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Automating Application Performance Monitoring

Keith Bromley

Application performance monitoring (APM) is becoming more complex as the days go by. Server virtualization and cloud-based systems with containers and orchestration layers are part of this growing complexity, especially as the number of data sources increases and continues to change dynamically.

To keep up with this changing environment, you will need to automate as many of your systems as possible. Open APIs can be an effective way to combat this scenario.

The use of open APIs applies to the monitoring data capture process as well. Your APM tools need good data to make good conclusions. This has never changed and never will. A good choice to address this issue includes using a Representational State Transfer (REST) based interface to a network packet broker (NPB). The NPB is useful in the data capture process as it can aggregate data from multiple sources, filter that data on Layer 2 through 4 criteria and/or Layer 7 criteria, and then distribute that specific subset of data to the APM tool for analysis and the creation of actionable insights.

Two common use cases for the automation of the monitoring data capture process include the following:

■ Event triggers

■ Orchestration systems

Event triggered responses are fairly straight forward. Once an event is spotted by a security information and event management (SIEM) or the APM tool, instructions can be sent to the NPB to collect specific types of data (based upon IP address or other criteria) and then send that data to the APM tool for analysis.

In a different use case, orchestration and management systems can be used to support a zero-touch provisioning process. In the case of the NPB, built-in features like a RESTful interface allow for the use of automated provisioning systems, which reduces start-to-finish programming times to five minutes or less.

Besides the initial programming and provisioning, this solution can also be adapted to implement a continuous self-configuration system for the NPB and the monitoring data capture process, taking advantage of the flexibility of virtualized tools and cloud-based security analytics tools for monitoring. As the network (and data sources) changes, the NPB can be reconfigured automatically to collect the right data. Your APM system can then continue to perform its central function of analyzing data.

If you need a way to keep up with a dynamically changing environment, Open APIs could be a good answer.

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Automating Application Performance Monitoring

Keith Bromley

Application performance monitoring (APM) is becoming more complex as the days go by. Server virtualization and cloud-based systems with containers and orchestration layers are part of this growing complexity, especially as the number of data sources increases and continues to change dynamically.

To keep up with this changing environment, you will need to automate as many of your systems as possible. Open APIs can be an effective way to combat this scenario.

The use of open APIs applies to the monitoring data capture process as well. Your APM tools need good data to make good conclusions. This has never changed and never will. A good choice to address this issue includes using a Representational State Transfer (REST) based interface to a network packet broker (NPB). The NPB is useful in the data capture process as it can aggregate data from multiple sources, filter that data on Layer 2 through 4 criteria and/or Layer 7 criteria, and then distribute that specific subset of data to the APM tool for analysis and the creation of actionable insights.

Two common use cases for the automation of the monitoring data capture process include the following:

■ Event triggers

■ Orchestration systems

Event triggered responses are fairly straight forward. Once an event is spotted by a security information and event management (SIEM) or the APM tool, instructions can be sent to the NPB to collect specific types of data (based upon IP address or other criteria) and then send that data to the APM tool for analysis.

In a different use case, orchestration and management systems can be used to support a zero-touch provisioning process. In the case of the NPB, built-in features like a RESTful interface allow for the use of automated provisioning systems, which reduces start-to-finish programming times to five minutes or less.

Besides the initial programming and provisioning, this solution can also be adapted to implement a continuous self-configuration system for the NPB and the monitoring data capture process, taking advantage of the flexibility of virtualized tools and cloud-based security analytics tools for monitoring. As the network (and data sources) changes, the NPB can be reconfigured automatically to collect the right data. Your APM system can then continue to perform its central function of analyzing data.

If you need a way to keep up with a dynamically changing environment, Open APIs could be a good answer.

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As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

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A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

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