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18 Ways to Ensure Mobile App Performance - Part 4

Mobile apps are serious business, and mobile app performance is key. With this in mind, APMdigest asked industry experts – from analysts and consultants to the top vendors – to recommend the best ways to ensure mobile app performance. Part 4 of the list, the final installment, covers more production solutions such as NPM and ITOA.

Start with Part 1 of the List

Start with Part 2 of the List

Start with Part 3 of the List

14-A. NETWORK PERFORMANCE MANAGEMENT (NPM)

You can’t improve what you don’t understand, so it really comes down to metrics. First, establish a quality-of-service baseline and trend data for the entire mobile application stack, from the platform out to the mobile devices. Next, use technology such as network performance management to start monitoring key performance metrics, including: network signal strength, upload throughput, upload latency, upload dropped packets, download throughput, download latency and download dropped packets. Then you must seek to understand how each key performance metric affects the overall experience of using the application so you can quickly detect, alert, pinpoint and troubleshoot a problem in the mobile application stack. For instance, signal strength may cause excessive buffering, while dropped packets may lead to jitter with real-time streaming media.
Michael Thompson
Director, Systems Management Product Marketing, SolarWinds

Monitoring the delivery of content rich, responsive mobile applications is predominantly performed using traditional, enterprise style APM products with the emphasis on application performance, server performance, application dependency mapping, transaction monitoring, using synthetic transactions and monitoring client performance (RUM). Within enterprise environments (where networks have much higher availability, throughput and redundancy than mobile networks) it is now widely recognized that networks are not perfect connectors of applications, servers and clients and that performance and availability problems are often hard to pinpoint as being solely in the application, server or networking domain thus making it difficult to improve performance and to resolve availability issues. When delivering mobile services across dynamic, high latency, low bandwidth mobile networks the need for timely, accurate and complete network management information spanning the full range of networking technologies is essential.
Jeff Roper
CTO, Entuity

14-B. Application-Aware Network Performance Management (AANPM)

Behind every enterprise mobile application is a multi-tier service delivery architecture. The mobile application stack is subject to the same perils faced by traditional web business applications, including network latency and congestion, application chattiness, server resource issues, and database performance. Addressing mobile application delivery issues requires management platforms with broader visibility horizons that include both network and application analytics, which is exactly what Application-Aware Network Performance Management (AANPM) provides. AANPM uses key data points from the network with application-oriented analysis, creating a system with cross-platform visibility and enabling IT to ensure high-performance delivery from the back end of the mobile application stack. AANPM solutions enable IT to quickly locate problems, regardless of their source and solve them as quickly as possible.
Bruce Kosbab
CTO, Fluke Networks

The best way to ensure outstanding mobile performance is to provide high-speed, robust (minimal dropped packets and timeouts) end-to-end network connectivity that includes LTE or high-speed WiFi. The operative words here are "robust" and "end-to-end", which spans from the mobile user to the application server. Assuring subscriber satisfaction means providing application-aware performance intelligence at all points across the infrastructure from the WiFi/LTE access point to the core. Application-performance awareness is important as different applications require varying network performance to deliver acceptable customer experience. For example, the streaming-video experience contrasts with simply downloading a web page. The first requires considerable throughput and is highly latency sensitive while the latter is much more forgiving from a network performance perspective – so long as the page content loads in a relatively quick manner, it's acceptable. But lag and buffering in a streaming video can ruin the whole thing. Augmenting this with backend data center insight including in-depth application payload analysis provides comprehensive service understanding.
Brad Reinboldt
Solution Manager, Network Instruments/JDSU

15. ITOA

Mobile apps are prone to impact from the unintended consequences of change, so the best way to ensure performance is to aggregate as many sources of data as possible – including user experience and usage – and identify abnormal patterns using advanced analytics. By detecting anomalies in datasets, companies are better able to identify problems they wouldn't otherwise have known existed – let alone have been monitoring for.
Mike Paqquette
VP of Security Products, Prelert

16. Reactive Data Layer

Today’s most performant mobile apps are capable of extreme data distribution, speed and scale, coping seamlessly with the huge amounts of heterogeneous data they need to pull from in order to work over the Internet. The best apps can do it without a single hiccup in performance. A reactive data layer is an emerging architecture that developers can leverage to vastly simplify the way an app processes these enormous swathes of data, which are coming from limitless sources in multiple languages. An RDL reduces the data’s complexity by rationalizing it into a single, live data model, allowing for responsive apps that push the envelope of performance.
Sean Bowen
CEO, Push Technology

