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HP SaaS and the Cloud: Seeing the Light

nashizawa on ‎03-29-2010 08:47 PM

Wouldn’t it be easier if you kept all of your lights on in your house all of the time? Think how cool that would be. No more getting out of bed to turn lights on. No more fumbling around aimlessly for a light switch. No more banging your shins on furniture. By keeping every light on, you’d be assured that whenever you need light, you’ll have light. 

Sure you’ll be over-provisioned 99% of the time, but hey, who’s getting tired of bruised shins? 

Well, if home lighting worked like traditional IT, this actually wouldn’t be a bad model. You could keep all the lights on to avoid extensively long light procurement cycles when demand increased. You’d pay for the lighting in a large, one-time capital investment so budgeting would be predictable. And, you’d have the peace of mind knowing that no matter the circumstance, each of your house guests would have light when they need it, ensuring lifestyle continuity and house guest satisfaction. 

Of course, the reason why we don’t use this capacity planning model is that home lighting is provided in an on-demand, pay-as-you-use service model.  

Sound familiar?

Elastic resources is an old concept  

Lighting is very similar to cloud compute resources – it’s elastic. The reason is obvious – the demand on lighting fluctuates to extremes and in condensed timeframes. Consider the following demand drivers: 

  • Time of day

  • Occupancy of the rooms

  • Need for lighting, e.g., sleeping or reading

  • Time of year (Christmas lights vs. daylight savings)

Fortunately for us, our homes come with power switches so that we can regulate our lighting consumption and manage the utility-based cost of electricity. In essence, we do our best to optimize our electricity bills by using lights only when needed and using energy efficient light bulbs to further minimize the cost. Also, if you’ve owned your home for a few years, you instinctively understand when your budget needs to increase depending on the situation, e.g., higher electricity bills during certain times of the year. After a while, you really aren’t surprised by the electricity bill as it becomes very predictable. 

Transforming Capacity Planning to Elasticity Planning 

So, when it comes to home lighting, you’ve instinctively used an ‘elasticity planning’ model in lieu of a capacity planning model. 

Pretty cool, huh?  

Interesting side note on elasticity planning in action… 

My sister’s family just came back from a week long vacation and their home power bill was 25% less than normal due to lower power consumption. 

Back to blog… 

If only optimizing the cost of cloud compute resources was that easy. Hmmm, well maybe it is that easy. After all, it seems like doing our best to minimize the cost of cloud would be paramount since lower costs is one of the cloud’s promises: 

  • How can we optimize the resources that are already in use?

  • How can we optimize the amount of resources depending on the fluctuating demand?

  • How can we make the variable pricing model of the cloud predictable? 

… and The BIG question is… 

  • How can we change from traditional on-premise capacity planning to cloud-based elasticity planning? 

The irony of the cloud 

What makes elasticity planning even more important to cloud is that elastically expanding more cloud compute resources doesn’t necessarily result in meeting more business demand. For example, if your application in the cloud is slow due to inefficient methods, expanding compute resources will not allow you to meet greater business demand. 

These types of performance problems will impact both low and peak usage.  The cloud creates what I refer to as a ‘business value trap’ – it beckons you with promises of lower cost, but may actually result in higher costs… oh the irony.

Making the cloud deliver on its cost promise 

The first step in elasticity planning is to tune your application, thereby optimizing the required compute resources. This is equivalent to using an energy efficient light bulb – higher efficiency leads to less electricity, which results in lower costs. 

Tuning the application means that method call chains and SQL statements are efficient and optimized. It also means that there are no memory leaks, so that all required CPU and memory resources are minimized to support maximum business demand. 

Once you’ve tuned the application in the cloud, you need to right-size the application’s compute resource footprint. In essence, you need to know the optimal compute resource footprints to support fluctuating business demands. 

Keeping a huge compute resource footprint deployed in the cloud to service low business demand makes about as much sense as keeping all of your lights on in your house during day time. 

So, if you benchmark properly through performance testing, you’ll know the various compute resource footprints needed to support low usage (off-season), medium usage (mid-season) and peak usage (holiday season). This results in two valuable outcomes – one, you’ll validate your application’s global class scale; and two, you’ll make your variable costs in the cloud extremely predictable. 

HP Cloud Assure for cost control 

Performing true elasticity planning in the cloud requires the proper toolset and expertise. HP Cloud Assure for cost control is a service provided by HP SaaS and is meant to help you with your elasticity planning transformation. Its intention is to assure you the right size of your cloud compute footprint, at the right cost.  

I actually had a great conversation with Dana Gardner, Software Productivity Analyst and Social Media Producer, on Cloud Assure for cost control. You may read a transcript or listen to the podcast. 

Avoid the business-value-trap of the cloud. Perform proper elasticity planning! 

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