Skip to main content

2016 APM Predictions: Single Pane of Glass

Bogdan Viher

As the IT world increasingly moves toward “single pane of glass” management consoles, customers are looking to understand:
 
■ How can these platforms enable true end-to-end monitoring and guaranteed infrastructure performance?

■ How will these platforms deal with the rise of cloud-based computing?
 
In 2016, consolidation of monitoring tools across five major platforms now accounts for more than 80 percent of the application monitoring market. In 2016, that percentage will continue to rise as environments become increasingly complex and the desire for "single pane of glass" monitoring increases, pushing IT departments to combine APM and holistic infrastructure monitoring.
 
In the coming year, the need for APM probes that are able to analyze both public and private cloud-based applications will become increasingly important as cloud computing continues its rise in popularity. This increased migration to the cloud will stem from the continued performance depreciation of legacy systems. For companies that continue to use existing infrastructure, there will be a need to supplement systems with native plugins and APM probes to mitigate latency and proactively identify potential problems across the network. 
 
Standalone APM tools that work only for a specific product or device will become increasingly less relevant as the industry continues to converge and consolidate and replaces these tools with native plug-in solutions. This effort will be driven by the need to lower administrative costs whilst increasing manageability in 2016.

Bogdan Viher is Product Director at Comtrade System Software and Tools.

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 ...

2016 APM Predictions: Single Pane of Glass

Bogdan Viher

As the IT world increasingly moves toward “single pane of glass” management consoles, customers are looking to understand:
 
■ How can these platforms enable true end-to-end monitoring and guaranteed infrastructure performance?

■ How will these platforms deal with the rise of cloud-based computing?
 
In 2016, consolidation of monitoring tools across five major platforms now accounts for more than 80 percent of the application monitoring market. In 2016, that percentage will continue to rise as environments become increasingly complex and the desire for "single pane of glass" monitoring increases, pushing IT departments to combine APM and holistic infrastructure monitoring.
 
In the coming year, the need for APM probes that are able to analyze both public and private cloud-based applications will become increasingly important as cloud computing continues its rise in popularity. This increased migration to the cloud will stem from the continued performance depreciation of legacy systems. For companies that continue to use existing infrastructure, there will be a need to supplement systems with native plugins and APM probes to mitigate latency and proactively identify potential problems across the network. 
 
Standalone APM tools that work only for a specific product or device will become increasingly less relevant as the industry continues to converge and consolidate and replaces these tools with native plug-in solutions. This effort will be driven by the need to lower administrative costs whilst increasing manageability in 2016.

Bogdan Viher is Product Director at Comtrade System Software and Tools.

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 ...