17. Dynamic Rate Control with Feedback (DRCF)

Mobile application experiences can live or die based on congestion-caused latency and packet loss in the network. With so many congestion spikes occurring in 3G and 4G networks, the user experience is constantly under threat. Our field deployments with mobile operators have proven that the best way to ensure performance is utilizing dynamic rate control with feedback (DRCF) technology to detect data traffic congestion at the cell level and instantly take action that minimizes latency and packet loss.
John Reister
VP of Marketing and Product Management, Vasona Networks

18. MANAGE THE MULTI-SCREEN EXPERIENCE

The top focus of ensuring mobile performance is on optimizing the multi-screen experience. We are only at the beginning of a revolution happening around the mobile device, in many dimensions:
First, wearables, add-ons, fashion gadgets etc. are predicted to proliferate at stunning rates, expanding new user experiences on and around the mobile device. Second, the definition of a mobile device is graying out: users want continuous experience from any screen (/"glass"): their TV, Internet console, mobile device, tablet, iWatch etc. Since the users are sensitive to performance from the device they are using, setting visual KPIs that measure end-to-end response times including backend performance, client side process and rendering, are critical. Naturally, due to the sophisticated, but fragile nature of thick clients running on mobile devices, it is important to measure performance from a variety of screen sizes and hardware restrictions while reproducing real world conditions such as degraded network conditions, resource consumption on the device, access to sensors on the device, unscheduled events (FB message, stock alerts) etc.
Amir Rozenberg
Director of Product Management, Perfecto Mobile

The Latest

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

The gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply ...

Seamless shopping is a basic demand of today's boundaryless consumer — one with little patience for friction, limited tolerance for disconnected experiences and minimal hesitation in switching brands. Customers expect intuitive, highly personalized experiences and the ability to move effortlessly across physical and digital channels within the same journey. Failure to deliver can cost dearly ...

If your best engineers spend their days sorting tickets and resetting access, you are wasting talent. New global data shows that employees in the IT sector rank among the least motivated across industries. They're under a lot of pressure from many angles. Pressure to upskill and uncertainty around what agentic AI means for job security is creating anxiety. Meanwhile, these roles often function like an on-call job and require many repetitive tasks ...

18 Ways to Ensure Mobile App Performance - Part 4

Mobile apps are serious business, and mobile app performance is key. With this in mind, APMdigest asked industry experts – from analysts and consultants to the top vendors – to recommend the best ways to ensure mobile app performance. Part 4 of the list, the final installment, covers more production solutions such as NPM and ITOA.

Start with Part 1 of the List

Start with Part 2 of the List

Start with Part 3 of the List

14-A. NETWORK PERFORMANCE MANAGEMENT (NPM)

You can’t improve what you don’t understand, so it really comes down to metrics. First, establish a quality-of-service baseline and trend data for the entire mobile application stack, from the platform out to the mobile devices. Next, use technology such as network performance management to start monitoring key performance metrics, including: network signal strength, upload throughput, upload latency, upload dropped packets, download throughput, download latency and download dropped packets. Then you must seek to understand how each key performance metric affects the overall experience of using the application so you can quickly detect, alert, pinpoint and troubleshoot a problem in the mobile application stack. For instance, signal strength may cause excessive buffering, while dropped packets may lead to jitter with real-time streaming media.
Michael Thompson
Director, Systems Management Product Marketing, SolarWinds

Monitoring the delivery of content rich, responsive mobile applications is predominantly performed using traditional, enterprise style APM products with the emphasis on application performance, server performance, application dependency mapping, transaction monitoring, using synthetic transactions and monitoring client performance (RUM). Within enterprise environments (where networks have much higher availability, throughput and redundancy than mobile networks) it is now widely recognized that networks are not perfect connectors of applications, servers and clients and that performance and availability problems are often hard to pinpoint as being solely in the application, server or networking domain thus making it difficult to improve performance and to resolve availability issues. When delivering mobile services across dynamic, high latency, low bandwidth mobile networks the need for timely, accurate and complete network management information spanning the full range of networking technologies is essential.
Jeff Roper
CTO, Entuity

14-B. Application-Aware Network Performance Management (AANPM)

