a breakdown by key applications and services. By then applying scenario modelling IT Analytics can predict each chosen scenario’s outcome taking into account busy-hour differences, hidden users, technology options and the total bandwidth each option will require.
In sizing server and storage requirements IT Analytics models the headroom, technology options, server and rack count required for the proposed change scenarios, accurately predicting the required environment to meet future capacity needs. For power and HVAC requirements, IT Analytics modelling predicts server load relationships, energy costs and associated CO2 emission rate of the consolidated environment.

Modelling technology options

Many consolidation strategies will also encompass some switch to different technology platforms. In applying scenario modelling for network change scenarios, for example, cloud vs. point-to-point, IT Analytics can effectively assess the likely impact on capacity distribution. Similarly, comparisons involving MPLS against Ethernet will determine the impact on application latency and throughput performance between the two options. In terms of assessing migration to virtualisation technology, IT Analytics provides insight to determine whether fewer servers is likely to result in cost savings, providing robust evidence to support cases for its adoption.

Measuring success and ongoing management

Once consolidation efforts are completed, IT Analytics has the ability to track their relative progress and business impact by taking regular samples of performance data for ongoing management of service quality. The advantage of adopting an IT Analytics approach post consolidation is that it enables IT organisations to maintain ongoing control over datacentre resources and costs, helping to keep track of performance and capacity, and actively identifying areas where services can improve quality.