Avoiding a Hotel California Infrastructure: Data

Checks in but Never Leaves

Xangati Blog

Atchison Frazer Vice President, Marketing

July 24, 2015

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Large IT operations and hybrid-cloud organizations seem to implement infrastructure management

and automation tools in an ad hoc manner that yields isolated pools of data that never seem to leave

their respective silos nor interact with other siloed data.

Siloed data traditionally collected from these tools include Security/Compliance, DevOps, Cloud

Orchestration, Workload Automation and Infrastructure Management/Provisioning. Pretty soon, you

realize you’re a victim of tool sprawl and data creep, and a prisoner of your own device! You’ve

checked into the Hotel California, but your data can never leave!

If you’re adding monitoring tools one project or problem at a time, then your dev-teams are most

likely having to write scripts to automate standard IT operations tasks and monitoring, which

unfortunately influences a silo mentality that bleeds into tool adoption.

Cloud and virtualization initiatives across converged or shared environments add multiple layers of

complexity to management and automation of the underlying physical IT infrastructure. As a result,

automated provisioning and ongoing management of workloads (applications and IT services) are

also becoming overly complex and expensive to maintain.

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The key to successful infrastructure management and automation is to implement ‘service-aware’

tools, which understand the performance context of applications or IT services. Some tools that are

designed to specifically solve one silo problem (e.g., network automation), lack a broader cross-

domain context that would make them practical for automating virtualized services that typically

include server, storage and networking components.

Without that service-aware cross-silo intelligence capability in virtualization and hybrid-cloud

environments, admins can easily lose visibility to degrading conditions. Performance storms are

created by the unintended toxic interactions among cross-silo shared resources in the converged

data center.

A performance storm entangles multiple objects – such as VMs, hosts and applications – even if

they are unrelated. The entanglement often has a dramatically adverse effect on overall

infrastructure performance and end-user quality of experience.

Some of the most common performance storms include:

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Storage storms: typically occur when applications unknowingly and excessively share a datastore,

which causes storage performance to deteriorate.

Memory storms: usually occur when you have multiple VMs trying to share insufficient amount of

memory – or, in other cases, you might have a VM that is ‘hogging’ memory and not leaving enough

for the others even with ballooning in place.

CPU storms: typically occur when there aren’t enough CPU cycles or virtual CPUs to go around in

the sharing of processing resources, leaving some with more and some with less.

Network storms: usually occur when too many VMs are attempting to communicate at the same

time on a specific interface or when a few VMs are ‘hogging’ a specific interface with traffic – limiting

the ability of other VMs to send or receive data.

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To see what is causing a performance storm, you need visibility not only into how objects are

consuming cloud resources but also how objects are interacting with other objects within the

infrastructure. Consumptive silo-specific alerts (using a combination of system-learned heuristics

and sophisticated algorithms) point to the effects of performance storms – an impacted application

or VM, for example – while interactional cross-silo alerts give details to accurately identify and

resolve the source of the problem.

In order to deliver these interactional alerts – and reveal the usage overload interactions that may be

occurring between different objects – you must incorporate a cross-silo analysis of the entire, end-to-

end infrastructure – cutting across network, server and storage tiers, as well as applications, end-

points and end-users to provide a service-aware context.

Furthermore, this cross-silo analysis needs to mesh and scale so that you can easily view the

distant and proximate areas of impact for a given storm, as well as the source of contention and the

resources affected. Only by visualizing and analyzing the cross-silo interactions can you accurately

identify the trends and patterns of interactions that are causing the storm.

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If you’re thinking to yourself, “this could be Heaven or this could be Hell,” then light up a Xangati

dashboard and let its live, granular, cross-silo data show you the way – then you’ll be living it up at

the Hotel California!