|By Dana Gardner||
|January 13, 2014 08:30 AM EST||
If, as the adage goes, you should fight fire with fire then perhaps its equally justified to fight Big Data optimization requirements with -- Big Data.
It turns out that high-performing, cost-effective Big-Data processing helps to make the best use of dynamic storage resources by taking in all the relevant storage activities data, analyzing it and then making the best real-time choices for dynamic hybrid storage optimization.
In other words, Big Data can be exploited to better manage complex data and storage. The concept, while tricky at first, is powerful and, I believe, a harbinger of what we're going to see more of, which is to bring high intelligence to bear on many more services, products and machines.
To explore how such Big Data analysis makes good on data storage efficiency, BriefingsDirect recently sat down with optimized hybrid storage provider Nimble Storage to hear their story on the use of HP Vertica as their data analysis platform of choice. Yes, it's the same Nimble that last month had a highly successful IPO. The expert is Larry Lancaster, Chief Data Scientist at Nimble Storage Inc. in San Jose, California. The discussion is moderated by me, Dana Gardner, Principal Analyst at Interarbor Solutions.
Here are some excerpts:
Gardner: How do you use big data to support your hybrid storage optimization value?
Lancaster: At a high level, Nimble Storage recognized early, near the inception of the product, that if we were able to collect enough operational data about how our products are performing in the field, get it back home and analyze it, we'd be able to dramatically reduce support costs. Also, we can create a feedback loop that allows engineering to improve the product very quickly, according to the demands that are being placed on the product in the field.
Looking at it from that perspective, to get it right, you need to do it from the inception of the product. If you take a look at how much data we get back for every array we sell in the field, we could be receiving anywhere from 10,000 to 100,000 data points per minute from each array. Then, we bring those back home, we put them into a database, and we run a lot of intensive analytics on those data.
Once you're doing that, you realize that as soon as you do something, you have this data you're starting to leverage. You're making support recommendations and so on, but then you realize you could do a lot more with it. We can do dynamic cache sizing. We can figure out how much cache a customer needs based on an analysis of their real workloads.
We found that big data is really paying off for us. We want to continue to increase how much it's paying off for us, but to do that we need to be able to do bigger queries faster. We have a team of data scientists and we don't want them sitting here twiddling their thumbs. That’s what brought us to Vertica at Nimble.
Using Big Data
Gardner: It's an interesting juxtaposition that you're using big data in order to better manage data and storage. What better use of it? And what sort of efficiencies are we talking about here, when you are able to get that data in that massive scale and do these analytics and then go back out into the field and adjust? What does that get for you?
Lancaster: We have a very tight feedback loop. In one release we put out, we may make some changes in the way certain things happen on the back end, for example, the way NVRAM is drained. There are some very particular details around that, and we can observe very quickly how that performs under different workloads. We can make tweaks and do a lot of tuning.
Without the kind of data we have, we might have to have multiple cases being opened on performance in the field and escalations, looking at cores, and then simulating things in the lab.
It's a very labor-intensive, slow process with very little data to base the decision on. When you bring home operational data from all your products in the field, you're now talking about being able to figure out in near real-time the distribution of workloads in the field and how people access their storage. I think we have a better understanding of the way storage works in the real world than any other storage vendor, simply because we have the data.
Gardner: So it's an interesting combination of a product lifecycle approach to getting data -- but also combining a service with a product in such a way that you're adjusting in real time.
Lancaster: That’s right. We do a lot of neat things. We do capacity forecasting. We do a lot of predictive analytics to try to figure out when the storage administrator is going to need to purchase something, rather than having them just stumble into the fact that they need to provision for equipment because they've run out of space.
A lot of things that should have been done in storage from the very beginning that sound straightforward were simply never done. We're the first company to take a comprehensive approach to it. We open and close 80 percent of our cases automatically, 90 percent of them are automatically opened.
We have a suite of tools that run on this operational data, so we don't have to call people up and say, "Please gather this data for us. Please send us these log posts. Please send us these statistics." Now, we take a case that could have taken two or three days and we turn it into something that can be done in an hour.
That’s the kind of efficiency we gain that you can see, and the InfoSight service delivers that to our customers.
