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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

AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...

Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...