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              Showing posts with label data centers. Show all posts
              Showing posts with label data centers. Show all posts

              Friday, September 28, 2012

              A Lean Greentech Approach



              I am a greentech enthusiast and I have been closely following the greentech VC investment landscape. The VCs like Kleiner Perkins who have had a large greentech portfolio including companies such as Bloom Energy are scaling down on greentech investment. Their current investment is not likely to get any returns close to what a VC would expect. The fundamental challenge with such greentech (excluding software) investment is that they are open ended capital-intensive; you just don't know home much time it would take to build the technology/product, how much it would cost, and how much you would be able to sell it for. The market fluctuations make things even worse. This is not only true in the case of start-ups but also true for the large companies; Applied Materials' grand plan to revolutionize thin-film solar business ended up in a bust.  

              There's a different way to approach this monumental challenge.

              Just look at how open source has evolved. It started out as non-commercial academia projects where a few individuals challenged the way the existing systems behaved and created new systems. These open source projects found corporate sponsors who embraced them and helped them find a permanent home. This also resulted in a vibrant ecosystem around it to extend those projects. A few entrepreneurs looked at these open source projects and built companies to commercialize them with the help of VC funding. Time after time, this business model has worked. Technologists are great at building technology, companies are great at throwing money at people, entrepreneurs are great at extending and combining existing technology to create new products, and VCs are great at funding those companies to help entrepreneurs build businesses. What VCs are not good at is doling out very large sum of money to bet on technology that doesn't yet exist.

              If we need to make it work, we need a three-way relationship. People in academia should work on capital-intensive greentech technology projects that are funded by corporations through traditional grants. These projects should become available in public domain with an open source like license or even a commercial license. The entrepreneurs can license these technology, open source or not, and raise venture money to build a profitable business. The companies that are constantly contributing their greentech initiatives to public domain should continue to do so. and Google continues to share their green data center design.

              The important aspect is to differentiate technology from a product. The VCs are not that good at investing into (non-software) technology but are certainly good at investing into products. For many greentech companies, technology is a key piece such as a battery, a specific kind of a solar film, a fuel cell etc. Commercializing this technology is a completely different story. This requires setting up key partnerships such as and Israeli government committing to a nationwide all-electric car infrastructure with Better Place.

              Many large companies have set up their incubators or "labs" to find something that is fundamentally disruptive that could help their business. Later, there have been a very few success stories of these incubators or labs because the start-up world is way more efficient to do what big companies want to do. These labs are also torn between technology and products. My suggestion to them would be to go back to what they were good at - hiring great scientists from academia and working with academia on the next-generation technology to create a business model by either using that technology in your products or to license it to others who want to build business. This shifts the investment from a few VCs to a relatively large number of corporations.

              What we really need is a lean greentech approach.

              Photo Courtesy: Kah Wai Lin

              Monday, November 7, 2011

              Early Signs Of Big Data Going Mainstream


              Today, Cloudera announced a new $40m funding round to scale their sales and marketing efforts and a partnership with NetApp where NetApp will resell Cloudera's Hadoop as part of their solution portfolio. These both announcements are critical to where the cloud and Big Data are headed.

              Big Data going mainstream: Hadoop and MapReduce are not only meant for Google, Yahoo, and fancy Silicon Valley start-ups. People have recognized that there's a wider market for Hadoop for consumer as well as enterprise software applications. As I have argued before Hadoop and Cloud is a match made in heaven. I blogged about Cloudera and the rising demand of data-centric massive parallel processing almost 2.5 years back, Obviously, we have come a long way. The latest Hadoop conference is completely sold out. It's good to see the early signs of Hadoop going mainstream. I am expecting to see similar success for companies such as Datastax (previously Riptano) which is a "Cloudera for Cassandra."

              Storage is a mega-growth category: We are barely scratching the surface when it comes to the growth in the storage category. Big data combined with the cloud growth is going to drive storage demand through the roof and the established storage vendors are in the best shape to take advantage of this opportunity. I wrote a cloud research report and predictions this year with a luminary analyst Ray Wang where I mentioned that cloud storage will be a hot cake and NoSQL will skyrocket. It's true this year and it's even more true next year.

