@CloudExpo Authors: Ram Sonagara, Pat Romanski, Liz McMillan, Stackify Blog, Elizabeth White

Related Topics: @CloudExpo

@CloudExpo: Blog Post

The Economics of Big Data: Why Faster Software is Cheaper

Faster means better and cheaper - lower latency and lower cost!

In big data computing, and more generally in all commercial highly parallel software systems, speed matters more than just about anything else. The reason is straightforward, and has been known for decades.

Put very simply, when it comes to massively parallel software of the kind need to handle big data, fast is both better AND cheaper. Faster means lower latency AND lower cost.

At first this may seem counterintuitive. A high-end sports car will be much faster than a standard family sedan, but the family sedan may be much cheaper. Cheaper to buy, and cheaper to run. But massively parallel software running on commodity hardware is a quite different type of product from a car. In general, the faster it goes, the cheaper it is to run.

Time Is Money
As has been noted many times in the history of computing, if you are a factor of 50x slower, then you will need 50x more nodes to run at the same speed (even assuming perfect parallelization), or your computation will need 50x more time. In either case, it will also be much more likely that you will experience at least one of your nodes crashing during a computation. This is not to argue that automatic fault tolerance and recovery should be ignored in the pursuit of speed, but rather that these two factors need to be carefully balanced. Good design in massively parallel systems is about achieving maximum speed along with the ability to recover from a given expected level of hardware failure, via checkpointing.

The key phrase here is "a given expected level of hardware failure". In certain types of peer-to-peer services which take advantage of idle PC capacity, it is necessary to assume that all machines are extremely unreliable and may go offline at any time. However, in a commercial big data cluster it may be reasonably asssumed that almost all machines will be available almost all of the time. This means that a much more optimistic point in the design space can be chosen, one which is designed much more for speed than for pathological failure scenarios.

The MapReduce model is an example of a model where speed has been sacrificed in a major way in order to achieve scalability on very unreliable hardware. As we have noted, while this is acceptable in certain types of free peer-to-peer services, it is much less acceptable in commercial big data systems deployed at scale.

Google, the inventors of the model, were the first to recognize the throughput and latency problems with the MapReduce model. To get the realtime performance they required, they recently replaced MapReduce in their Google Instant search engine.

The MapReduce model of Apache Hadoop is slow. In fact, it's very slow compared to, for example, the kinds of MPI or BSP clusters that have been routinely used in supercomputing for more than 15 years. On exactly the same hardware, MapReduce can be several orders of magnitude slower than MPI or BSP. By using MPI rather than MapReduce, HadoopBI gives customers the best possible big data solution, not only in terms of performance - massive throughput and extremely low latency - but also in terms of economics. HadoopBI is not just the fastest Big Data BI solution, it is also the cheapest at scale.

It's Free, But Is It Fast Enough?
Another frequently misunderstood element of big data economics concerns so-called "free" software. It has been argued by some that, since big data software needs to be run on many nodes, it is really important to have software that is free. Again this is an extreme oversimplification that ignores the dominant cost issues in big data economics. At large scale, software costs will in general be much smaller than hardware or cloud costs. And commercial software vendors should ensure that they are, if they want to stay in business.

Consider the following small-scale example. A company needs to process big data continuously in order to maximize competitive advantage. For simplicity, we will assume that the cost of running a single server (in-house or cloud) for one hour is $1, and that the company has a choice between two big data software systems - system A costs $1,000 per server and system B is free, but system A is 8x faster. Choosing system A, the company requires 5 servers, working continuously, to achieve the throughput required. However, if the company chooses system B, it will require 40 servers running continuously.

Simple arithmetic shows that within just six days, the initial cost of system A has been recovered, and from then on system A gives the company massive cost savings. Even if system A is only 2x or 3x faster and more efficient than system B, the initial cost will still be recovered in a matter of a few weeks.

The economic advantages of speed at scale are magnified even more in large-scale big data systems where, with volume licensing discounts, the payback time for super-fast software is even shorter.

The lesson of the above example is simple and very important. In parallel systems, speed at scale is king, as speed equates to efficiency, and efficiency equates to massive cost savings at scale. So, to be relevant for large scale production deployments, free parallel software has to be at least as fast and efficient as the best commercial software, otherwise the economics will be solidly against it. Some examples of free software, such as the Linux operating system, have achieved this goal. It remains to be seen whether this will also be the case with highly parallel big data software. In the meantime, it's important to remember that "free software is cheap, but fast software can be even cheaper".

