Archive for iPhone

Validating Application Detection Signatures

In the new world of next-generation networks, pretty much every leading network equipment manufacturer (NEM) today has application-awareness built into their products. Whether it’s an application firewall, serving gateway or edge router, they’re all using deep packet inspection (DPI) to look deep into the network traffic to identify the specific application.

For example, Cisco has Application Visibility & Control, Juniper has AppSecure, Palo Alto Networks has App-ID, Sandvine has Traffic Identification and Tellabs has Application Identification.

Each vendor has their own proprietary database comprised of hundreds or thousands of application signatures and on finding a match, their system can then take action based on the defined policy (e.g. block an application, apply QoS, etc…)

Before these new application signatures are released however, testing is needed to ensure the accuracy of the detection. One of the major challenges is to avoid the false positive, in which an application is misclassified.

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Mu App Quadrant #3 – Skype Voice on Mac (OS X) Expends at Least 28% More for Consumers and Operator Networks than on Other Devices

In the previous versions of the Mu App Quadrant we first compared the most popular video services, and then specifically focused on Netflix across multiple endpoints. Now, in this third edition we have looked at Skype. Not only is it one of the most popular voice apps on the internet with over 30 million users online at peak times, it’s also one of the biggest bandwidth guzzlers, due in part to the P2P nature of its architecture.

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Driving Real Application Traffic Through Junosphere Virtual Infrastructure

Today, Juniper announced Junosphere™ Lab, an innovative on-demand service that gives service providers and enterprises immediate and low cost access to a virtualized environment for designing and testing networks. Very cool stuff – leveraging the power of the cloud and helping customers dramatically reduce their TCO while accelerating the time to model networks.

Real Traffic in a Virtual Environment

So when you spin up a network environment and model a production topology, you’ll then need a way to create realistic application traffic to understand its impact across the network. That’s where we come in.

Mu Studio Performance has been integrated into the Junosphere Lab so you can just as easily spin up (and tear down) virtual instances of our performance testing solution to quickly and accurately recreate a mix of applications that represent the production environment – that is, real users on real devices, running real applications.

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Ensuring the Accuracy of the Mu TestCloud Application Tests

In a previous blog I discussed how we had started to build out the test content for different kinds of applications across categories like P2P, video, chat and social media in our Mu TestCloud store. Fast-forward to today, and we’ve now got well over 2,000 tests, with coverage for hundreds of different apps. We’ve also got lots of customers who are actively using these ready-to-run tests for a wide range of use-cases – everything from verifying application detection signatures to validating application policies, as well as billing and charging.

But regardless of their domain, there are two common questions that customers are curious to understand:

1. How do we select the applications in the first place?
2. How do we ensure the accuracy of the tests?

So for this blog I’m going to give you a behind-the-scenes view into our test content creation process.

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Mu App Quadrant #2 – Netflix on iPhone & iPad Impacts Operator Networks More than on Android Devices

In our first edition of the Mu App Quadrant, we compared the run-time aspects of the most popular desktop video applications – Netflix, YouTube, Hulu and Amazon Video on Demand – and showed that Netflix is not particularly friendly to both Consumers and Operators.

It’s not just the apps – devices matter too!
Operators continue to struggle with the unpredictable growth of applications and the devices used to access them. With millions of people running applications like Netflix, spikes can occur on the network which often leads to disruption of other applications and services.

Recently, Korea Telecom suffered a network outage where a third-party app took the voice-call success rate down to a mere 10% because the signaling traffic generated by the app overloaded its network.
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Creating an Application Mix to Model the Production Network

Video and P2P Rule!
The traffic making up today’s networks is in a rapid state of flux. Just last week Sandvine, in their Spring 2011 Global Phenomena Report, noted that real-time entertainment continues to increase, and within North America represents almost 50% of peak fixed access traffic (much of this of course is due to Netflix). P2P traffic also continues to carve out a sizeable piece of the pie at around 20%. The rest is a mix of voice, business apps, games, Facebook and chat.

What’s interesting though is that the relative amount of traffic that isn’t application-level is tiny – all the stuff that makes networks run like DNS, ICMP, BGP and so on.

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Mu App Quadrant #1 – Not all video streaming apps are created equal…

There’s been a lot of debate recently about the impact of video apps on the network. According to Nielsen, Netflix alone now accounts for 20% of downstream traffic during peak times in the United States. In a previous blog Kowsik explained the behind-the-scenes interactions that are happening unbeknownst to you when you watch a Netflix movie. So that got us thinking – are all the popular video apps as network-intensive as Netflix? Are some video apps more user-friendly with their bandwidth consumption than others? Are some video apps more operator-friendly with their consumption of networking resources?

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blitz.io: The [long] night before an iPhone app launch

We’d been helping out a blitz.io customer late into the night and here’s what we were up against. A spanking new iPhone app was going live the next day. It was expected to support 100,000 concurrent users with cool new location-aware awesomeness (can’t really talk about it, sorry!). The back-end architecture had a load balancer, multiple web servers, Fast-CGI scripts, replicated MySQL databases, memcached and yet we just couldn’t get past a few thousand concurrent users. This blog is about insights into performance tuning and bottlenecks uncovered by using blitz.io to load test their application.

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Testing for Today’s Most Popular Apps…The Holy Grail of Testing?

I’ve been in the testing business for many years now and I’ve come across a lot of grandiose claims by test tool vendors with regards to features and capabilities that just sound too good to be true. And in many cases, they are.

When it comes to today’s world of smartphones and tablets and the explosive growth of web and mobile applications, it’s mind-blowing to see the sheer quantity of apps out there. If you look at the number of apps available today on just three of the leading app stores (Apple, Android and Facebook), there’s over a million applications, with tens of thousands of new ones every single month. Full Post »

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Scaling it out – Building high performance mobile apps

We’ve been using blitz.io to optimize the backend server of a location-aware iPhone app (customer) that’s going to be released soon and we learned some interesting things that I thought I’ll pass on. The first key observation really is that performance optimization is a continuous, iterative and agile process. This means that the tools you use to validate performance have to be painless and friendly so you spend more time on tuning your code instead of figuring out how to run the tests. RESTful backends for iPhone and Android apps are exploding. I think we are past the era of simple utility apps like Sudoku and Chess, but instead the social element is now connecting these apps to backend RESTful servers that are expected to scale to the extreme, given the sheer number of mobile devices there are.

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