              Making PaaS even more exciting: PaaS is the future and Hadoop and Cassandra are not easy to deploy and program. Availability of such frameworks at lower layers makes PaaS even more exciting. I don't expect the PaaS developers to solve these problems. I expect them to work on providing a layer that exposes the underlying functionality in a declarative as well as a programmatic way to let application developers pick their choice of PaaS platform and build killer applications.

              Push to the private cloud: Like it or not, availability of Hadoop from an "enterprise" vendor is going to help the private cloud vendors. NetApp has a fairly large customer base and their products are omnipresent in large private data centers. I know many companies that are interested in exploring Hadoop for a variety of their needs but are somewhat hesitant to go out to a public cloud since it requires them to move their large volume of on-premise data to the cloud. They're more likely to use a solution that comes to their data as opposed to moving their data to where a solution resides.

              Thursday, October 16, 2008

              Greening The Data Centers

              Recently Google published the Power Usage Efficiency (PUE) numbers of their data centers. PUE is defined as a ratio of the total power consumed by a data center to the power consumed by the IT equipments of the facility. Google's data centers' PUE ranges from 1.1 to 1.3 which is quite impressive. Though it is unclear why all the data centers have slightly different PUE. Are they designed differently or are they all not tuned to improve for the energy efficiency? In any case I am glad to see that Google is committed to the Green Grid initiative and is making the measurement data and method publicly available. This should encourage other organizations to improve the energy performance of their data centers.

              The energy efficiency of a data center can be classified into three main categories:

              1. Efficiency of the facility: The PUE is designed to measure this kind of efficiency that is based on how a facility that hosts a data center is designed such as its physical location, layout, sizing, cooling systems etc. Some organizations have gotten quite creative to improve this kind of efficiency by setting up an underground data center to achieve consistent temperature or setting up data centers near a power generation facility or even setting up their own captive power plant to reduce the distribution loss from the grid and meet the peak load demand.

              2. Efficiency of the servers: This efficiency is based on the efficiency of the hardware components of the servers such as CPU, cooling fans, drive motors etc. has made significant progress in this area to provide energy-efficient solutions. Sun has backed up the organization OpenEco that helps participants assess, track, and compare energy performance. Sun has also published their carbon footprint.

              3. Efficiency of the software architecture: To achieve this kind of efficiency the software architecture is optimized to consume less energy to provide the same functionality. The optimization techniques have by far focused on the performance, storage, and manageability ignoring the software architecture tuning that brings in energy efficiency.

              Round Robbin is a popular load balancing algorithm to optimize the load on servers but this algorithm is proven to be energy in-efficient. Another example is about the compression. If data is compressed on a disk it would require CPU cycles to uncompress it versus requiring more I/O calls if it is stored uncompressed. Given everything else being the same, which approach would require less power? These are not trivial questions.

              I do not favor an approach where the majority of the programmers are required to change their behavior and learn new way of writing code. One of the ways to optimize the energy performance of the software architecture is to adopt an 80/20 rule. The 80% of the applications use 20% of the code and in most of the cases it is an infrastructure or middleware code. It is relatively easy to educate and train these small subset of the programmers to optimize the code and the architecture for energy-efficiency. Virtualization could also help a lot in this area since the execution layers can be abstracted into something that can be rapidly changed and tuned without affecting the underlying code to provide consistent functionality and behavior.

              The energy efficiency cannot be achieved by tuning things in separation. It requires a holistic approach. PUE ratios identify the energy loss prior to it reaches a server, the energy-efficient server requires less power to execute the same software compared to other servers, and the energy-efficient software architecture actually lowers the consumption of energy for the same functionality that the software is providing. We need to invest into all the three categories.

              Power consumption is just one aspect of being green. There are many other factors such as how a data center handles the e-waste, the building material used, the green house gases out of the captive power plant (if any) and the cooling plants etc. However tackling energy efficiency is a great first step in greening the data centers.
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