Skip to main content

Top Recommendations to Ensure Performance for the IoT - Part 1

Gartner says: "By 2020, 21 billion of Internet of Things (IoT) devices will be in use worldwide."

"IoT is a growing concept in terms of exposure and implementation," explained John Myers, Managing Research Director for Business Intelligence at Enterprise Management Associates (EMA), in The Rise of IoT. "There are new estimates for the number of linked devices almost every quarter. Some of these estimates go as far as to say that within five years, there will be nearly 40 billion connected devices around the globe."

"Within the next few years billions of smart devices will be communicating and sharing important data on just about everything – healthcare, manufacturing, financial services, food processing, environmental science, lifestyles, and more," added Ron Lifton, Senior Enterprise Solutions Marketing Manager, NetScout.

The IoT is in position to become one of the greatest application performance management challenges faced by IT. The potential number of connected devices, the massive amount of data these devices will generate, and the growing complexity of the infrastructure all compound this challenge.

"As more business and industrial applications are created, more devices are being connected, forcing IT systems to handle greater volumes of data," confirms Ross Garrett, Director Product Marketing at Push Technology in a recent blog on APMdigest. "And more importantly, these connected systems don't have the same tolerance or understanding for tardiness their human counterparts do. Performance – no matter the number of connections, volume of data, distance to travel, or network capability – is critical, and that's the dilemma facing many enterprise architects and systems integrators."

With this challenge in mind, APMdigest asked experts across the industry – including analysts, consultants and vendors – for their recommendations on how to ensure performance for IoT applications. APMdigest will post the in-depth list of expert recommendations over the next four days. Part 1 covers visibility and Application Performance Management.

1. COMPREHENSIVE VISIBILITY

In terms of application performance for the IoT, we recommend that organizations focus their IT efforts on the visibility of all connected devices and the elements with which they are interacting in the IT network. Without full visibility of the entire network, it is impossible to understand interdependencies and impact on performance, and therefore not feasible to meet this new challenge.
Zvika Meiseles
CTO, Correlsense

The sheer surface area of an IoT infrastructure means that the lines blur between security of the infrastructure and performance of IoT applications. The common denominator that ensures maximum security of the IoT infrastructure and performance of the IoT applications is visibility into any data-in-motion between the different elements involved in an IoT deployment.
Ananda Rajagopal
VP, Product Management, Gigamon

2. UNDERSTAND DEPENDENCIES

The key to assuring performance is by understanding all application and service dependencies across the IoT infrastructure so when a problem occurs, it can quickly be identified. IoT performance is dependent on gaining unrestricted operational visibility to identify potential problems from the edge into the cloud and data centers. If the IT organization achieves that, then they can confidently navigate through IoT changes and help reduce business risk.
Ron Lifton
Senior Enterprise Solutions Manager, NetScout

3. MONITOR CONNECTION POINTS BETWEEN TECHNOLOGIES

IoT is about data collection for multiple use cases, but most IoT solutions rely on existing and legacy components as well whether it be IT or OT. Having consistent data collection across technologies is a challenge. Showing the interconnection points between technologies and how each is performing is key in order to isolate performance issues.
Jonah Kowall
VP of Market Development and Insights, AppDynamics

4. APPLICATION PERFORMANCE MANAGEMENT (APM)

Since IoT started gaining traction among enterprises, IT teams have had to deal with an additional layer of complexity on top of the existing management challenges. As enterprises bring more connected devices online, IT has to deal with a large number of devices as well as the massive amounts of data that stream into their big data stores. Ensuring performance of IoT applications can be a cumbersome manual process, one that leaves performance blind spots and gaps in visibility. To address this performance challenge, enterprise IT teams should implement proactive application performance monitoring to gain end-to-end visibility into their distributed applications and the underlying infrastructure. They should understand the dependencies between the different application components and the transactions that flow through it. With the help of performance data collected, IT teams can quickly identify the root cause of application performance bottlenecks, and fix them before users are affected.
Arun Balachandran
Applications Manager Market Analyst, ManageEngine

Traditionally, APM solutions have been very adept at identifying approaching thresholds and bottlenecks in other critical systems. Similarly, ensuring strong performance for IoT depends on the ability to automatically detect and pre-empt performance issues in the systems and applications supporting IoT.
Mehdi Daoudi
CEO and Founder, Catchpoint

5. NEXT-GEN APM

Every new IoT device that connects to the Internet at the frontend, will have an impact on the network and also the hardware at the backend. Therefore, it is essential for Application Performance Management solutions to keep up! This will be extremely critical because its still early in the development and innovation stages of IoT. Who can predict the expansion, connectivity, layers and technology for IoT over in the next 3 - 5 years? Therein lies the challenge for APM!
Hayden James
Linux Systems Analyst, haydenjames.io

6. SEAMLESS INTEGRATION OF KEY COMPONENTS

From looking at comments and conversations on IT Central Station, I've noticed that IT professionals have broken down the key components of IoT into 5 components: the UI/UX Layer, Data Processing or Analytics, Connectivity, Sensors Layer, and the Embedded Systems processor. In order to ensure performance for the IoT, it is critical that each of these components work together harmoniously. Users seem particularly interested in the ability to automate each of these key components, but the critical point is that they seamlessly work together.
Russell Rothstein
Founder and CEO, IT Central Station

Read Top Recommendations to Ensure Performance for the IoT - Part 2, covering data and analytics.

