Wednesday, January 8, 2014

Nimble Storage leverages big data and cloud to produce data performance optimization on the fly

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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.
That’s the kind of efficiency we gain that you can see, and the InfoSight service delivers that to our customers.

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.
It would be hard to be the best hybrid storage solution without the kind of analytics that we're doing.

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.

Larger lifecycle

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?
I have to tell you, I fell in love with Vertica because of the performance benefits that it provided.

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.

[Learn more about column store architectures and how they benefit data speed and management for Infinity Insurance.]

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.

Greater efficiency

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.
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.

Rich information

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.
I'm really excited. We're getting exactly what we wanted and better.

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?

No worries

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.
If you do your job right as a cloud provider, people just want more and more and more.

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.
Listen to the podcast. Find it on iTunes. Read a full transcript or download a copy. Sponsor: HP.

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Tuesday, January 7, 2014

Inside story on how HP implemented the TippingPoint intrusion prevention system across its own security infrastructure

Listen to the podcast. Find it on iTunes. Read a full transcript or download a copy.

The high cost of unwanted intrusion and malware across corporate networks is well known. Less talked-about are the successful ways that organizations are thwarting ongoing, adaptive and often-insider-driven security breaches.

Companies are understandably reluctant to readily discuss either their defenses or mishaps. Yet HP, one of the world's largest companies, is both a provider and a practitioner of enterprise intrusion prevention systems (IPS). And so we asked HP to explain how it is both building and using such technologies, along with seeking some insider tips on best practices.

And so the next edition of the HP Discover Podcast Series explores the ins and outs of improving enterprise intrusion prevention. We learn how HP and its global cyber security partners have made the HP Global Network more resilient and safe. We also gain more insight into HP's vision for security and learn how that has been effectively translated into actual implementation.

The inside story comes from Jim O'Shea, Network Security Architect for HP Cyber Security Strategy and Infrastructure Engagement. The discussion is moderated by me, Dana Gardner, Principal Analyst at Interarbor Solutions.

Here are some excerpts:
Gardner: What are some of the major trends that are driving the need for better intrusion detection and prevention nowadays?

O’Shea: If you look at the past, you had reaction technologies. We had firewalls that blocked and looked at the port level. Then we evolved to trying to detect things that were malicious with intent by using IPS. But that was still a reactionary-type thing. It was a nice approach, but we were reacting. But if you knew it was bad, why did we let it in in the first place?

The evolution was in the IPS, to the prevention. If you know it's bad, why do you even want to see it? Why do you want to try to react to it? Just block it early. That’s the trend that we’ve been following.

Gardner: But we can’t just have a black-and-white entry. We want access control, rather than just a firewall. So is there a new thinking, a new vision, that’s been developed over the past several years about these networks and what should or shouldn't be allowed through them?

O’Shea: You’re talking about letting the good in. Those are the evolutions and the trends that we are all trying to strive for. Let the good traffic in. Let who you are be a guide. Maybe look at what you have. You can also explore the health of your device. Those are all trends that we’re all striving for now.

Gardner: I recall Jim, that there was a Ponemon Institute report about a year or so ago that really outlined some of the issues here.

Number of attacks

O’Shea: The Ponemon study was illustrating the vast number of attacks and the trend toward the costs for intrusion. It was highlighting those type of trends, all of which we’re trying to head off. Those type of reports are guiding factors in taking a more proactive, automated-type response. [Learn more about intrusion prevention systems.]

Gardner: I suppose what’s also different nowadays is that we’re not only concerned with outside issues in terms of risk, but also insider attacks.

O’Shea: You’re exactly right. Are you hiring the right people? That’s a big issue. Are they being influenced? Those are all huge issues. Big data can handle some of that and pull that in. Our approach on intrusion detection isn’t to just look at what’s coming from the outside, but also look at all data traversing the network.
You have a whole rogue wireless-type approach in which people can gain access and can they probe and poke around.

When we deployed the TippingPoint solution, we didn’t change our policies or profiles that we were deploying based on whether it’s starting on the inside or starting on the outside. It was an equal deployment.

