Cloud Computing vs. Hardware as a Service

In a previous post, I have discussed why I believed that cloud computing was going to be a big player arena, and not a friendly place for the little guys.

Recently, many people told about such and such small company that was supposed to deliver cloud computing too, and that their service would match the ones offered by big players.

Basically, the discussion goes like this:

Hey, we too are able to instantiate virtual machines on-demand. We have some nice virtual machine deployment scripts, a nice WebUI to administrate all the nodes, we are now matching the Amazon offer.

Nope, you’re not.

Basically, what those little players are doing is simply Hardware as a Service. For years now, computing hardware has been more or less an on-demand commodity. My favorite host can typically set-up a new server in 48h, and I can cancel my subscription anytime (although I will have to pay for the entire month). Some more aggressive host providers are providing fully automated server setup, and your new server is usually available in less than 1h.

Now, what those small companies calling themselves cloud providers are able setup new servers in seconds instead of minutes; and the trick to do that is simple: they use virtualization and deployment scripts.

But, in my opinion, this isn’t cloud computing, this is just hardware as a service with a lower overhead both at infrastructure level, but also for the system administrators themselves.

So, what is so radically different with cloud computing?

In my opinion, the radical novelty of cloud computing is the promise that you won’t have to worry about resource allocation anymore.

In particular, I don’t want to figure out if I need 1, 2, 3 or 42 computing nodes to handle a massive web traffic surge from Slashdot, I just want to tell my cloud provider:

Here is the script for my web page, do whatever is needed to ensure good performance, and send me the bill at the end of the month.

Note that this is exactly what Google App Engine is doing. Google App Engine is relieving web developers from the burden of having to figure out how they are going to scale their web apps. Google is doing the magic for them so that web developers can focus on the specific value of their web apps instead of focusing on the complex infrastructure actually needed to achieve scalability.

Quoting Thomas Serval from Round Table about Azure a few months ago:

In the past, each time we have multiplied the traffic by 10 on our applications, we have been forced more or less to rewrite the application from scratch. The promise of cloud computing is to let you achieve unlimited scalability from day one.

Obviously, cloud computing isn’t magic, thus applications will need to be carefully designed to achieve unlimited scalability, yet I believe that thanks to the cloud computing frameworks currently being published, it won’t be that hard in the future.

Thus cloud computing is not Hardware as a Service simply because Hardware as a Service does not do anything about scalability in itself.

The true benefits of cloud computing is to provide what I would call scalable computing abstractions. Those abstractions represent physical resources such as CPU, memory or bandwidth, but with additional constraints (usually structural constraints) so that it becomes actually possible to provide an infinitely scalable instance of the desired resource.

For example, nowadays more or less all cloud providers are including in their offer a distributed and reliable hashtable implementation: S3 for Amazon, Blob Storage for Windows Azure, … FIPFO is another popular scalable storage abstraction: First-In Probably First Out, i.e. queues but without deterministic behavior. As long as you rely only on those scalable storage abstractions, you should not care about scalability of your storage.

So far, scalable storage abstractions have been the primary focus of most cloud providers. Yet, I suspect that the next battle will be scalable CPU abstractions.

Indeed, Amazon has recently unveiled their now Amazon Elastic MapReduce, and as other people believe, I too believe that MapReduce will be a game changer. First, Amazon is delivering CPU at 0.015 USD/h while its competitors are still above 0.10 USD/h at the time. Then, if we consider that the Amazon native MapReduce implementation is going to be way more efficient than custom in-house implementations - simply because Amazon folks have the time and the experience needed to get the load balancing settings rights - then Amazon has just divided the CPU price by 10.

Then, what I see as the killing benefit of MapReduce is that I don’t have to care anymore about how many nodes I need.

For example at Lokad, we have tons of time-series to process. Let say that we want to extract seasonality patterns out of 100 millions time-series (each time time-series ranging from a few hundreds to a few thousands points). With MapReduce, I just have to specify the algorithm to process a single time-series and pass the huge time-series collection as argument. The cloud infrastructure will be handling all the magic for me. In particular, I don’t have to care anymore about node crashing along the way, or about dynamically expanding / shrinking the number of computing nodes.

MapReduce is a very constrained framework that forces you to apply the very same function everywhere, but the input collection can be arbitrarily large. In my experience, if you’re not able to scale a data-mining problem through MapReduce, then nothing will – or, more precisely, the design complexity will be so great that you are most likely to give up anyway.

Those scalable resource abstractions represent the core value offered by cloud providers. Yet, those scalable resource abstractions are truly hard to design and even harder to optimize. Yes, you might know a small company that auto-deploys virtual machines, but, in my opinion, this does not reach even 10% of the potential benefits brought by the cloud.

Those benefits will be achieved though scalable resource abstractions; and each one of those abstractions is going to cost a massive amount of brain power to get done right.


Reader Comments (2)

Regarding the prices: “Amazon Elastic MapReduce pricing is in addition to normal Amazon EC2 and Amazon S3 pricing”. April 6, 2009 | Rinat Abdullin


[…] FIFO (First In First Out) is a very well known concept in computer science. In one of my previous post, I used the word FIPFO to refer to First In Probably First Out to refer to the cloud equivalent of […] May 19, 2009 | Joannes Vermorel’s blog