|By Bill McColl||
|January 4, 2012 05:15 AM EST||
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".
The buzz continues for cloud, data analytics and the Internet of Things (IoT) and their collective impact across all industries. But a new conversation is emerging - how do companies use industry disruption and technology enablers to lead in markets undergoing change, uncertainty and ambiguity? Organizations of all sizes need to evolve and transform, often under massive pressure, as industry lines blur and merge and traditional business models are assaulted and turned upside down. In this new data-driven world, marketplaces reign supreme while interoperability, APIs and applications deliver un...
Oct. 9, 2015 04:00 PM EDT Reads: 301
The Internet of Things (IoT) is growing rapidly by extending current technologies, products and networks. By 2020, Cisco estimates there will be 50 billion connected devices. Gartner has forecast revenues of over $300 billion, just to IoT suppliers. Now is the time to figure out how you’ll make money – not just create innovative products. With hundreds of new products and companies jumping into the IoT fray every month, there’s no shortage of innovation. Despite this, McKinsey/VisionMobile data shows "less than 10 percent of IoT developers are making enough to support a reasonably sized team....
Oct. 9, 2015 04:00 PM EDT Reads: 232
Electric power utilities face relentless pressure on their financial performance, and reducing distribution grid losses is one of the last untapped opportunities to meet their business goals. Combining IoT-enabled sensors and cloud-based data analytics, utilities now are able to find, quantify and reduce losses faster – and with a smaller IT footprint. Solutions exist using Internet-enabled sensors deployed temporarily at strategic locations within the distribution grid to measure actual line loads.
Oct. 9, 2015 03:49 PM EDT
You have your devices and your data, but what about the rest of your Internet of Things story? Two popular classes of technologies that nicely handle the Big Data analytics for Internet of Things are Apache Hadoop and NoSQL. Hadoop is designed for parallelizing analytical work across many servers and is ideal for the massive data volumes you create with IoT devices. NoSQL databases such as Apache HBase are ideal for storing and retrieving IoT data as “time series data.”
Oct. 9, 2015 03:45 PM EDT Reads: 503
Too often with compelling new technologies market participants become overly enamored with that attractiveness of the technology and neglect underlying business drivers. This tendency, what some call the “newest shiny object syndrome,” is understandable given that virtually all of us are heavily engaged in technology. But it is also mistaken. Without concrete business cases driving its deployment, IoT, like many other technologies before it, will fade into obscurity.
Oct. 9, 2015 03:45 PM EDT Reads: 137
Today air travel is a minefield of delays, hassles and customer disappointment. Airlines struggle to revitalize the experience. GE and M2Mi will demonstrate practical examples of how IoT solutions are helping airlines bring back personalization, reduce trip time and improve reliability. In their session at @ThingsExpo, Shyam Varan Nath, Principal Architect with GE, and Dr. Sarah Cooper, M2Mi's VP Business Development and Engineering, will explore the IoT cloud-based platform technologies driving this change including privacy controls, data transparency and integration of real time context w...
Oct. 9, 2015 03:30 PM EDT Reads: 104
The Internet of Everything is re-shaping technology trends–moving away from “request/response” architecture to an “always-on” Streaming Web where data is in constant motion and secure, reliable communication is an absolute necessity. As more and more THINGS go online, the challenges that developers will need to address will only increase exponentially. In his session at @ThingsExpo, Todd Greene, Founder & CEO of PubNub, will explore the current state of IoT connectivity and review key trends and technology requirements that will drive the Internet of Things from hype to reality.
Oct. 9, 2015 03:05 PM EDT
The IoT market is on track to hit $7.1 trillion in 2020. The reality is that only a handful of companies are ready for this massive demand. There are a lot of barriers, paint points, traps, and hidden roadblocks. How can we deal with these issues and challenges? The paradigm has changed. Old-style ad-hoc trial-and-error ways will certainly lead you to the dead end. What is mandatory is an overarching and adaptive approach to effectively handle the rapid changes and exponential growth.
Oct. 9, 2015 03:00 PM EDT Reads: 200
Today’s connected world is moving from devices towards things, what this means is that by using increasingly low cost sensors embedded in devices we can create many new use cases. These span across use cases in cities, vehicles, home, offices, factories, retail environments, worksites, health, logistics, and health. These use cases rely on ubiquitous connectivity and generate massive amounts of data at scale. These technologies enable new business opportunities, ways to optimize and automate, along with new ways to engage with users.
Oct. 9, 2015 02:00 PM EDT Reads: 187
The IoT is upon us, but today’s databases, built on 30-year-old math, require multiple platforms to create a single solution. Data demands of the IoT require Big Data systems that can handle ingest, transactions and analytics concurrently adapting to varied situations as they occur, with speed at scale. In his session at @ThingsExpo, Chad Jones, chief strategy officer at Deep Information Sciences, will look differently at IoT data so enterprises can fully leverage their IoT potential. He’ll share tips on how to speed up business initiatives, harness Big Data and remain one step ahead by apply...
