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IoT Ultimate Test for APM

Monitoring Infinite Microservices Demands New Approach to APM
Jeffrey Kaplan

Last December, my APMdigest prediction for 2015 was,

"The advent of the “Internet of Things” (IoT) will elevate the importance of implementing powerful, easy-to-use and cost-effective APM (Application Performance Management) solutions as a rapidly expanding universe of end-points are connected by software-enabled sensors and systems. The new generation of APM solutions will have to contend with an exponentially greater number of connections, transactions and data points. The APM solutions will also have to span Cloud and on-premise applications which will be linked together in the IoT environment. The task of implementing and administering the APM solutions will increasingly be performed by highly specialized, third-party service providers."

Less than halfway through the new year and we're seeing the market activity around IoT opportunities accelerate. Not only are the number of 'things' currently and expected to be connected growing exponentially, but the types of business processes being impacted by IoT deployments is also expanding rapidly. Because of all our connected devices, there are already far more 'things' communicating via the web than there are people. And, Cisco Systems expects the number of Internet-connected things will reach 50 billion by 2020, and these connected products and services could generate $19 trillion in profits and cost savings over the next decade.

While there are plenty of industry forecasts projecting hyper-growth of the IoT market, Cisco's forecasts are sufficient to clearly show that APM players and users are going to be severely tested by this unprecedented surge in connected things. More important than the extraordinary number of things being connected are the growing number and widening assortment of business applications which will be impacted. This combination significantly escalates the scale and complexity of the APM challenge.

With the explosive growth of "wearables", software-enabled sensors are being used by people for fitness, fashion and health-related reasons that all demand reliable application performance. They are also being deployed in an infinite array of 'things', including cattle and crops in addition to every imaginable consumer and commercial product, service and location.

Rather than monitor relatively stable enterprise applications or typical web applications to support specific business functions, a common IoT deployment might entail monitoring many inter-related applications which impact a series of business processes.

For instance, a sensor on a remote device will have software commands to monitor and report activity that could be transmitted to a service management system and trigger a service call. The alert could initiate a request for replacement material that is controlled by an inventory management system and lead to the dispatch of a service agent guided by a logistics system. It could also be imported into a CRM, ERP or other enterprise app to ensure sales, finance and other departments are aware of the customer status. The information could also be used to redesign the product and services to make them more reliable and improve customer satisfaction or corporate efficiency.

Measuring application performance across the IoT supply-chain of multivendor software elements is the new APM challenge. And, reporting the APM data to a broader set of interested parties - executives, end-users, customers and partners - will compound the challenge.

Jeffrey Kaplan is the Managing Director of THINKstrategies and Founder of the Cloud Computing Showplace.

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IoT Ultimate Test for APM

Monitoring Infinite Microservices Demands New Approach to APM
Jeffrey Kaplan

Last December, my APMdigest prediction for 2015 was,

"The advent of the “Internet of Things” (IoT) will elevate the importance of implementing powerful, easy-to-use and cost-effective APM (Application Performance Management) solutions as a rapidly expanding universe of end-points are connected by software-enabled sensors and systems. The new generation of APM solutions will have to contend with an exponentially greater number of connections, transactions and data points. The APM solutions will also have to span Cloud and on-premise applications which will be linked together in the IoT environment. The task of implementing and administering the APM solutions will increasingly be performed by highly specialized, third-party service providers."

Less than halfway through the new year and we're seeing the market activity around IoT opportunities accelerate. Not only are the number of 'things' currently and expected to be connected growing exponentially, but the types of business processes being impacted by IoT deployments is also expanding rapidly. Because of all our connected devices, there are already far more 'things' communicating via the web than there are people. And, Cisco Systems expects the number of Internet-connected things will reach 50 billion by 2020, and these connected products and services could generate $19 trillion in profits and cost savings over the next decade.

While there are plenty of industry forecasts projecting hyper-growth of the IoT market, Cisco's forecasts are sufficient to clearly show that APM players and users are going to be severely tested by this unprecedented surge in connected things. More important than the extraordinary number of things being connected are the growing number and widening assortment of business applications which will be impacted. This combination significantly escalates the scale and complexity of the APM challenge.

With the explosive growth of "wearables", software-enabled sensors are being used by people for fitness, fashion and health-related reasons that all demand reliable application performance. They are also being deployed in an infinite array of 'things', including cattle and crops in addition to every imaginable consumer and commercial product, service and location.

Rather than monitor relatively stable enterprise applications or typical web applications to support specific business functions, a common IoT deployment might entail monitoring many inter-related applications which impact a series of business processes.

For instance, a sensor on a remote device will have software commands to monitor and report activity that could be transmitted to a service management system and trigger a service call. The alert could initiate a request for replacement material that is controlled by an inventory management system and lead to the dispatch of a service agent guided by a logistics system. It could also be imported into a CRM, ERP or other enterprise app to ensure sales, finance and other departments are aware of the customer status. The information could also be used to redesign the product and services to make them more reliable and improve customer satisfaction or corporate efficiency.

Measuring application performance across the IoT supply-chain of multivendor software elements is the new APM challenge. And, reporting the APM data to a broader set of interested parties - executives, end-users, customers and partners - will compound the challenge.

Jeffrey Kaplan is the Managing Director of THINKstrategies and Founder of the Cloud Computing Showplace.

Hot Topics

The Latest

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...

Technology leaders across the federal landscape are facing, and will continue to face, an uphill battle when it comes to fortifying their digital environments against hostile and persistent threat actors. On one hand, they are being asked to push digital transformation ... On the other hand, they are facing the fiscal uncertainty of continuing resolutions (CR) and government shutdowns looming near and far. In the face of these challenges, CIOs, CTOs, and CISOs must figure out how to modernize legacy systems and infrastructure while doing more with less and still defending against external and internal threats ...

Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ...