4 Differences Between Mobile and Server Performance Monitoring

According to eMarketer, as of 2014 Americans consume more media using mobile devices than laptops and desktops combined. This shift in consumer behavior is also occurring within corporations, as employees increasingly rely on mobile devices for their work.

With such a surge in mobile usage there is a growing need for corporations to ensure that their mobile experience is high quality and not broken.

Since 86% of mobile experiences occur within apps and not mobile browsers [source: Flurry], focusing on improving app performance has a larger impact on mobile quality.

The following are 4 key differences that companies monitoring their server (and website) performance should consider when selecting a mobile app performance monitoring solution.

1. Different Team, Different Needs

At most companies, mobile teams are not part of the server/website teams. Instead mobile teams are completely separate and in many cases they are an outsourced team.

The mobile teams have unique pain points when releasing mobile apps that are different from those of web and backend developers (more on this below). Solutions that slightly tweak the interface of a server performance monitoring service do not cut it. These teams require solutions designed from the ground up to solve their problems.

2. Scaling vs. Fragmentation Challenge

Developers on server teams face scaling problems. When a website or backend developer writes a line of code they need to ensure that it performs well as traffic grows and lots of users hit that code.

On the other hand developers on mobile teams face fragmentation problems. When a mobile developer writes a line of code they need to ensure that it will run well on thousands of device configurations, including varying device types, connection types, and OS versions.

A recent study by OpenSignal found that there are over 18,000 types of Android devices. How does a mobile developer confirm that their app code doesn’t break across all these devices? There is only one way, monitor production performance using a service that makes it easy to slide and dice the live performance.

3. Network vs. Device Performance

Server teams are primarily concerned with network performance. When the network is slow the bits don’t get downloaded to the thin client, usually a browser, and the end user suffers.

Mobile teams however are concerned with much more than just the network performance; they are dealing with low-end devices running their evolving client code base.

Unique challenges for mobile app developers include:

- How smooth are the interactions (e.g. scrolling)?

- Are apps hitting memory limits on certain devices hurting the user experience?

- Are users on lower end devices waiting an unreasonable amount of time for calculations to finish?

- Is the app draining the battery at an unreasonable rate?

A performance solution for mobile developers needs to be much more comprehensive in the type of metrics captured, and go beyond simply reporting on network issues.

4. Greater Variability of User Experiences

Unlike desktops and laptops, which are high-powered devices often used indoors on reliable networks, mobile devices have more chaotic environments with a wide range of capabilities running on top of unreliable networks.

Since mobile has more variability, performance monitoring solutions need to remove noise from the data to make it usable. For example, the ability to slice and dice the data to view the data that matters, like performance in the US of the latest app version on older but popular handsets.

Mobile performance monitoring solutions should also provide the ability to handle noise introduced by outliers that distort the average performance. This can be addressed by metrics like 95th percentile performance, which are more representative of a slow experience, and 50th percentile performance, to better measure the typical experience.

Finally, noise is created by interrupted app sessions like answering a phone call in the middle of a session. Solutions that detect and handle interruptions present a clearer picture of true performance.

Summary

As users migrate to using mobile apps, businesses face a challenge ensuring the same high quality experiences provided on the Web. In selecting a mobile performance monitoring service to help discover and prioritize outstanding issues, businesses should consider the unique pain points their mobile teams face as outlined above.

ABOUT Ofer Ronen

Ofer Ronen is the Co-founder and CEO of Pulse.io, a performance monitoring service for mobile app developers. The service monitors over 400 monthly sessions for companies of all sizes. It is unique in the level of performance metrics reported, ensuring that issues are not missed. Ronen previously was CEO of Sendori (sold to IAC), a mobile and web ad network. He received a computer engineering MS/BS from Michigan, and MBA from Cornell.

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4 Differences Between Mobile and Server Performance Monitoring

According to eMarketer, as of 2014 Americans consume more media using mobile devices than laptops and desktops combined. This shift in consumer behavior is also occurring within corporations, as employees increasingly rely on mobile devices for their work.