More Stories By Bill McColl

Bill McColl left Oxford University to found Cloudscale. At Oxford he was Professor of Computer Science, Head of the Parallel Computing Research Center, and Chairman of the Computer Science Faculty. Along with Les Valiant of Harvard, he developed the BSP approach to parallel programming. He has led research, product, and business teams, in a number of areas: massively parallel algorithms and architectures, parallel programming languages and tools, datacenter virtualization, realtime stream processing, big data analytics, and cloud computing. He lives in Palo Alto, CA.

Comments (0)

Share your thoughts on this story.

Add your comment
You must be signed in to add a comment. Sign-in | Register

In accordance with our Comment Policy, we encourage comments that are on topic, relevant and to-the-point. We will remove comments that include profanity, personal attacks, racial slurs, threats of violence, or other inappropriate material that violates our Terms and Conditions, and will block users who make repeated violations. We ask all readers to expect diversity of opinion and to treat one another with dignity and respect.

@CloudExpo Stories
The IoT Will Grow: In what might be the most obvious prediction of the decade, the IoT will continue to expand next year, with more and more devices coming online every single day. What isn’t so obvious about this prediction: where that growth will occur. The retail, healthcare, and industrial/supply chain industries will likely see the greatest growth. Forrester Research has predicted the IoT will become “the backbone” of customer value as it continues to grow. It is no surprise that retail is ...
Digital Transformation: Preparing Cloud & IoT Security for the Age of Artificial Intelligence. As automation and artificial intelligence (AI) power solution development and delivery, many businesses need to build backend cloud capabilities. Well-poised organizations, marketing smart devices with AI and BlockChain capabilities prepare to refine compliance and regulatory capabilities in 2018. Volumes of health, financial, technical and privacy data, along with tightening compliance requirements by...
Business professionals no longer wonder if they'll migrate to the cloud; it's now a matter of when. The cloud environment has proved to be a major force in transitioning to an agile business model that enables quick decisions and fast implementation that solidify customer relationships. And when the cloud is combined with the power of cognitive computing, it drives innovation and transformation that achieves astounding competitive advantage.
Evan Kirstel is an internationally recognized thought leader and social media influencer in IoT (#1 in 2017), Cloud, Data Security (2016), Health Tech (#9 in 2017), Digital Health (#6 in 2016), B2B Marketing (#5 in 2015), AI, Smart Home, Digital (2017), IIoT (#1 in 2017) and Telecom/Wireless/5G. His connections are a "Who's Who" in these technologies, He is in the top 10 most mentioned/re-tweeted by CMOs and CIOs (2016) and have been recently named 5th most influential B2B marketeer in the US. H...
How is DevOps going within your organization? If you need some help measuring just how well it is going, we have prepared a list of some key DevOps metrics to track. These metrics can help you understand how your team is doing over time. The word DevOps means different things to different people. Some say it a culture and every vendor in the industry claims that their tools help with DevOps. Depending on how you define DevOps, some of these metrics may matter more or less to you and your team.
DevOpsSummit New York 2018, colocated with CloudEXPO | DXWorldEXPO New York 2018 will be held November 11-13, 2018, in New York City. Digital Transformation (DX) is a major focus with the introduction of DXWorldEXPO within the program. Successful transformation requires a laser focus on being data-driven and on using all the tools available that enable transformation if they plan to survive over the long term. A total of 88% of Fortune 500 companies from a generation ago are now out of bus...
DXWordEXPO New York 2018, colocated with CloudEXPO New York 2018 will be held November 11-13, 2018, in New York City and will bring together Cloud Computing, FinTech and Blockchain, Digital Transformation, Big Data, Internet of Things, DevOps, AI, Machine Learning and WebRTC to one location.
Dion Hinchcliffe is an internationally recognized digital expert, bestselling book author, frequent keynote speaker, analyst, futurist, and transformation expert based in Washington, DC. He is currently Chief Strategy Officer at the industry-leading digital strategy and online community solutions firm, 7Summits.
@DevOpsSummit New York 2018, colocated with CloudEXPO | DXWorldEXPO New York 2018 will be held November 11-13, 2018, in New York City. From showcase success stories from early adopters and web-scale businesses, DevOps is expanding to organizations of all sizes, including the world's largest enterprises - and delivering real results.