An insider attack could also be somebody who walks into a facility, gains physical access, and connects to your network. You have a whole rogue wireless-type approach in which people can gain access and can they probe and poke around. And if it’s malware traffic from our perspective, with the IDS we took the approach, inside or outside -- doesn’t matter. If we can detect it, if we can be in the path, it’s a block.

TippingPoint technology is an appliance-based technology. It’s an inline device. We deploy it inline. It sits in the network, and the traffic is flowing through it. It’s looking for characteristics or reputation on the type of traffic, and reputation is a more real-time change in the system. This network, IP address, or URL is known for malware, etc. That’s a dynamic update, but the static updates are signature-type, and the detection of vulnerability or a specific exploit aimed at an operating system.
So intrusion prevention is through the detection of that, and blocking and preventing that from completing its communication to the end node.

Bigger picture

All the events get logged into HP ArcSight to create the bigger picture. Are you seeing these type of events occurring other places? So you have the bigger picture correlation.

Network-based anomaly detection is the ability to detect something that is occurring in the network and it's based on an IP address or it’s based on a flow. Taking advantage of reputation we can insert those IP addresses, detected based on flow, that are doing something anomalous.

It could be that they’re beaconing out, spreading a worm. If they look like they’re causing concerns with a high degree of accuracy, then we can put that into the reputation and take advantage of moving blocks.

So reputation is a self-deploying feature. You insert an IP address into it and it can self-update. We haven’t taken the automated step yet, although that’s in the plan. Today, it’s a manual process for us, but ideally, through application programming interfaces (APIs), we can automate all that. It works in a lab, but we haven’t deployed it on our production that way.

Gardner: Clearly HP is a good example of a large enterprise, one of the largest in the world, with global presence, with a lot of technology, a lot of intellectual property, and therefore a lot to protect. Let’s look at how you actually approached protecting the HP network.
We wanted to prevent mal traffic, mal-formed traffic, malware -- any traffic with the mal intent of reaching the data center.

What’s the vision, if you will, for HP's Global Cyber Security, when it comes to these newer approaches? Do you have an overarching vision that then you can implement? How do we begin to think about chunking out the problem in order to then solve it effectively?

O’Shea: You must be able to detect, block, and prevent as an overarching strategy. We also wanted to take advantage of inserting a giant filter inline on all data that’s going into the data center. We wanted to prevent mal traffic, mal-formed traffic, malware -- any traffic with the "mal" intent of reaching the data center.

So why make that an application decision to block and rely on host-level defenses, when we have the opportunity to do it at the network? So it made the network more hygienically clean, blocking traffic that you don’t want to see.

We wrapped it around the data center, so all traffic going into our data centers goes through that type of filter. [Learn more about intrusion prevention systems.]

Key to deployment

Because this is all an inline technology, and you are going inline in the network, you’re changing flows. It could be mal traffic, but yet maybe a researcher is trying to do something. So we need to have the ability to have that level of partnership with the network team. They have to see it. They have to understand what it is. It has to be manageable.

When we deployed it, we looked at what could go wrong and we designed around that. What could go wrong? A device failed. So we have an N+1 type installation. If a single device fails, we’re not down, we are not blocking traffic. We have the ability to handle the capacity of our network, which grows, and we are growing, and so it has to be built for the now and the future. It has to be manageable.

It has to be able to be understood by “first responders,” the people that get called first. Everybody blames the network first, and then it's the application afterward. So the network team gets pulled in on many calls, at all types of hours, and they have to be able to get that view.

That was key to get them broad-based training, so that the technology was there. Get a process integrated into how you’re going to handle updates and how you’re going to add beyond what TippingPoint recommended. TippingPoint makes a recommendation on profiles and new settings. If we take that, do we want to add other things? So we have to have a global cyber-security view and a global cyber-security input and have that all vetted.

The application team had to be onboard and aware, so that everybody understands. Finally, because we were going into a very large installed network that was handling a lot of different types of traffic, we brought in TippingPoint Professional Services and had everything looked at, re-looked at, and signed off on, so that what we’re doing is a best practice. We looked at it from multiple angles and took a lot of things into consideration.
We proxy the events. That gives us the ability to have multiple ArcSight instances and also to evolve.