With such a surge in mobile usage there is a growing need for corporations to ensure that their mobile experience is high quality and not broken.

Since 86% of mobile experiences occur within apps and not mobile browsers [source: Flurry], focusing on improving app performance has a larger impact on mobile quality.

The following are 4 key differences that companies monitoring their server (and website) performance should consider when selecting a mobile app performance monitoring solution.

1. Different Team, Different Needs

At most companies, mobile teams are not part of the server/website teams. Instead mobile teams are completely separate and in many cases they are an outsourced team.

The mobile teams have unique pain points when releasing mobile apps that are different from those of web and backend developers (more on this below). Solutions that slightly tweak the interface of a server performance monitoring service do not cut it. These teams require solutions designed from the ground up to solve their problems.

2. Scaling vs. Fragmentation Challenge

Developers on server teams face scaling problems. When a website or backend developer writes a line of code they need to ensure that it performs well as traffic grows and lots of users hit that code.

On the other hand developers on mobile teams face fragmentation problems. When a mobile developer writes a line of code they need to ensure that it will run well on thousands of device configurations, including varying device types, connection types, and OS versions.

A recent study by OpenSignal found that there are over 18,000 types of Android devices. How does a mobile developer confirm that their app code doesn’t break across all these devices? There is only one way, monitor production performance using a service that makes it easy to slide and dice the live performance.

3. Network vs. Device Performance

Server teams are primarily concerned with network performance. When the network is slow the bits don’t get downloaded to the thin client, usually a browser, and the end user suffers.

Mobile teams however are concerned with much more than just the network performance; they are dealing with low-end devices running their evolving client code base.

Unique challenges for mobile app developers include:

- How smooth are the interactions (e.g. scrolling)?

- Are apps hitting memory limits on certain devices hurting the user experience?

- Are users on lower end devices waiting an unreasonable amount of time for calculations to finish?

- Is the app draining the battery at an unreasonable rate?

A performance solution for mobile developers needs to be much more comprehensive in the type of metrics captured, and go beyond simply reporting on network issues.

4. Greater Variability of User Experiences

Unlike desktops and laptops, which are high-powered devices often used indoors on reliable networks, mobile devices have more chaotic environments with a wide range of capabilities running on top of unreliable networks.

Since mobile has more variability, performance monitoring solutions need to remove noise from the data to make it usable. For example, the ability to slice and dice the data to view the data that matters, like performance in the US of the latest app version on older but popular handsets.

Mobile performance monitoring solutions should also provide the ability to handle noise introduced by outliers that distort the average performance. This can be addressed by metrics like 95th percentile performance, which are more representative of a slow experience, and 50th percentile performance, to better measure the typical experience.

Finally, noise is created by interrupted app sessions like answering a phone call in the middle of a session. Solutions that detect and handle interruptions present a clearer picture of true performance.

Summary

As users migrate to using mobile apps, businesses face a challenge ensuring the same high quality experiences provided on the Web. In selecting a mobile performance monitoring service to help discover and prioritize outstanding issues, businesses should consider the unique pain points their mobile teams face as outlined above.

ABOUT Ofer Ronen

Ofer Ronen is the Co-founder and CEO of Pulse.io, a performance monitoring service for mobile app developers. The service monitors over 400 monthly sessions for companies of all sizes. It is unique in the level of performance metrics reported, ensuring that issues are not missed. Ronen previously was CEO of Sendori (sold to IAC), a mobile and web ad network. He received a computer engineering MS/BS from Michigan, and MBA from Cornell.

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As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Beyond Traditional Autoscaling: The Future of Kubernetes in AI Infrastructure

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI Drives Surge in Data Budgets

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned Architecture Causes Service Disruptions, High Operational Costs and Security Challenges

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

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A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

Will AI Solve the Growing Data Divide?

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

Top Concerns for Tech Decision Makers

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

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