Oct. 9, 2015 01:45 PM EDT Reads: 561
There will be 20 billion IoT devices connected to the Internet soon. What if we could control these devices with our voice, mind, or gestures? What if we could teach these devices how to talk to each other? What if these devices could learn how to interact with us (and each other) to make our lives better? What if Jarvis was real? How can I gain these super powers? In his session at 17th Cloud Expo, Chris Matthieu, co-founder and CTO of Octoblu, will show you!
Oct. 9, 2015 01:15 PM EDT
As a company adopts a DevOps approach to software development, what are key things that both the Dev and Ops side of the business must keep in mind to ensure effective continuous delivery? In his session at DevOps Summit, Mark Hydar, Head of DevOps, Ericsson TV Platforms, will share best practices and provide helpful tips for Ops teams to adopt an open line of communication with the development side of the house to ensure success between the two sides.
Oct. 9, 2015 01:00 PM EDT Reads: 602
SYS-CON Events announced today that ProfitBricks, the provider of painless cloud infrastructure, will exhibit at SYS-CON's 17th International Cloud Expo®, which will take place on November 3–5, 2015, at the Santa Clara Convention Center in Santa Clara, CA. ProfitBricks is the IaaS provider that offers a painless cloud experience for all IT users, with no learning curve. ProfitBricks boasts flexible cloud servers and networking, an integrated Data Center Designer tool for visual control over the cloud and the best price/performance value available. ProfitBricks was named one of the coolest Clo...
Oct. 9, 2015 01:00 PM EDT Reads: 794
SYS-CON Events announced today that IBM Cloud Data Services has been named “Bronze Sponsor” of SYS-CON's 17th Cloud Expo, which will take place on November 3–5, 2015, at the Santa Clara Convention Center in Santa Clara, CA. IBM Cloud Data Services offers a portfolio of integrated, best-of-breed cloud data services for developers focused on mobile computing and analytics use cases.
Oct. 9, 2015 12:00 PM EDT Reads: 738
SYS-CON Events announced today that Sandy Carter, IBM General Manager Cloud Ecosystem and Developers, and a Social Business Evangelist, will keynote at the 17th International Cloud Expo®, which will take place on November 3–5, 2015, at the Santa Clara Convention Center in Santa Clara, CA.
Oct. 9, 2015 11:15 AM EDT
Developing software for the Internet of Things (IoT) comes with its own set of challenges. Security, privacy, and unified standards are a few key issues. In addition, each IoT product is comprised of at least three separate application components: the software embedded in the device, the backend big-data service, and the mobile application for the end user's controls. Each component is developed by a different team, using different technologies and practices, and deployed to a different stack/target - this makes the integration of these separate pipelines and the coordination of software upd...
Oct. 9, 2015 09:00 AM EDT Reads: 296
Mobile messaging has been a popular communication channel for more than 20 years. Finnish engineer Matti Makkonen invented the idea for SMS (Short Message Service) in 1984, making his vision a reality on December 3, 1992 by sending the first message ("Happy Christmas") from a PC to a cell phone. Since then, the technology has evolved immensely, from both a technology standpoint, and in our everyday uses for it. Originally used for person-to-person (P2P) communication, i.e., Sally sends a text message to Betty – mobile messaging now offers tremendous value to businesses for customer and empl...
Oct. 9, 2015 08:30 AM EDT Reads: 308
"Matrix is an ambitious open standard and implementation that's set up to break down the fragmentation problems that exist in IP messaging and VoIP communication," explained John Woolf, Technical Evangelist at Matrix, in this SYS-CON.tv interview at @ThingsExpo, held Nov 4–6, 2014, at the Santa Clara Convention Center in Santa Clara, CA.
Oct. 9, 2015 07:00 AM EDT Reads: 5,886
WebRTC converts the entire network into a ubiquitous communications cloud thereby connecting anytime, anywhere through any point. In his session at WebRTC Summit,, Mark Castleman, EIR at Bell Labs and Head of Future X Labs, will discuss how the transformational nature of communications is achieved through the democratizing force of WebRTC. WebRTC is doing for voice what HTML did for web content.
Oct. 9, 2015 06:00 AM EDT Reads: 1,415
Nowadays, a large number of sensors and devices are connected to the network. Leading-edge IoT technologies integrate various types of sensor data to create a new value for several business decision scenarios. The transparent cloud is a model of a new IoT emergence service platform. Many service providers store and access various types of sensor data in order to create and find out new business values by integrating such data.
Oct. 9, 2015 04:00 AM EDT Reads: 570