DXWorldEXPO LLC announced today that Dez Blanchfield joined the faculty of CloudEXPO's "10-Year Anniversary Event" which will take place on November 11-13, 2018 in New York City. Dez is a strategic leader in business and digital transformation with 25 years of experience in the IT and telecommunications industries developing strategies and implementing business initiatives. He has a breadth of expertise spanning technologies such as cloud computing, big data and analytics, cognitive computing, m...
DXWorldEXPO LLC announced today that Kevin Jackson joined the faculty of CloudEXPO's "10-Year Anniversary Event" which will take place on November 11-13, 2018 in New York City. Kevin L. Jackson is a globally recognized cloud computing expert and Founder/Author of the award winning "Cloud Musings" blog. Mr. Jackson has also been recognized as a "Top 100 Cybersecurity Influencer and Brand" by Onalytica (2015), a Huffington Post "Top 100 Cloud Computing Experts on Twitter" (2013) and a "Top 50 C...
Cloud-enabled transformation has evolved from cost saving measure to business innovation strategy -- one that combines the cloud with cognitive capabilities to drive market disruption. Learn how you can achieve the insight and agility you need to gain a competitive advantage. Industry-acclaimed CTO and cloud expert, Shankar Kalyana presents. Only the most exceptional IBMers are appointed with the rare distinction of IBM Fellow, the highest technical honor in the company. Shankar has also receive...
Enterprises have taken advantage of IoT to achieve important revenue and cost advantages. What is less apparent is how incumbent enterprises operating at scale have, following success with IoT, built analytic, operations management and software development capabilities - ranging from autonomous vehicles to manageable robotics installations. They have embraced these capabilities as if they were Silicon Valley startups.
Poor data quality and analytics drive down business value. In fact, Gartner estimated that the average financial impact of poor data quality on organizations is $9.7 million per year. But bad data is much more than a cost center. By eroding trust in information, analytics and the business decisions based on these, it is a serious impediment to digital transformation.
Daniel Jones is CTO of EngineerBetter, helping enterprises deliver value faster. Previously he was an IT consultant, indie video games developer, head of web development in the finance sector, and an award-winning martial artist. Continuous Delivery makes it possible to exploit findings of cognitive psychology and neuroscience to increase the productivity and happiness of our teams.
Predicting the future has never been more challenging - not because of the lack of data but because of the flood of ungoverned and risk laden information. Microsoft states that 2.5 exabytes of data are created every day. Expectations and reliance on data are being pushed to the limits, as demands around hybrid options continue to grow.
The standardization of container runtimes and images has sparked the creation of an almost overwhelming number of new open source projects that build on and otherwise work with these specifications. Of course, there's Kubernetes, which orchestrates and manages collections of containers. It was one of the first and best-known examples of projects that make containers truly useful for production use. However, more recently, the container ecosystem has truly exploded. A service mesh like Istio addr...
Cloud Expo | DXWorld Expo have announced the conference tracks for Cloud Expo 2018. Cloud Expo will be held June 5-7, 2018, at the Javits Center in New York City, and November 6-8, 2018, at the Santa Clara Convention Center, Santa Clara, CA. Digital Transformation (DX) is a major focus with the introduction of DX Expo within the program. Successful transformation requires a laser focus on being data-driven and on using all the tools available that enable transformation if they plan to survive ov...
DXWorldEXPO | CloudEXPO are the world's most influential, independent events where Cloud Computing was coined and where technology buyers and vendors meet to experience and discuss the big picture of Digital Transformation and all of the strategies, tactics, and tools they need to realize their goals. Sponsors of DXWorldEXPO | CloudEXPO benefit from unmatched branding, profile building and lead generation opportunities.
The dynamic nature of the cloud means that change is a constant when it comes to modern cloud-based infrastructure. Delivering modern applications to end users, therefore, is a constantly shifting challenge. Delivery automation helps IT Ops teams ensure that apps are providing an optimal end user experience over hybrid-cloud and multi-cloud environments, no matter what the current state of the infrastructure is. To employ a delivery automation strategy that reflects your business rules, making r...