Welcome!

Big Data Journal Authors: Elizabeth White, Yeshim Deniz, Carmen Gonzalez, Pat Romanski, Adrian Bridgwater

Blog Feed Post

Why Contextual Data Locality Matters

Big Data is quickly overtaking SDN as a key phrase in today’s networking lingo. And overused already as it may be, it actually has a lot more meaning and definition compared to SDN. Big Data solutions are designed to work on lots of data as the name suggests. Of course they have been around forever, talk to any large bank, credit card company, airline or logistics company and all of them have had applications running on extremely large databases and data sets forever. But this is the new Big Data, the one inspired by Hadoop, MapReduce and friends. High performance compute clusters specifically created to analyze large amounts of data and reduce it to a form and quantity that human brains can use in decision making.

What makes today’s Big Data solutions different than its more traditional large database based applications, beyond the sheer datasets being analyzed, is the distributed nature of the analysis. Big Data solutions are designed to run across 100s or even 1000s of servers, each with multiple CPU cores to chew on the data. Traditional large database applications tend to be more localized with fewer applications and servers accessing the data, allowing for more tightly custom integrated solutions, the likes of which Oracle and friends are experts at.

Big Data Flashback

In the late 80s I started my career working as a network engineer for a high energy physics research institute. Working closely with the folks at CERN in Geneva, these physicists were (at the time, and probably still) masters of creating very large datasets. Every time an experiment was run, Tbytes of data (probably Pbytes by now) were generated by thousands of sensors along the tunnel or ring particles were passed through to collide.

The Big Data solution at the time was primitive, but not all that much different than today. The large datasets were manually broken into manageable pieces, something that would fit on a tape or disk. These datasets were then hand copied onto a compute server or super computer and the analysis application would churn through it to find specific data, correlate events and simply reduce the data to something smaller and meaningful. This would then create a new dataset, which would be combined, chopped up again, and the process repeated itself until they arrived at data that was consumable for humans to create new theories from, or provide a piece of proof of an existing theory.

During that first job, the IT group spend an enormous amount of time moving data around. A lot of it manual: tapes and disks were constantly being copied onto the appropriate compute server. The data had to be local to have any chance of analyzing the data. Between tapes, local disks and the network, the local disks were the only storage with appropriate speed to have a hope of finalizing the data reductions. And even then it would not be unusual to have a rather powerful (for the time) Apollo workstation run for several weeks on a single data set.

Back to the here and now

Forward the clock to now. The above description is really not that different from how Hadoop MapReduce works. Start with a big data set, chop it into pieces, replicate the data, compute on the data close to physical locality of the data. Then send results to Reducers, combine the results, then perhaps repeat again to get to human interpretable results.

As fast as we believe the network is within 10GbE access ports, it is still commonly the most restrictive component in the compute, distributed storage and network trio. Compute power increments have far outpaced network speed increments and even memory speed increments. We have many more cycles available to compute, but have not been able to get the data into these CPUs with the same increments. As a result, storage solutions are becoming increasingly distributed, closer to the compute power that needs it.

It’s a natural thought to have the data close to where it needs to be processed, close enough that the effort of retrieving it does not impact the overall completion of the task that uses that data. If I am writing a research paper that takes several hours to complete, I do not mind having to wait a second here or there for the right web sites to load. I would mind if I had to get into my car and drive to the library to look something up, drive back home to work on my paper, and keep doing that. The relationship between time and effort to get data has to become negligible compared to the time and effort required to complete the task.

Locality and growth

This type of contextual locality is extremely hard to manage in a dynamic and growing environment. How do you make sure that the right data remains contextually close to where it is needed when servers and VMs may not be physically close? They may not be in the same rack for the same application or customer, they may not even be in the same pod or datacenter. Storage is relatively cheap, but replication for closeness can very quickly lead to a data distribution complexity that is unmanageable in environments where its not a single orchestrated big data solution.

To solve this problem you need help from your network. You need to be able to create locality on the fly. Things that are not physically close need to be made virtually close, but with the characteristics of physical locality. And in network terms these are of course measured in the usual staples of latency and bandwidth. This is when you want to articulate relationships between the data and the applications that need that data and create virtual closeness that resembles the physical. This may mean dedicated paths through multiple switches to avoid congestion that will dramatically impact latency. These same paths can provide direct physical connectivity through dynamically engineered optical paths between application and storage, or simply appropriate prioritization of traffic along these paths. Without having to worry explicitly where the application is or where the storage is.

Physics will always stand in the way of what we really want or need, but that does not mean we use that same physics with a bit of math to create solutions that manage the complexity of creating dynamic locality. Locality is important. More pronounced in Big Data solutions, but even at a smaller scale it is important within the context of the compute effort on that data.

[Today's fun fact: Lake Superior is the world's largest lake. With that kind of naming accuracy we would like to hire the person that named the lake as our VP of Naming and Terminology]

The post Why Contextual Data Locality Matters appeared first on Plexxi.

