Monday, December 8, 2008

Cloud Computing Predictions for 2009

GoGrid's Michael Sheehan just published his cloud computing predictions for 2009.

1- Clouds reduce the effect of the recession.
The basic argument being that since cloud computing is a more cost effective means to obtain IT services, cloud computing enables the IT budget to go further. But that would simply take money away from the IHVs and big consultancies, so a more careful study would need to be made to assert if this is zero-sum game or not. My thought here would be that the recession may accelerate the adoption of cloud computing so that consumers of IT spent less, but it will hurt the IHVs.

2- Broader depth of clouds
This prediction is the simple progression of a new technology that is getting adopted. More customers are coming in and all have slightly different requirements that the cloud providers will cater to. It is easier to do that with specialized solutions and thus we'll see a broadening of the features offered in clouds.

3- VC, money & long term viability
This is an interesting prediction from Michael: cloud aggregators will be funded and the other players in the stack will get squeezed. Cloud aggregators are companies like RightScale and Cassatt and there is no doubt in my mind that they will do well since leveraging cloud computes is still hard work. I personally think that the VCs are not going to play in this space because of the presence of large incumbents like IBM, Amazon, Google, HP, and Sun. Personally, I think the real innovation investments will come from the emerging markets since they have the most to gain from lower IT costs.

4- Cloud providers become M&A targets
This item reads as a prediction that the consolidation in the cloud space will accelerate in 2009. My prediction is contrarian in the sense that I think we'll see more specialized clouds show up to cater to very specific nitches and thus we'll see a market segmentation first before we'll see a consolidation. For example, most clouds are web application centric, and putting up a web server is one feature that is widely supported. However, the financial industry has a broader need than just web servers, as do product organizations like Boeing and GE. I think there is a great opportunity to build specialized clouds for those customers as it can be piggy backed on supply chain integration so players like Tibco can come in. That is a very large market with very high value: much more interesting than a little $49/month hosted web server.

5- Hybrid solutions
On-premise and cloud solutions working together. That prediction is more of a looking back but it is a sign that cloud computing is accepted and companies are actively planning how to leverage this new IT capability in their day to day operation.

6- Web 3.0
More tightly integrated Web 2.0? It clearly is all about the business or entertainment value. I really like what I am seeing in the data mining space where knowledge integration is creating opportunities for small players with deep domain experts to make a lot of money. Simply take a look at marketing intelligence: the most innovative solutions come from tiny players. I think this innovation will drive cloud computing for the next couple of years since it completely levels the playing field between SMBs and large enterprise. This make domain expertise more valuable and the SMBs are much more nimble and can now monetize that skill. Very exciting!

7- Standards and interoperability
Customers will demand it, incumbent cloud providers will fight it. I can't see Google and IBM giving up their closed systems so the world will add another ETL layer to IT operations and spring to live some more consultants.

8- Staggered growth
A simple prediction that everything cloud will expand.

9- Technology advances at the cloud molecular level
This is an item dear to my heart: cloud optimized silicon. It is clear that a processor that works well in your iPhone will not be the right silicon for the cloud. There are many problems to be solved in cloud computing that only have a silicon answer, so we are seeing fantastic opportunities here. This innovation will be attenuated by the lack of liquidity in the western world but this provides amazing opportunities for the BRIC countries to develop centers of excellence that surpass the US. And 2009 will be the key year for this possible jump since the US market will be distracted trying to stay in cash till clarity improves. As they say, fortunes are made in recessions.

10- Larger Adoption
A good prediction to end with for a cloud computing audience: business will be good in 2009.

Sunday, December 7, 2008

Comparing the Cost Continued...

The next step was to select our benchmarks and calculate their costs. We extracted two workloads that are common to many product development companies: a regression workload that arises when a team collaborates on the same development task, and a technical workload when an individual is using computer models to generate new insight/knowledge.

The regression workload can be generated by a software design team developing a new application, a financial engineering team back testing new trading strategies, or a mechanical design team designing a new combustion engine that runs on alternative fuels.

The technical workload can be a new rendering algorithm to model fur on an animated character, or a new economic model that drives critical risk parameters in a trading strategy, or an acoustic characterization of a automobile cabin.

The first workload is characterized by a collection of tests that are run to guarantee correctness of the product during development. Our test case for a typical regression run is a 1000 tests that run at an average of 15 minutes each. Each developer typically runs two such regressions per day, and for a 50 person design team this yields 100 regression runs per day. The total workload equates to roughly 1050 cpu hours per hour and would keep a 1000 processor cluster 100% occupied.

The second workload shifts the focus from capacity to capability. The computational task is a single simulation that requires 5 cpu hours to complete. The benchmark workload is the work created by a ten person research team that runs five simulations per day. Many of these algorithms can actually run in parallel and such a task could run in 30 minutes when executed in parallel on ten processors. Latency to solution is a major driver on R&D team productivity and this workload would have priority over the regression workload particularly during the work day. The total workload equates to roughly 31 cpu hours per hour because this workload runs just in the eight hour work day.

Running these two workloads on our cloud computing providers we get the following costs per day:
Regression Workload$25,075.17$18,250.25
Knowledge Discovery$265.09$230.13

The total cost of $20-25k per day makes the regression workload too expensive for outsourcing to today's cloud providers. A 1000 processor on-premise x86 cluster costs roughly $10k/day including overhead and amortization. The cost of bulk computes like the regression workload needs to go down by at least a factor of 5x before cloud computing can bring in small and medium-sized enterprises. However, the technical workload at $250/day is very attractive to move to the cloud since this workload is periodical with respect to the development cycle and it moves CapEx to OpEx to frees up capital for other purposes.

The big cost difference between Rackspace/Mosso and Amazon is the Disk I/O charge. It doesn't appear that Rackspace monetizes this cost. From the cost models, this appears to be a liability for them since the Disk I/O cost (moving the VM image and data sets to and from disk) represents roughly 20% of the total costs. Fast storage is notoriously expensive so this appears to be a weakness of Rackspace.

In a future article we will dissect these costs further.

Comparing Costs of Different Cloud Computing Providers

The past month we have been trying to quantify the cost of moving some of our workloads into the cloud. It has been a very painful experience. Each vendor insists on mixing up the pricing in such a way that direct comparisons require major mental gymnastics. On top of that, the big three, IBM Blue Cloud, HP Adaptive Infrastructure as a Service, or AIaaS (who in the marketing department came up with that one?), and Sun are so incredibly opaque that we have just given up. Furthermore, Sun started out at $1/cpu hour and that simply is not competitive. Sun has taken the site down and the home page of the site claims that they are working on something else. Out of sheer frustration, we have ditched IBM and HP as well. It appear that they are catering to their existing deep-pocket customers and we do not expect their solutions to be cost competitive for the disruptive cloud computing concept that will usher in the new economics.

Many activities at the US National Labs are directed to evaluate if it is cost effective to move to AWS or similar services. To be able to compare our results to that research we decided to map all costs into AWS compatible pricing units. This yielded the following very short list:
ProviderCPU $/cpu-hrDisk I/O $/GBInternet I/O $/GBStorage $GB-month

The reason for the short list is that there are very few providers that actually sell computes. Most of the vendors that use the label cloud provider are actually just hosting companies of standard web services. Companies like 3Tera, Bungee Labs, Appistry, and Google cast their services in terms of web application services, not generic compute services. This makes these services not applicable to the value-add computes that are common during the research and development phase of product companies.

In the next article we are going to quantify the cost of different IT workloads.