Gardner: Larry, just to be clear, you're supporting both flash and traditional disk storage, but you're able to exploit the hybrid relationship between them because of this data and analysis. Tell us a little bit about how the hybrid storage works.
Challenge for hard drives
Lancaster: At a high level, you have hard drives, which are inexpensive, but they're slow for random I/O. For sequential I/O, they are all right, but for random I/O performance, they're slow. It takes time to move the platter and the head. You're looking at 5 to 10 milliseconds seek time for random read.
That's been the challenge for hard drives. Flash drives have come out and they can dramatically improve on that. Now, you're talking about microsecond-order latencies, rather than milliseconds.
But the challenge there is that they're expensive. You could go buy all flash or you could go buy all hard drives and you can live with those downsides of each. Or, you can take the best of both worlds.
Then, there's a challenge. How do I keep the data that I need to access randomly in flash, but keep the rest of the data that I don't care so much about in a frequent random-read performance, keep that on the hard drives only, and in that way, optimize my use of flash. That's the way you can save money, but it's difficult to do that.
It comes down to having some understanding of the workloads that the customer is running and being able to anticipate the best algorithms and parameters for those algorithms to make sure that the right data is in flash.
We've built up an enormous dataset covering thousands of system-years of real-world usage to tell us exactly which approaches to caching are going to deliver the most benefit. It would be hard to be the best hybrid storage solution without the kind of analytics that we're doing.
Gardner: Then, to extrapolate a little bit higher, or maybe wider, for how this benefits an organization, the analysis that you're gathering also pertains to the data lifecycle, things like disaster recovery (DR), business continuity, backups, scheduling, and so forth. Tell us how the data gathering analytics has been applied to that larger data lifecycle equation.
Lancaster: You're absolutely right. One of the things that we do is make sure that we audit all of the storage that our customers have deployed to understand how much of it is protected with local snapshots, how much of it is replicated for disaster recovery, and how much incremental space is required to increase retention time and so on.
We have very efficient snapshots, but at the end of the day, if you're making changes, snapshots still do take some amount of space. So, learning exactly what is that overhead, and how can we help you achieve your disaster recovery goals.
We have a good understanding of that in the field. We go to customers with proactive service recommendations about what they could and should do. But we also take into account the fact that they may be doing DR when we forecast how much capacity they are going to need.
It is part of a larger lifecycle that we address, but at the end of the day, for my team it's still all about analytics. It's about looking to the data as the source of truth and as the source of recommendation.
We can tell you roughly how much space you're going to need to do disaster recovery on a given type of application, because we can look in our field and see the distribution of the extra space that would take and what kind of bandwidth you're going to need. We have all that information at our fingertips.
When you start to work this way, you realize that you can do things you couldn't do before. And the things you could do before, you can do orders of magnitude better. So we're a great case of actually applying data science to the product lifecycle, but also to front-line revenue and cost enhancement.
Gardner: How can you actually get that analysis in the speed, at the scale, and at the cost that you require?
Lancaster: To give you a brief history of my awareness of HP Vertica and my involvement around the product, I don’t remember the exact year, but it may have been eight years ago roughly. At some point, there was an announcement that Mike Stonebraker was involved in a group that was going to productize the C-Store Database, which was sort of an academic experiment at UC Berkeley, to understand the benefits and capabilities of real column store.
I was immediately interested and contacted them. I was working at another storage company at the time. I had a 20 terabyte (TB) data warehouse, which at the time was one of the largest Oracle on Linux data warehouses in the world.
They didn't want to touch that opportunity just yet, because they were just starting out in alpha mode. I hooked up with them again a few years later, when I was CTO at a company called Glassbeam, where we developed what's substantially an extract, transform, and load (ETL) platform.
By then, they were well along the road. They had a great product and it was solid. So we tried it out, and I have to tell you, I fell in love with Vertica because of the performance benefits that it provided.
When you start thinking about collecting as many different data points as we like to collect, you have to recognize that you’re going to end up with a couple choices on a row store. Either you're going to have very narrow tables and a lot of them or else you're going to be wasting a lot of I/O overhead, retrieving entire rows where you just need a couple fields.
That was what piqued my interest at first. But as I began to use it more and more at Glassbeam, I realized that the performance benefits you could gain by using HP Vertica properly were another order of magnitude beyond what you would expect just with the column-store efficiency.
