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Don't Let Perishable Apps Go Bad

When we think of the application lifecycle, we tend to focus on apps that are built to last. CRM, mobile banking, streaming video, or m-commerce apps are usually built for users to access for weeks, months, or years at a time. If performance problems arise after the app has been deployed, the issues can be discovered and solved through proper monitoring and remediation.

But what if your mission-critical app is not designed to last? What if your app is perishable with a shelf life of only a few hours or days? With these apps, traditional monitoring and remediation become irrelevant. The app will expire before it can be fixed. But, that doesn't mean that perishable apps are fated to be poor performing and unsatisfying. Proper planning, testing, and additional resources can keep your perishable apps from going bad.

One of the most egregious and public perishable app failures happened this past Election Day. The Romney campaign's voter turnout app, codenamed Orca, was supposed to organize, energize, and empower volunteers. Months of development and millions in budget were dedicated to a mission-critical app that would “live” for less than 24 hours.

From all accounts, the app did not help the campaign's voter turnout efforts. It performed so poorly that Comcast, the ISP monitoring the app's servers, temporarily shut off service because it thought the repeated attempts of the campaign users to access data came from a denial-of-service attack.

While the purpose of your perishable app might not be for something as important as winning a presidential election, the blowback to your business could be proportional if it fails.

Think About Performance from the Beginning

What the Romney campaign should have done, and what you can do, is to think about performance as a critical requirement from the beginning. One way to do that is to engage in detailed location-specific planning. If you know your app will be used primarily for a five-day conference in Vegas, ensure that your network data for testing is specific to that exact location. Through network virtualization and in-the-field discovery, the exact network conditions for a specific exhibition hall, or even a specific room, can be used in your performance testing.

Remember, however, that even if your app performs well on an “off day” connection, the network will hold a different profile at the height of your event. Orca worked when only a few people taxed the network from the field or inside the Boston Garden. At peak network usage, Orca was overwhelmed.

You avoid this issue if you load and performance test for both typical and worst case scenarios. Say you have 15 thousand conference registrations. What happens when seven thousand more register at the door? You expect most users to connect over the venue's Wi-Fi, but what if that goes down and they have to connect on a 3G or 4G mobile network?

By virtualizing these variable load and network conditions in your pre-deployment performance testing, you can test to a variety of typical and worst-case scenarios, better understand the breaking points for your app, and plan accordingly.

Worst case planning extends beyond development and testing. We've all suffered the frustration of a flight delay that could have been tempered if information was provided as to the cause of the delay and/or how long the delay would last. That way, we know if we had time to grab a burger and beer or needed to book a hotel room for the night.

If your app does go down, have fallback scenarios in place. Romney campaign volunteers could have manually reported poll results with an automated phone system. For events, caching is a useful option for providing information, such as a map or schedule, when connectivity is compromised.

Make sure your app fails gracefully. Build in an automated email alert when your app hits a certain performance threshold. This way, your users are informed about the issue and know you are doing all you can do to fix the problem. A little bit of common sense and courtesy goes a long way to keep users happy and reduce blowback to your organization in the event of an app failure.

Since the app is only usable for a fixed amount of time, putting it in the cloud makes sense for easy upload and termination. Hosting it in the cloud also makes it easy and cost effective to call up new servers to address peak usage requirements. But, you need to know when you should call up a new server. This can be accomplished in testing to see if you can scale your app. If so, you can see where bottlenecks are likely to occur. That data can then be used to automate the spin-up of additional servers.

There is no reason why performance for perishable apps should be an afterthought. If you are developing an app, it is for a specific reason and your users will expect it to meet their performance needs. Anything less reflects negatively on your company's reputation and, perhaps, bottom line. Through proper planning, testing, and by having additional resources at the ready, you can ensure your perishable app provides users with a satisfying, rather than rotten, experience.

ABOUT Dave Berg

Dave Berg is the Vice President of Product Strategy at Shunra Software, a Philadelphia-based company specializing in network virtualization to help firms worldwide ensure application performance and end user experience.