Read the original blog entry...

More Stories By Michael Bushong

The best marketing efforts leverage deep technology understanding with a highly-approachable means of communicating. Plexxi's Vice President of Marketing Michael Bushong has acquired these skills having spent 12 years at Juniper Networks where he led product management, product strategy and product marketing organizations for Juniper's flagship operating system, Junos. Michael spent the last several years at Juniper leading their SDN efforts across both service provider and enterprise markets. Prior to Juniper, Michael spent time at database supplier Sybase, and ASIC design tool companies Synopsis and Magma Design Automation. Michael's undergraduate work at the University of California Berkeley in advanced fluid mechanics and heat transfer lend new meaning to the marketing phrase "This isn't rocket science."

Latest Stories from Big Data Journal
Software is eating the world. Companies that were not previously in the technology space now find themselves competing with Google and Amazon on speed of innovation. As the innovation cycle accelerates, companies must embrace rapid and constant change to both applications and their infrastructure, and find a way to deliver speed and agility of development without sacrificing reliability or efficiency of operations. In her keynote DevOps Summit, Victoria Livschitz, CEO of Qubell, will discuss ho...
Enthusiasm for the Internet of Things has reached an all-time high. In 2013 alone, venture capitalists spent more than $1 billion dollars investing in the IoT space. With “smart” appliances and devices, IoT covers wearable smart devices, cloud services to hardware companies. Nest, a Google company, detects temperatures inside homes and automatically adjusts it by tracking its user’s habit. These technologies are quickly developing and with it come challenges such as bridging infrastructure gaps,...
Predicted by Gartner to add $1.9 trillion to the global economy by 2020, the Internet of Everything (IoE) is based on the idea that devices, systems and services will connect in simple, transparent ways, enabling seamless interactions among devices across brands and sectors. As this vision unfolds, it is clear that no single company can accomplish the level of interoperability required to support the horizontal aspects of the IoE. The AllSeen Alliance, announced in December 2013, was formed wi...
Goodness there is a lot of talk about cloud computing. This ‘talk and chatter’ is part of the problem, i.e., we look at it, we prod it and we might even test it out – but do we get down to practical implementation, deployment and (if you happen to be a fan of the term) actual cloud ‘rollout’ today? Cloud offers the promise of a new era they say – and a new style of IT at that. But this again is the problem and we know that cloud can only deliver on the promises it makes if it is part of a well...
There’s Big Data, then there’s really Big Data from the Internet of Things. IoT is evolving to include many data possibilities like new types of event, log and network data. The volumes are enormous, generating tens of billions of logs per day, which raise data challenges. Early IoT deployments are relying heavily on both the cloud and managed service providers to navigate these challenges. In her session at 6th Big Data Expo®, Hannah Smalltree, Director at Treasure Data, to discuss how IoT, B...
SYS-CON Events announced today that Connected Data, the creator of Transporter, the world’s first peer-to-peer private cloud storage device, will exhibit at SYS-CON's 15th International Cloud Expo®, which will take place on November 4–6, 2014, at the Santa Clara Convention Center in Santa Clara, CA. Connected Data is the creator of Transporter, the world’s first peer-to-peer private cloud storage device. Connected Data is focused on providing elegantly designed solutions for consumers, professi...
Cisco on Wedesday announced its intent to acquire privately held Metacloud. Based in Pasadena, Calif., Metacloud deploys and operates private clouds for global organizations with a unique OpenStack-as-a-Service model that delivers and remotely operates production-ready private clouds in a customer's data center. Metacloud's OpenStack-based cloud platform will accelerate Cisco's strategy to build the world's largest global Intercloud, a network of clouds, together with key partners to address cu...
I write and study often on the subject of digital transformation - the digital transformation of industries, markets, products, business models, etc. In brief, digital transformation is about the impact that collected and analyzed data can have when used to enhance business processes and workflows. If Amazon knows your preferences for particular books and films based upon captured data, then they can apply analytics to predict related books and films that you may like. This improves sales. T...
Technology is enabling a new approach to collecting and using data. This approach, commonly referred to as the “Internet of Things” (IoT), enables businesses to use real-time data from all sorts of things including machines, devices and sensors to make better decisions, improve customer service, and lower the risk in the creation of new revenue opportunities. In his session at Internet of @ThingsExpo, Dave Wagstaff, Vice President and Chief Architect at BSQUARE Corporation, will discuss the real...
IoT is still a vague buzzword for many people. In his session at Internet of @ThingsExpo, Mike Kavis, Vice President & Principal Cloud Architect at Cloud Technology Partners, will discuss the business value of IoT that goes far beyond the general public's perception that IoT is all about wearables and home consumer services. The presentation will also discuss how IoT is perceived by investors and how venture capitalist access this space. Other topics to discuss are barriers to success, what is n...