That's because of certain features that Vertica allows, such as something called pre-join projections. We can drill into that sort of stuff more if you like, but, at a high-level, it lets you maintain the normalized logical integrity of your schema, while having under the hood, an optimized denormalized query performance physically on disk.
Now you might ask you can be efficient if you have a denormalized structure on disk. It's because Vertica allows you to do some very efficient types of encoding on your data. So all of the low cardinality columns that would have been wasting space in a row store end up taking almost no space at all.
What you find, at least it's been my impression, is that Vertica is the data warehouse that you would have wanted to have built 10 or 20 years ago, but nobody had done it yet.
Nowadays, when I'm evaluating other big data platforms, I always have to look at it from the perspective of it's great, we can get some parallelism here, and there are certain operations that we can do that might be difficult on other platforms, but I always have to compare it to Vertica. Frankly, I always find that Vertica comes out on top in terms of features, performance, and usability.
Gardner: When you arrived there at Nimble Storage, what were they using, and where are you now on your journey into a transition to Vertica?
Lancaster: I built the environment here from the ground up. When I got here, there were roughly 30 people. It's a very small company. We started with Postgres. We started with something free. We didn’t want to have a large budget dedicated to the backing infrastructure just yet. We weren’t ready to monetize it yet.
So, we started on Postgres and we've scaled up now to the point where we have about 100 TBs on Postgres. We get decent performance out of the database for the things that we absolutely need to do, which are micro-batch updates and transactional activity. We get that performance because the database lives on Nimble Storage.
I don't know what the largest unsharded Postgres instance is in the world, but I feel like I have one of them. It's a challenge to manage and leverage. Now, we've gotten to the point where we're really enjoying doing larger queries. We really want to understand the entire installed base of how we want to do analyses that extend across the entire base.
We want to understand the lifecycle of a volume. We want to understand how it grows, how it lives, what its performance characteristics are, and then how gradually it falls into senescence when people stop using it. It turns out there is a lot of really rich information that we now have access to to understand storage lifecycles in a way I don't think was possible before.
But to do that, we need to take our infrastructure to the next level. So we've been doing that and we've loaded a large number of our sensor data that’s the numerical data I have talked about into Vertica, started to compare the queries, and then started to use Vertica more and more for all the analysis we're doing.
Internally, we're using Vertica, just because of the performance benefits. I can give you an example. We had a particular query, a particularly large query. It was to look at certain aspects of latency over a month across the entire installed base to understand a little bit about the distribution, depending on different factors, and so on.
We ran that query in Postgres, and depending on how busy the server was, it took anywhere from 12 to 24 hours to run. On Vertica, to run the same query on the same data takes anywhere from three to seven seconds.
I anticipated that because we were aware upfront of the benefits we'd be getting. I've seen it before. We knew how to structure our projections to get that kind of performance. We knew what kind of infrastructure we'd need under it. I'm really excited. We're getting exactly what we wanted and better.
This is only a three node cluster. Look at the performance we're getting. On the smaller queries, we're getting sub-second latencies. On the big ones, we're getting sub-10 second latencies. It's absolutely amazing. It's game changing.
People can sit at their desktops now, manipulate data, come up with new ideas and iterate without having to run a batch and go home. It's a dramatic productivity increase. Data scientists tend to be fairly impatient. They're highly paid people, and you don’t want them sitting at their desk waiting to get an answer out of the database. It's not the best use of their time.
Gardner: Larry, is there another aspect to the HP Vertica value when it comes to the cloud model for deployment? It seems to me that if Nimble Storage continues to grow rapidly and scales that, bringing all that data back to a central single point might be problematic. Having it distributed or in different cloud deployment models might make sense. Is there something about the way Vertica works within a cloud services deployment that is of interest to you as well?
Lancaster: There's the ease of adding nodes without downtime, the fact that you can create a K-safe cluster. If my cluster is 16 nodes wide now, and I want two nodes redundancy, it's very similar to RAID. You can specify that, and the database will take care of that for you. You don’t have to worry about the database going down and losing data as a result of the node failure every time or two.
I love the fact that you don’t have to pay extra for that. If I want to put more cores or nodes on it or I want to put more redundancy into my design, I can do that without paying more for it. Wow! That’s kind of revolutionary in itself.
