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Load Balancing 101: Scale versus Fail

Elasticity is a design pattern for scalability, not necessarily failability.

One of the phrases you hear associated with cloud computing is "architecting for failure." Rather than build in a lot of hardware-level redundancy – power, disk, network, etc… – the idea is that you expect it to fail and can simply replace the application (which is what you care about anyway, right?) with a clone running on the same cheap hardware somewhere else in the data center.

Awesome idea, right?

But when it comes down to it, cloud computing environments are architected for scale, not fail.

lb-scale-vs-fail

SCALE versus FAIL

Most enterprise-class data centers have been architected with failure in mind; we call these high-availability (HA) architectures. The goal is to ensure that if any element in the data path fails that another can almost immediately take its place. Within a hardware platform, this implies dual power supplies, a high RAID level, and lights-out management. At the network and higher level, this requires redundant network elements – from load balancers to switches to routers to firewalls to servers, all elements must be duplicated to ensure a (near) immediate failover in the event of a failure. This generally requires configurations and support for floating (shared) IP addresses across redundant elements, allowing for immediate redirection upon detection of a failure upstream.

At the application/server tier, the shared address concept is still applied but it is done so at the load balancing layer, where VIP (virtual IP addresses) act as a virtual instance of the application. A primary node (server) is designated that is active with a secondary being designated as the "backup" instance which remains idle in "standby" mode*.

If the primary instance fails – whether due to hardware or software or network failure – the secondary immediately becomes active, and continuity of service is assured by virtue of the fact that existing sessions are managed by the load balancing service, not the server. In the event a network element fails, continuity (high-availability) is achieved due to the mirroring (replication) of those same sessions between the active (primary) and standby (secondary) elements. inoneminute1

Is it perfect? No, but it does provide sub-second response to failure, which means very high levels of availability (or as I like to call it, failability).

That's architected for "FAIL".

Now, most cloud computing environments are architected not with failure in mind but with scale in mind – that is, they are designed to enable elasticity (scale out, scale in) that is, in part, based on the ability to rapidly provision the resources required.

A load balancing instance is required and it works in much the same way as a high-availability architecture (minus the redundancy). The load balancing service acts as the virtual application, with at least one instance behind it. As demand increases, new instances are provisioned and added to the service to ensure that performance and availability are not adversely impacted. When this process is also capable of scaling back in by automatically eliminating instances when demand contracts it's called "elasticity".

If the only instance available fails, this architecture is not going to provide high availability of the application because it takes time to launch an instance to replace it. Even if there are ten active instances and one fails, performance and/or availability for some clients may be impacted because, as noted already, it takes time to launch an instance to replace it.

Similarly, if an upstream element fails, such as the load balancing service, availability may be adversely impacted – because it takes time to replace it.

But when considering how well the system responds to changes in demand for resources, it works well. That's scalability.

That's architected for "SCALE".

SCALE and FAIL are NOT INTERCHANGEABLE

These two are not interchangeable, they cannot be conflated with the expectation that either architecture is able to meet both goals equally well. They are designed to resolve two different problems.

The two can be combined to achieve a scalable, high-availability architecture where redundancy is used to assure availability while elasticity is leveraged to realize scale while reducing the time to provision and investment costs by implementing a virtual, flexible resource model.

It's important to understand the difference in these architectures especially when looking to public cloud as an option because they are primarily designed to enable scalability, not failability. If you absolutely need failability, you'll need to do some legwork of your own (scripts or manual intervention – perhaps both) to ensure a more seamless failover in the event of failure or specifically seek out cloud providers that recognize the inherent differences between the two architectures and support either the one you need, or both.

Relying on an elastic architecture to provide high-availability – or vice-versa – is likely to end poorly.


Downtime cost source: IT Downtime Costs $26.5 Billion In Lost Revenue

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More Stories By Lori MacVittie

Lori MacVittie is responsible for education and evangelism of application services available across F5’s entire product suite. Her role includes authorship of technical materials and participation in a number of community-based forums and industry standards organizations, among other efforts. MacVittie has extensive programming experience as an application architect, as well as network and systems development and administration expertise. Prior to joining F5, MacVittie was an award-winning Senior Technology Editor at Network Computing Magazine, where she conducted product research and evaluation focused on integration with application and network architectures, and authored articles on a variety of topics aimed at IT professionals. Her most recent area of focus included SOA-related products and architectures. She holds a B.S. in Information and Computing Science from the University of Wisconsin at Green Bay, and an M.S. in Computer Science from Nova Southeastern University.

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