Behind every enterprise mobile application is a multi-tier service delivery architecture. The mobile application stack is subject to the same perils faced by traditional web business applications, including network latency and congestion, application chattiness, server resource issues, and database performance. Addressing mobile application delivery issues requires management platforms with broader visibility horizons that include both network and application analytics, which is exactly what Application-Aware Network Performance Management (AANPM) provides. AANPM uses key data points from the network with application-oriented analysis, creating a system with cross-platform visibility and enabling IT to ensure high-performance delivery from the back end of the mobile application stack. AANPM solutions enable IT to quickly locate problems, regardless of their source and solve them as quickly as possible.
Bruce Kosbab
CTO, Fluke Networks

The best way to ensure outstanding mobile performance is to provide high-speed, robust (minimal dropped packets and timeouts) end-to-end network connectivity that includes LTE or high-speed WiFi. The operative words here are "robust" and "end-to-end", which spans from the mobile user to the application server. Assuring subscriber satisfaction means providing application-aware performance intelligence at all points across the infrastructure from the WiFi/LTE access point to the core. Application-performance awareness is important as different applications require varying network performance to deliver acceptable customer experience. For example, the streaming-video experience contrasts with simply downloading a web page. The first requires considerable throughput and is highly latency sensitive while the latter is much more forgiving from a network performance perspective – so long as the page content loads in a relatively quick manner, it's acceptable. But lag and buffering in a streaming video can ruin the whole thing. Augmenting this with backend data center insight including in-depth application payload analysis provides comprehensive service understanding.
Brad Reinboldt
Solution Manager, Network Instruments/JDSU

15. ITOA

Mobile apps are prone to impact from the unintended consequences of change, so the best way to ensure performance is to aggregate as many sources of data as possible – including user experience and usage – and identify abnormal patterns using advanced analytics. By detecting anomalies in datasets, companies are better able to identify problems they wouldn't otherwise have known existed – let alone have been monitoring for.
Mike Paqquette
VP of Security Products, Prelert

16. Reactive Data Layer

Today’s most performant mobile apps are capable of extreme data distribution, speed and scale, coping seamlessly with the huge amounts of heterogeneous data they need to pull from in order to work over the Internet. The best apps can do it without a single hiccup in performance. A reactive data layer is an emerging architecture that developers can leverage to vastly simplify the way an app processes these enormous swathes of data, which are coming from limitless sources in multiple languages. An RDL reduces the data’s complexity by rationalizing it into a single, live data model, allowing for responsive apps that push the envelope of performance.
Sean Bowen
CEO, Push Technology

17. Dynamic Rate Control with Feedback (DRCF)

Mobile application experiences can live or die based on congestion-caused latency and packet loss in the network. With so many congestion spikes occurring in 3G and 4G networks, the user experience is constantly under threat. Our field deployments with mobile operators have proven that the best way to ensure performance is utilizing dynamic rate control with feedback (DRCF) technology to detect data traffic congestion at the cell level and instantly take action that minimizes latency and packet loss.
John Reister
VP of Marketing and Product Management, Vasona Networks

18. MANAGE THE MULTI-SCREEN EXPERIENCE

The top focus of ensuring mobile performance is on optimizing the multi-screen experience. We are only at the beginning of a revolution happening around the mobile device, in many dimensions:
First, wearables, add-ons, fashion gadgets etc. are predicted to proliferate at stunning rates, expanding new user experiences on and around the mobile device. Second, the definition of a mobile device is graying out: users want continuous experience from any screen (/"glass"): their TV, Internet console, mobile device, tablet, iWatch etc. Since the users are sensitive to performance from the device they are using, setting visual KPIs that measure end-to-end response times including backend performance, client side process and rendering, are critical. Naturally, due to the sophisticated, but fragile nature of thick clients running on mobile devices, it is important to measure performance from a variety of screen sizes and hardware restrictions while reproducing real world conditions such as degraded network conditions, resource consumption on the device, access to sensors on the device, unscheduled events (FB message, stock alerts) etc.
Amir Rozenberg
Director of Product Management, Perfecto Mobile

The Latest

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

The gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply ...

Seamless shopping is a basic demand of today's boundaryless consumer — one with little patience for friction, limited tolerance for disconnected experiences and minimal hesitation in switching brands. Customers expect intuitive, highly personalized experiences and the ability to move effortlessly across physical and digital channels within the same journey. Failure to deliver can cost dearly ...

If your best engineers spend their days sorting tickets and resetting access, you are wasting talent. New global data shows that employees in the IT sector rank among the least motivated across industries. They're under a lot of pressure from many angles. Pressure to upskill and uncertainty around what agentic AI means for job security is creating anxiety. Meanwhile, these roles often function like an on-call job and require many repetitive tasks ...