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Load Balancing in a Cloud

How should auto-scaling work, and why doesn’t it?

Although “rapid elasticity” is part of NIST’s definition of cloud computing, it may be interesting to note that many cloud computing environments don’t include this capability at all – or charge you extra for it. Many providers offer the means by which you can configure a load balancing service and manually add or remove instances, but there may not be a way to automate that process. If it’s manual, it’s certain “rapid” in the sense that’s it’s probably faster than you can do it (because you’d have to acquire hardware and deploy the application, as opposed to simply hitting a button and “cloning” the environment ‘out there’) but it’s not necessarily as fast as it could be (because it’s manual) nor is it automated. There’s a number of reasons for that – but we’ll get to that later. First, let’s look at how auto-scaling is supposed to work, at least theoretically.

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1. Some external entity that is monitoring capacity for “Application A” triggers an event that indicates a new instance is required to maintain availability. This external entity could be an APM solution, the cloud management console, or a custom developed application. How it determines capacity limitations is variable and might be based solely on VM status (via VMware APIs), data received from the load balancer, or a combination thereof.

2. A new instance is launched. This is accomplished via the cloud management console or an external API as part of a larger workflow/orchestration.

3. The external entity grabs the IP address of the newly launched instance and instructs the load balancer to add it to the pool for resources for “Application A.” This is accomplished via the standards-based API which presents the configuration and management control plane of the load balancer to external consumers as services.

4. The load balancer adds the new application instance to the appropriate pool and as soon as it has confirmation that the instance is available and responding to requests, begins to direct traffic to the instance.

This process is easily reversed upon termination of an instance. Note: there are other infrastructure components that are involved in this process that must also be notified on launch and decommission, but for this discussion we’re just looking at the load balancing piece as it’s critical to the concept of auto-scaling.

The important thing to note here is that the process for adding – and subsequently using – the new application instance to the load balancer should be automatic. Once the instance is  launched, all that remains is to inform the load balancer that a new instance is available. The load balancer should take care of the rest, and it should do so transparently. That means no interruption of service, no reboot, no reload the configuration, nothing. Similarly the termination and removal of the application instance from the pool of resources should be as seamless and non-disruptive. It does not matter whether the load balancing solution is hardware or a VNA (Virtual Network Appliance) as long as it has an API through which available resources can be added and removed on-demand.

This process is relatively straightforward and can even be accomplished without the “external entity” by directly integrating the application with the load balancing service. By hooking the appropriate setup and tear-down routines in imagethe application, the load balancer can be automatically instructed to perform the appropriate actions whenever an instance of that application is launched or terminated.

1. Application is initialized and instructs load balancer to add it to the appropriate resource pool.

2. Load balancer adds the application instance to the right pool of resources and begins directing traffic to it as configured.

3. Application is terminating, instructs load balancer to remove it from the available resource pool.

4. Load balancer removes the application instance from pool of resources.

This requires tighter coupling of the load balancer with the application, however, which may be less than ideal unless you are very tied to your load balancing solution. The advantage of this solution is that regardless of whether the application is “physical” or “virtual”, the same process occurs, which lets you mix and match and be a lot more flexible in your architecture.


WHY is THIS so HARD THEN?

It’s not, if the load balancer has an API. Herein lies the problem: building out an automated, dynamic infrastructure requires the ability to communicate, to collaborate, to integrate network and application network infrastructure with the data center management system. That means some sort of API is required. Oh, you could do it with a little SSH and a local script, but that’s not nearly as efficient or flexible as using an API that’s designed to be leveraged for integration and automation in the first place.

While just about every hardware load balancer (and their virtualized equivalents) is enabled with a standards-based control plane, most web-server-turned-rudimentary-load-balancers  are not enabled with an API that allows this level of integration. That means hacking the system with scripts that inject or remove configurations, forcing reloading of the configuration (or worse, stopping and starting the system) and most likely interrupting service. Not only does it mean hacking the system but the scripts must somehow pull from customer-specific meta-data information in order to properly configure the load balancing solution.

 

 

So in order to achieve this in a way that maintains availability and is easy to use and essentially a multi-tenant solution needs the services of an API. When it comes to integration there are few options and the most flexible method is to use a standards-based API to manage a solution that supports the notion of multi-tenant configuration natively. The problem is, of course, that most of the API-enabled (and therefore easy to integrate and automate) load balancers cost money. It requires an investment, up front, and many folks just aren’t willing to do that. Except they are doing that; they’re just trading capital expenses for longer-term development of scripts and research that approximate (but never quite reach the reliability) of an API that’s been specifically developed, tested, and deployed for the purpose of integrating with external management systems and scripts. What’s worse is that eventually most of these solutions developed on sweat and scripts will end up turning to a more sophisticated, proven API-based solution anyway because of the inherent challenges associated with scalability of the solution itself. That results in a financially inefficient way to go about building a dynamic infrastructure that supports rapid, elastic scalability.

So many organizations faced with the choice of investing in a more robust, flexible solution when they’ve already sunk a lot of time, effort, and money into their existing “free” solution, simply continue to hack and paste and shove a square peg into a round hole, which means auto-scaling never works quite as expected and advanced features come at a snail’s pace because of the difficulty in scripting and integration.

It doesn’t have to be so hard, but you do have to view load balancing for what it is: a key component of the cloud computing strategy, and give it the focus it deserves, even if that means it’ll cost you more up front because in the end it’ll cost you far less than the alternative.

Read the original blog entry...

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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