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 ...

Top Recommendations to Ensure Performance for the IoT - Part 1

Gartner says: "By 2020, 21 billion of Internet of Things (IoT) devices will be in use worldwide."

"IoT is a growing concept in terms of exposure and implementation," explained John Myers, Managing Research Director for Business Intelligence at Enterprise Management Associates (EMA), in The Rise of IoT. "There are new estimates for the number of linked devices almost every quarter. Some of these estimates go as far as to say that within five years, there will be nearly 40 billion connected devices around the globe."

"Within the next few years billions of smart devices will be communicating and sharing important data on just about everything – healthcare, manufacturing, financial services, food processing, environmental science, lifestyles, and more," added Ron Lifton, Senior Enterprise Solutions Marketing Manager, NetScout.

The IoT is in position to become one of the greatest application performance management challenges faced by IT. The potential number of connected devices, the massive amount of data these devices will generate, and the growing complexity of the infrastructure all compound this challenge.

"As more business and industrial applications are created, more devices are being connected, forcing IT systems to handle greater volumes of data," confirms Ross Garrett, Director Product Marketing at Push Technology in a recent blog on APMdigest. "And more importantly, these connected systems don't have the same tolerance or understanding for tardiness their human counterparts do. Performance – no matter the number of connections, volume of data, distance to travel, or network capability – is critical, and that's the dilemma facing many enterprise architects and systems integrators."

With this challenge in mind, APMdigest asked experts across the industry – including analysts, consultants and vendors – for their recommendations on how to ensure performance for IoT applications. APMdigest will post the in-depth list of expert recommendations over the next four days. Part 1 covers visibility and Application Performance Management.

1. COMPREHENSIVE VISIBILITY

In terms of application performance for the IoT, we recommend that organizations focus their IT efforts on the visibility of all connected devices and the elements with which they are interacting in the IT network. Without full visibility of the entire network, it is impossible to understand interdependencies and impact on performance, and therefore not feasible to meet this new challenge.
Zvika Meiseles
CTO, Correlsense

The sheer surface area of an IoT infrastructure means that the lines blur between security of the infrastructure and performance of IoT applications. The common denominator that ensures maximum security of the IoT infrastructure and performance of the IoT applications is visibility into any data-in-motion between the different elements involved in an IoT deployment.
Ananda Rajagopal
VP, Product Management, Gigamon

2. UNDERSTAND DEPENDENCIES

The key to assuring performance is by understanding all application and service dependencies across the IoT infrastructure so when a problem occurs, it can quickly be identified. IoT performance is dependent on gaining unrestricted operational visibility to identify potential problems from the edge into the cloud and data centers. If the IT organization achieves that, then they can confidently navigate through IoT changes and help reduce business risk.
Ron Lifton
Senior Enterprise Solutions Manager, NetScout

3. MONITOR CONNECTION POINTS BETWEEN TECHNOLOGIES

IoT is about data collection for multiple use cases, but most IoT solutions rely on existing and legacy components as well whether it be IT or OT. Having consistent data collection across technologies is a challenge. Showing the interconnection points between technologies and how each is performing is key in order to isolate performance issues.
Jonah Kowall
VP of Market Development and Insights, AppDynamics

4. APPLICATION PERFORMANCE MANAGEMENT (APM)

Since IoT started gaining traction among enterprises, IT teams have had to deal with an additional layer of complexity on top of the existing management challenges. As enterprises bring more connected devices online, IT has to deal with a large number of devices as well as the massive amounts of data that stream into their big data stores. Ensuring performance of IoT applications can be a cumbersome manual process, one that leaves performance blind spots and gaps in visibility. To address this performance challenge, enterprise IT teams should implement proactive application performance monitoring to gain end-to-end visibility into their distributed applications and the underlying infrastructure. They should understand the dependencies between the different application components and the transactions that flow through it. With the help of performance data collected, IT teams can quickly identify the root cause of application performance bottlenecks, and fix them before users are affected.
Arun Balachandran
Applications Manager Market Analyst, ManageEngine

Traditionally, APM solutions have been very adept at identifying approaching thresholds and bottlenecks in other critical systems. Similarly, ensuring strong performance for IoT depends on the ability to automatically detect and pre-empt performance issues in the systems and applications supporting IoT.
Mehdi Daoudi
CEO and Founder, Catchpoint

5. NEXT-GEN APM

Every new IoT device that connects to the Internet at the frontend, will have an impact on the network and also the hardware at the backend. Therefore, it is essential for Application Performance Management solutions to keep up! This will be extremely critical because its still early in the development and innovation stages of IoT. Who can predict the expansion, connectivity, layers and technology for IoT over in the next 3 - 5 years? Therein lies the challenge for APM!
Hayden James
Linux Systems Analyst, haydenjames.io

6. SEAMLESS INTEGRATION OF KEY COMPONENTS

From looking at comments and conversations on IT Central Station, I've noticed that IT professionals have broken down the key components of IoT into 5 components: the UI/UX Layer, Data Processing or Analytics, Connectivity, Sensors Layer, and the Embedded Systems processor. In order to ensure performance for the IoT, it is critical that each of these components work together harmoniously. Users seem particularly interested in the ability to automate each of these key components, but the critical point is that they seamlessly work together.
Russell Rothstein
Founder and CEO, IT Central Station

Read Top Recommendations to Ensure Performance for the IoT - Part 2, covering data and analytics.

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 ...