It's great to see a database company incented to give you great performance. They're incented to help you work better with more nodes and more cores. They don't have to worry about people not being able to pay the additional license fees to deploy more resources. In that sense, it's great.
We have our own private cloud -- that’s how I like to think of it -- at an offsite colocation facility. We do DR through Nimble Storage. At the same time, we have a K-safe cluster. We had a hardware glitch on one of the nodes last week, and the other two nodes stayed up, served data, and everything was fine.
Those kinds of features are critical, and that ability to be flexible and expand is critical for someone who is trying to build a large cloud infrastructure, because you're never going to know in advance exactly how much you're going to need.
If you do your job right as a cloud provider, people just want more and more and more. You want to get them hooked and you want to get them enjoying the experience. Vertica lets you do that.
You may also be interested in:
- MZI Healthcare Identifies Big Data Patient Productivity Gems Using HP Vertica
- Thought Leader Interview: HP's Global CISO Brett Wahlin on the future of Security and Risk
- Panel explains how CSC creates a tough cybersecurity posture against global threats
- Risk and complexity: Businesses need to get a grip
- HP Vertica General Manager Colin Mahony on the next generation of analytics platforms
- Advanced IT monitoring Delivers Predictive Diagnostics Focus to United Airlines
- CSC and HP team up to define the new state needed for comprehensive enterprise cybersecurity
- BYOD brings new security challenges for IT: Allowing greater access while protecting networks
- HP Vertica Architecture Gives Massive Performance Boost to Toughest BI Queries for Infinity Insurance
Software AG helps organizations transform into Digital Enterprises, so they can differentiate from competitors and better engage customers, partners and employees. Using the Software AG Suite, companies can close the gap between business and IT to create digital systems of differentiation that drive front-line agility. We offer four on-ramps to the Digital Enterprise: alignment through collaborative process analysis; transformation through portfolio management; agility through process automation and integration; and visibility through intelligent business operations and big data.
Sep. 29, 2014 09:30 PM EDT Reads: 1,195
There will be 50 billion Internet connected devices by 2020. Today, every manufacturer has a propriety protocol and an app. How do we securely integrate these "things" into our lives and businesses in a way that we can easily control and manage? Even better, how do we integrate these "things" so that they control and manage each other so our lives become more convenient or our businesses become more profitable and/or safe? We have heard that the best interface is no interface. In his session at Internet of @ThingsExpo, Chris Matthieu, Co-Founder & CTO at Octoblu, Inc., will discuss how these devices generate enough data to learn our behaviors and simplify/improve our lives. What if we could connect everything to everything? I'm not only talking about connecting things to things but also systems, cloud services, and people. Add in a little machine learning and artificial intelligence and now we have something interesting...
Sep. 29, 2014 06:45 AM EDT Reads: 1,795
Last week, while in San Francisco, I used the Uber app and service four times. All four experiences were great, although one of the drivers stopped for 30 seconds and then left as I was walking up to the car. He must have realized I was a blogger. None the less, the next car was just a minute away and I suffered no pain. In this article, my colleague, Ved Sen, Global Head, Advisory Services Social, Mobile and Sensors at Cognizant shares his experiences and insights.
Sep. 28, 2014 09:45 AM EDT Reads: 1,476
We are reaching the end of the beginning with WebRTC and real systems using this technology have begun to appear. One challenge that faces every WebRTC deployment (in some form or another) is identity management. For example, if you have an existing service – possibly built on a variety of different PaaS/SaaS offerings – and you want to add real-time communications you are faced with a challenge relating to user management, authentication, authorization, and validation. Service providers will want to use their existing identities, but these will have credentials already that are (hopefully) irreversibly encoded. In his session at Internet of @ThingsExpo, Peter Dunkley, Technical Director at Acision, will look at how this identity problem can be solved and discuss ways to use existing web identities for real-time communication.