Gardner: Is there something about TippingPoint and ArcSight that provides data, views, and analytics in such a way that it's easier for these groups to work together in ways that they hadn’t before?

O’Shea: One of the nice things about the way the TippingPoint events occur is that you have a choice. You can send them from an individual IDS units themselves or you can proxy them from the management console. Again, the ability to manage was critical to us, so we chose to do it from the console.

We proxy the events. That gives us the ability to have multiple ArcSight instances and also to evolve. ArcSight evolves. When they’re changing, evolving, and growing, and they want to bring up a new collector, we’re able to send very rapidly to the new collector.

ArcSight pulls in firewall logs. You can get proxy events and events from antivirus. You can pull in that whole view and get a bigger picture at the ArcSight console. The TippingPoint view is of what’s happening from the inline TippingPoint and what's traversing it. Then, the ArcSight view adds a lot of depth to that.

Very flexible

So it gives a very broad picture, but from the TippingPoint view, we’re very flexible and able to add and stay in step with ArcSight growth quickly. It's kind of a concert. That includes sending events on different ports. You’re not restricted to one port. If you want to create a secure port or a unique port for your events to go on to ArcSight, you have that ability.

After the deployment we’ve had some DoS attacks against us, and they have been blocked and deflected. We’ve had some other events that we have been able to block and defend rapidly. [Learn more about intrusion prevention systems.]
If you think back historically of how we dealt with them, those were kind of a Whac-A-Mole-type of defenses. Something happened, and you reacted. So I guess the metric would be that we’re not as reactionary, but do we have hard metrics to prove that? I don’t have those.

How much volume?

Gardner: We can appreciate the scale of what the systems are capable of. Do we have a number of events detected or that sort of thing, blocks per month, any sense of how much volume we can handle?

O’Shea: We took a month’s sample. I’m trying to recall the exact number, but it was 100 million events in one month that were detected as mal events. That’s including Internet-facing events. That’s why the volume is high, but it was 100 million events that were automatically blocked and that were flagged as mal events.
The Professional Services teams have been able to deploy in a very large network and have worked with the requirements that a large enterprise has.

The Professional Services teams have been able to deploy in a very large network and have worked with the requirements that a large enterprise has. That includes standard deployment, how things are connected and what the drawings are going to look like, as well as how are you going to cable it up.

A large enterprise has different standards than a small business would have, and that was a give back to the Professional Services to be able to deploy it in a large enterprise. It has been a good relationship, and there is always opportunity for improvement, but it certainly has helped.

Current trends

Gardner: Jim, looking to the future a little bit, we know that there’s going to be more and more cloud and hybrid-cloud types of activities. We’re certainly seeing already a huge uptick in mobile device and tablet use on corporate networks. This is also part of the bring-your-own-device (BYOD) trend that we’re seeing.

So should we expect a higher degree of risk and more variables and complication, and what does that portend for the use of these types of technologies going forward? How much gain do you get by getting on the IDS bandwagon sooner rather than later?

O’Shea: BYOD is a new twist on things and it means something different to everybody, because it's an acronym term, but let's take the view of you bringing in a product you buy.
BYOD is a new twist on things and it means something different to everybody, because it's an acronym term.

Somebody is always going to get a new device, they are going to bring in it, they are going to try it out, and they are going to connect it to the corporate network, if they can. And because they are coming from a different environment and they’re not necessarily to corporate standards, they may bring unwanted guests into the network, in terms of malware.

Now, we have the opportunity, because we are inline, to detect and block that right away. Because we are an integrated ecosystem, they will show up as anomalous events. ArcSight and our Cyber Defense Center will be able to see those events. So you get a bigger picture.

Those events can be then translated into removing that node from the network. We have that opportunity to do that. BYOD not only brings your own device, but it also brings things you don’t know that are going to happen, and the only way to block that is prevention and anomalous type detection, and then try to bring it altogether in a bigger picture.
Listen to the podcast. Find it on iTunes. Read a full transcript or download a copy.
Sponsor: HP. Learn more about intrusion prevention systems.

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