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5 Predictions for Application Performance Management in 2016

Srinivas Ramanathan

One can safely say that Application Performance Management (APM) will grow even further in importance in 2016 as businesses turn to application software to operate their key internal and external processes. But we can also expect some changes in the focus of APM purchasers and software vendors in 2016:

1. End User Experience Monitoring

End User Experience Monitoring is important but not necessarily the only thing that APM must focus on and be measured by. Because so many key internal businesses processes are run by software – e.g., day-end reconciliation, backend order fulfilment, chargeback and inventory tracking, etc., – failure or slowdown of these services is business-affecting. So far, end-user response time has been regarded as the defining measure of an online businesses' performance and thus the foundational APM requirement. But the performance of key business processes will ultimately affect user experience, so tracking these processes proactively and detecting issues before users are affected will grow as a primary requirement.

2. Transaction Tracing

Transaction tracing is important for rapid application performance problem diagnosis, but is not sufficient by itself for successful APM. Transaction tracing – i.e., the ability to watch a transaction through all its processing stages and determining which stage is responsible for slowdowns – is a key part of APM, so much so that in the recent years, transaction tracing and APM are virtually synonymous. While transaction tracing is a key for good APM, it is not the only requirement for APM. For example, if there is a slowdown of the backend database, all transactions will highlight slowness for database queries, but this is not very helpful in determining where a problem lies. Automating route cause analysis is outside of the purview of transaction tracing but it is a critical function, so must be addressed either separately, or better, holistically.

3. Deep-Dive Visibility

Transaction visibility must be augmented with deep-dive visibility into every tier of the underlying infrastructure. Troubleshooting performance issues requires extensive expertise about each and every tier of the infrastructure. Enabling performance diagnosis to be accomplished easily and with minimal human intervention requires a great deal of automation. APM tools must augment user experience monitoring and transaction tracing with in-depth insights and domain expertise inside every layer and every tier of the infrastructure. Additionally, these tools should be easy to set up and use, to help remove potential barriers to adoption.

4. Virtualization and Cloud

APM tools must become virtualization and cloud-aware. Virtualization and cloud computing cannot be looked at as yet another infrastructure silo. Performance issues in the virtualization or cloud computing tier affects application performance. Hence, APM tools must discover and correlate virtualization performance with that of the individual application component tiers.

5. Collaborative Management

Organizations will move to collaborative management from silo management. Given the number of tiers that an application cuts across, it will no longer be practical to have individual administrators focus on just the tiers of the infrastructure they operate and control. For the application has to support the business well, the entire application operations team must function as a cohesive unit. Application performance issues will be correlated across the different tiers of the infrastructure so that problems can be resolved quickly. This requires unified and correlated visibility into the entire infrastructure, which APM tools will provide. Development and operations will standardize on the same tool sets so problems detected by operations can be rapidly remediated by the exact development or operations area from which a performance issue is originating.

Srinivas Ramanathan is CEO and Founder of eG Innovations.

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5 Predictions for Application Performance Management in 2016

Srinivas Ramanathan

One can safely say that Application Performance Management (APM) will grow even further in importance in 2016 as businesses turn to application software to operate their key internal and external processes. But we can also expect some changes in the focus of APM purchasers and software vendors in 2016:

1. End User Experience Monitoring

End User Experience Monitoring is important but not necessarily the only thing that APM must focus on and be measured by. Because so many key internal businesses processes are run by software – e.g., day-end reconciliation, backend order fulfilment, chargeback and inventory tracking, etc., – failure or slowdown of these services is business-affecting. So far, end-user response time has been regarded as the defining measure of an online businesses' performance and thus the foundational APM requirement. But the performance of key business processes will ultimately affect user experience, so tracking these processes proactively and detecting issues before users are affected will grow as a primary requirement.

2. Transaction Tracing

Transaction tracing is important for rapid application performance problem diagnosis, but is not sufficient by itself for successful APM. Transaction tracing – i.e., the ability to watch a transaction through all its processing stages and determining which stage is responsible for slowdowns – is a key part of APM, so much so that in the recent years, transaction tracing and APM are virtually synonymous. While transaction tracing is a key for good APM, it is not the only requirement for APM. For example, if there is a slowdown of the backend database, all transactions will highlight slowness for database queries, but this is not very helpful in determining where a problem lies. Automating route cause analysis is outside of the purview of transaction tracing but it is a critical function, so must be addressed either separately, or better, holistically.

3. Deep-Dive Visibility

Transaction visibility must be augmented with deep-dive visibility into every tier of the underlying infrastructure. Troubleshooting performance issues requires extensive expertise about each and every tier of the infrastructure. Enabling performance diagnosis to be accomplished easily and with minimal human intervention requires a great deal of automation. APM tools must augment user experience monitoring and transaction tracing with in-depth insights and domain expertise inside every layer and every tier of the infrastructure. Additionally, these tools should be easy to set up and use, to help remove potential barriers to adoption.

4. Virtualization and Cloud

APM tools must become virtualization and cloud-aware. Virtualization and cloud computing cannot be looked at as yet another infrastructure silo. Performance issues in the virtualization or cloud computing tier affects application performance. Hence, APM tools must discover and correlate virtualization performance with that of the individual application component tiers.

5. Collaborative Management

Organizations will move to collaborative management from silo management. Given the number of tiers that an application cuts across, it will no longer be practical to have individual administrators focus on just the tiers of the infrastructure they operate and control. For the application has to support the business well, the entire application operations team must function as a cohesive unit. Application performance issues will be correlated across the different tiers of the infrastructure so that problems can be resolved quickly. This requires unified and correlated visibility into the entire infrastructure, which APM tools will provide. Development and operations will standardize on the same tool sets so problems detected by operations can be rapidly remediated by the exact development or operations area from which a performance issue is originating.

Srinivas Ramanathan is CEO and Founder of eG Innovations.

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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 ...

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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 ...