Sep. 27, 2014 11:30 PM EDT Reads: 1,824
Can call centers hang up the phones for good? Intuitive Solutions did. WebRTC enabled this contact center provider to eliminate antiquated telephony and desktop phone infrastructure with a pure web-based solution, allowing them to expand beyond brick-and-mortar confines to a home-based agent model. It also ensured scalability and better service for customers, including MUY! Companies, one of the country's largest franchise restaurant companies with 232 Pizza Hut locations. This is one example of WebRTC adoption today, but the potential is limitless when powered by IoT. Attendees will learn real-world benefits of WebRTC and explore future possibilities, as WebRTC and IoT intersect to improve customer service.
Sep. 27, 2014 10:30 PM EDT Reads: 1,755
From telemedicine to smart cars, digital homes and industrial monitoring, the explosive growth of IoT has created exciting new business opportunities for real time calls and messaging. In his session at Internet of @ThingsExpo, Ivelin Ivanov, CEO and Co-Founder of Telestax, will share some of the new revenue sources that IoT created for Restcomm – the open source telephony platform from Telestax. Ivelin Ivanov is a technology entrepreneur who founded Mobicents, an Open Source VoIP Platform, to help create, deploy, and manage applications integrating voice, video and data. He is the co-founder of TeleStax, an Open Source Cloud Communications company that helps the shift from legacy IN/SS7 telco networks to IP-based cloud comms. An early investor in multiple start-ups, he still finds time to code for his companies and contribute to open source projects.
Sep. 27, 2014 10:30 PM EDT Reads: 2,230
The Internet of Things (IoT) promises to create new business models as significant as those that were inspired by the Internet and the smartphone 20 and 10 years ago. What business, social and practical implications will this phenomenon bring? That's the subject of "Monetizing the Internet of Things: Perspectives from the Front Lines," an e-book released today and available free of charge from Aria Systems, the leading innovator in recurring revenue management.
Sep. 27, 2014 09:45 PM EDT Reads: 2,407
The Internet of Things will put IT to its ultimate test by creating infinite new opportunities to digitize products and services, generate and analyze new data to improve customer satisfaction, and discover new ways to gain a competitive advantage across nearly every industry. In order to help corporate business units to capitalize on the rapidly evolving IoT opportunities, IT must stand up to a new set of challenges.
Sep. 27, 2014 08:45 PM EDT Reads: 2,295
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, Big Data and deployments are processing massive data volumes from wearables, utilities and other machines.
Sep. 27, 2014 01:00 PM EDT Reads: 1,986
All major researchers estimate there will be tens of billions devices – computers, smartphones, tablets, and sensors – connected to the Internet by 2020. This number will continue to grow at a rapid pace for the next several decades. With major technology companies and startups seriously embracing IoT strategies, now is the perfect time to attend @ThingsExpo in Silicon Valley. Learn what is going on, contribute to the discussions, and ensure that your enterprise is as "IoT-Ready" as it can be!
Sep. 27, 2014 11:00 AM EDT Reads: 2,104
P2P RTC will impact the landscape of communications, shifting from traditional telephony style communications models to OTT (Over-The-Top) cloud assisted & PaaS (Platform as a Service) communication services. The P2P shift will impact many areas of our lives, from mobile communication, human interactive web services, RTC and telephony infrastructure, user federation, security and privacy implications, business costs, and scalability. In his session at Internet of @ThingsExpo, Erik Lagerway, Co-founder of Hookflash, will walk through the shifting landscape of traditional telephone and voice services to the modern P2P RTC era of OTT cloud assisted services.
Sep. 26, 2014 11:45 PM EDT Reads: 1,464
While great strides have been made relative to the video aspects of remote collaboration, audio technology has basically stagnated. Typically all audio is mixed to a single monaural stream and emanates from a single point, such as a speakerphone or a speaker associated with a video monitor. This leads to confusion and lack of understanding among participants especially regarding who is actually speaking. Spatial teleconferencing introduces the concept of acoustic spatial separation between conference participants in three dimensional space. This has been shown to significantly improve comprehension and conference efficiency.
Sep. 26, 2014 10:45 PM EDT Reads: 1,404
The Internet of Things is tied together with a thin strand that is known as time. Coincidentally, at the core of nearly all data analytics is a timestamp. When working with time series data there are a few core principles that everyone should consider, especially across datasets where time is the common boundary. In his session at Internet of @ThingsExpo, Jim Scott, Director of Enterprise Strategy & Architecture at MapR Technologies, will discuss single-value, geo-spatial, and log time series data. By focusing on enterprise applications and the data center, he will use OpenTSDB as an example to explain some of these concepts including when to use different storage models.