Related Links:

www.shunra.com

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Don't Let Perishable Apps Go Bad

When we think of the application lifecycle, we tend to focus on apps that are built to last. CRM, mobile banking, streaming video, or m-commerce apps are usually built for users to access for weeks, months, or years at a time. If performance problems arise after the app has been deployed, the issues can be discovered and solved through proper monitoring and remediation.

But what if your mission-critical app is not designed to last? What if your app is perishable with a shelf life of only a few hours or days? With these apps, traditional monitoring and remediation become irrelevant. The app will expire before it can be fixed. But, that doesn't mean that perishable apps are fated to be poor performing and unsatisfying. Proper planning, testing, and additional resources can keep your perishable apps from going bad.

One of the most egregious and public perishable app failures happened this past Election Day. The Romney campaign's voter turnout app, codenamed Orca, was supposed to organize, energize, and empower volunteers. Months of development and millions in budget were dedicated to a mission-critical app that would “live” for less than 24 hours.

From all accounts, the app did not help the campaign's voter turnout efforts. It performed so poorly that Comcast, the ISP monitoring the app's servers, temporarily shut off service because it thought the repeated attempts of the campaign users to access data came from a denial-of-service attack.

While the purpose of your perishable app might not be for something as important as winning a presidential election, the blowback to your business could be proportional if it fails.

Think About Performance from the Beginning

What the Romney campaign should have done, and what you can do, is to think about performance as a critical requirement from the beginning. One way to do that is to engage in detailed location-specific planning. If you know your app will be used primarily for a five-day conference in Vegas, ensure that your network data for testing is specific to that exact location. Through network virtualization and in-the-field discovery, the exact network conditions for a specific exhibition hall, or even a specific room, can be used in your performance testing.

Remember, however, that even if your app performs well on an “off day” connection, the network will hold a different profile at the height of your event. Orca worked when only a few people taxed the network from the field or inside the Boston Garden. At peak network usage, Orca was overwhelmed.

You avoid this issue if you load and performance test for both typical and worst case scenarios. Say you have 15 thousand conference registrations. What happens when seven thousand more register at the door? You expect most users to connect over the venue's Wi-Fi, but what if that goes down and they have to connect on a 3G or 4G mobile network?

By virtualizing these variable load and network conditions in your pre-deployment performance testing, you can test to a variety of typical and worst-case scenarios, better understand the breaking points for your app, and plan accordingly.

Worst case planning extends beyond development and testing. We've all suffered the frustration of a flight delay that could have been tempered if information was provided as to the cause of the delay and/or how long the delay would last. That way, we know if we had time to grab a burger and beer or needed to book a hotel room for the night.

If your app does go down, have fallback scenarios in place. Romney campaign volunteers could have manually reported poll results with an automated phone system. For events, caching is a useful option for providing information, such as a map or schedule, when connectivity is compromised.

Make sure your app fails gracefully. Build in an automated email alert when your app hits a certain performance threshold. This way, your users are informed about the issue and know you are doing all you can do to fix the problem. A little bit of common sense and courtesy goes a long way to keep users happy and reduce blowback to your organization in the event of an app failure.

Since the app is only usable for a fixed amount of time, putting it in the cloud makes sense for easy upload and termination. Hosting it in the cloud also makes it easy and cost effective to call up new servers to address peak usage requirements. But, you need to know when you should call up a new server. This can be accomplished in testing to see if you can scale your app. If so, you can see where bottlenecks are likely to occur. That data can then be used to automate the spin-up of additional servers.

There is no reason why performance for perishable apps should be an afterthought. If you are developing an app, it is for a specific reason and your users will expect it to meet their performance needs. Anything less reflects negatively on your company's reputation and, perhaps, bottom line. Through proper planning, testing, and by having additional resources at the ready, you can ensure your perishable app provides users with a satisfying, rather than rotten, experience.

ABOUT Dave Berg

Dave Berg is the Vice President of Product Strategy at Shunra Software, a Philadelphia-based company specializing in network virtualization to help firms worldwide ensure application performance and end user experience.

Related Links:

www.shunra.com

Hot Topics

The Latest

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...