Sep. 26, 2014 07:45 PM EDT Reads: 2,241
SYS-CON Events announced today that Gridstore™, the leader in software-defined storage (SDS) purpose-built for Windows Servers and Hyper-V, 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. Gridstore™ is the leader in software-defined storage purpose built for virtualization that is designed to accelerate applications in virtualized environments. Using its patented Server-Side Virtual Controller™ Technology (SVCT) to eliminate the I/O blender effect and accelerate applications Gridstore delivers vmOptimized™ Storage that self-optimizes to each application or VM across both virtual and physical environments. Leveraging a grid architecture, Gridstore delivers the first end-to-end storage QoS to ensure the most important App or VM performance is never compromised. The storage grid, that uses Gridstore’s performance optimized nodes or capacity optimized nodes, starts with as few a...
Sep. 26, 2014 06:15 PM EDT Reads: 1,586
The Transparent Cloud-computing Consortium (abbreviation: T-Cloud Consortium) will conduct research activities into changes in the computing model as a result of collaboration between "device" and "cloud" and the creation of new value and markets through organic data processing High speed and high quality networks, and dramatic improvements in computer processing capabilities, have greatly changed the nature of applications and made the storing and processing of data on the network commonplace. These technological reforms have not only changed computers and smartphones, but are also changing the data processing model for all information devices. In particular, in the area known as M2M (Machine-To-Machine), there are great expectations that information with a new type of value can be produced using a variety of devices and sensors saving/sharing data via the network and through large-scale cloud-type data processing. This consortium believes that attaching a huge number of devic...
Sep. 26, 2014 06:00 PM EDT Reads: 1,515
Innodisk is a service-driven provider of industrial embedded flash and DRAM storage products and technologies, with a focus on the enterprise, industrial, aerospace, and defense industries. Innodisk is dedicated to serving their customers and business partners. Quality is vitally important when it comes to industrial embedded flash and DRAM storage products. That’s why Innodisk manufactures all of their products in their own purpose-built memory production facility. In fact, they designed and built their production center to maximize manufacturing efficiency and guarantee the highest quality of our products.
Sep. 26, 2014 05:00 PM EDT Reads: 1,514
Can call centers hang up the phones for good? Intuitive Solutions did. WebRTC enabled this contact center provider to eliminate antiquated telephony and desktop phone infrastructure with a pure web-based solution, allowing them to expand beyond brick-and-mortar confines to a home-based agent model. Download Slide Deck: ▸ Here
Sep. 26, 2014 10:00 AM EDT Reads: 1,469
All major researchers estimate there will be tens of billions devices - computers, smartphones, tablets, and sensors - connected to the Internet by 2020. This number will continue to grow at a rapid pace for the next several decades. Over the summer Gartner released its much anticipated annual Hype Cycle report and the big news is that Internet of Things has now replaced Big Data as the most hyped technology. Indeed, we're hearing more and more about this fascinating new technological paradigm. Every other IT news item seems to be about IoT and its implications on the future of digital business.
Sep. 26, 2014 10:00 AM EDT Reads: 2,000
BSQUARE is a global leader of embedded software solutions. We enable smart connected systems at the device level and beyond that millions use every day and provide actionable data solutions for the growing Internet of Things (IoT) market. We empower our world-class customers with our products, services and solutions to achieve innovation and success. For more information, visit www.bsquare.com.
Sep. 26, 2014 09:45 AM EDT Reads: 1,361
With the iCloud scandal seemingly in its past, Apple announced new iPhones, updates to iPad and MacBook as well as news on OSX Yosemite. Although consumers will have to wait to get their hands on some of that new stuff, what they can get is the latest release of iOS 8 that Apple made available for most in-market iPhones and iPads. Originally announced at WWDC (Apple’s annual developers conference) in June, iOS 8 seems to spearhead Apple’s newfound focus upon greater integration of their products into everyday tasks, cross-platform mobility and self-monitoring. Before you update your device, here is a look at some of the new features and things you may want to consider from a mobile security perspective.
Sep. 26, 2014 09:00 AM EDT Reads: 1,349