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AppDynamics Summer '16 Release Features New Microservices iQ

AppDynamics announced its Summer '16 release, which includes the new Microservices iQ, built for the enterprise to accelerate digital transformation powered by microservices.

Microservices iQ, a new Performance Engine in the App iQ Platform, will help enable enterprises to deliver application performances that exceed the scale, sophistication and velocity expectations of today’s customers.

“Every enterprise is in the midst of a digital transformation and looking for a faster and smarter way to manage their evolution,” said David Wadhwani, President and CEO, AppDynamics. “The world’s largest companies are eager to embrace microservices but are waiting for a scalable way of managing the potential impact on their IT infrastructures. With AppDynamics, they have the proven platform to accelerate their digital transformation and help future-proof their microservices investments.”

With the AppDynamics Summer '16 release, AppDynamics will deliver Microservices iQ with capabilities for the monitoring, management and optimization of microservices architectures. Enterprises are breaking up their large, rigid, monolithic applications into smaller, more manageable pieces with microservices. Microservices require many more application server instances to run the smaller pieces, creating a significantly larger footprint of application instances.

Until Microservices iQ, companies didn't have the means to monitor, manage or bolster their performance in enterprise production environments. Now, they can accelerate their microservices deployments and gain control of their performance with Microservices iQ capabilities, including:

- Service Endpoints — Microservices iQ automatically detects the service endpoints of a microservices architecture, enabling companies to shine a spotlight on a particular microservice without worrying about potentially multiple business transactions that use it. DevOps teams can then monitor that microservice’s key performance indicators (KPIs) like calls per minute, average response time, and errors per minute from early in the development lifecycle through production. Snapshots with detailed diagnostics enable DevOps teams to drill down and isolate the root cause of any performance issues affecting any microservices. Microservices are developed in isolation, and then used by one or more business transactions. Now DevOps teams can focus on their areas of responsibility, making them more efficient, while AppDynamics helps ensure the broader business is performing.

- Elasticity Management — In highly dynamic environments, with microservices deployed in containers or the cloud, underlying infrastructure nodes can scale up and down rapidly, creating a significant operational challenge to track their performance. Microservices iQ uses a logical identity system to solve this and maintains historical data under these identities, making it easy and efficient to keep track of them continuously. Companies can now focus on the health of their microservices overall, without being distracted by fluctuations of hosts or containers starting and stopping.

- Contention Analysis — Microservices often must scale to support thousands of simultaneous requests, which raises the likelihood of bottlenecks as requests try to access the same data. When used within the scope of service endpoints the new thread contention analyzer helps identify methods where threads are blocked–identifying block time, blocking object and the blocking line of code. Thus significantly minimizing the time required to isolate and resolve application performance issues with microservices and the business transactions invoking them. Now companies can scale their microservices with confidence that the potentially thousands of simultaneous requests will succeed.

Microservices iQ extends AppDynamics’ existing App iQ Platform that enables enterprises to deliver performance that exceeds the scale, sophistication and velocity expectations of today’s customers. The platform is the foundation to AppDynamics’ customers’ success and powered by intelligent Performance Engines. These intelligent performance engines work in concert to help ensure enterprises can deliver peak performance across any application, user engagement and business transaction.

“Every company wants to become the disruptor in their industry, but doesn’t have the means to overcome the complexities presented by digital transformation,” said Donnie Berkholz, Research Director of Development, DevOps and IT Op, 451 Research. “Microservices are the future of how enterprises swiftly make the change and AppDynamics has the smart approach to managing performance during the transition with its platform.”

AppDynamics Summer ’16 is expected to be generally available in August 2016.

Microservices iQ Performance Engine will be included in licenses of AppDynamics’ Application Performance Management solution for no additional cost.

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AppDynamics Summer '16 Release Features New Microservices iQ

AppDynamics announced its Summer '16 release, which includes the new Microservices iQ, built for the enterprise to accelerate digital transformation powered by microservices.

Microservices iQ, a new Performance Engine in the App iQ Platform, will help enable enterprises to deliver application performances that exceed the scale, sophistication and velocity expectations of today’s customers.

“Every enterprise is in the midst of a digital transformation and looking for a faster and smarter way to manage their evolution,” said David Wadhwani, President and CEO, AppDynamics. “The world’s largest companies are eager to embrace microservices but are waiting for a scalable way of managing the potential impact on their IT infrastructures. With AppDynamics, they have the proven platform to accelerate their digital transformation and help future-proof their microservices investments.”

With the AppDynamics Summer '16 release, AppDynamics will deliver Microservices iQ with capabilities for the monitoring, management and optimization of microservices architectures. Enterprises are breaking up their large, rigid, monolithic applications into smaller, more manageable pieces with microservices. Microservices require many more application server instances to run the smaller pieces, creating a significantly larger footprint of application instances.

Until Microservices iQ, companies didn't have the means to monitor, manage or bolster their performance in enterprise production environments. Now, they can accelerate their microservices deployments and gain control of their performance with Microservices iQ capabilities, including:

- Service Endpoints — Microservices iQ automatically detects the service endpoints of a microservices architecture, enabling companies to shine a spotlight on a particular microservice without worrying about potentially multiple business transactions that use it. DevOps teams can then monitor that microservice’s key performance indicators (KPIs) like calls per minute, average response time, and errors per minute from early in the development lifecycle through production. Snapshots with detailed diagnostics enable DevOps teams to drill down and isolate the root cause of any performance issues affecting any microservices. Microservices are developed in isolation, and then used by one or more business transactions. Now DevOps teams can focus on their areas of responsibility, making them more efficient, while AppDynamics helps ensure the broader business is performing.

- Elasticity Management — In highly dynamic environments, with microservices deployed in containers or the cloud, underlying infrastructure nodes can scale up and down rapidly, creating a significant operational challenge to track their performance. Microservices iQ uses a logical identity system to solve this and maintains historical data under these identities, making it easy and efficient to keep track of them continuously. Companies can now focus on the health of their microservices overall, without being distracted by fluctuations of hosts or containers starting and stopping.

- Contention Analysis — Microservices often must scale to support thousands of simultaneous requests, which raises the likelihood of bottlenecks as requests try to access the same data. When used within the scope of service endpoints the new thread contention analyzer helps identify methods where threads are blocked–identifying block time, blocking object and the blocking line of code. Thus significantly minimizing the time required to isolate and resolve application performance issues with microservices and the business transactions invoking them. Now companies can scale their microservices with confidence that the potentially thousands of simultaneous requests will succeed.

Microservices iQ extends AppDynamics’ existing App iQ Platform that enables enterprises to deliver performance that exceeds the scale, sophistication and velocity expectations of today’s customers. The platform is the foundation to AppDynamics’ customers’ success and powered by intelligent Performance Engines. These intelligent performance engines work in concert to help ensure enterprises can deliver peak performance across any application, user engagement and business transaction.

“Every company wants to become the disruptor in their industry, but doesn’t have the means to overcome the complexities presented by digital transformation,” said Donnie Berkholz, Research Director of Development, DevOps and IT Op, 451 Research. “Microservices are the future of how enterprises swiftly make the change and AppDynamics has the smart approach to managing performance during the transition with its platform.”

AppDynamics Summer ’16 is expected to be generally available in August 2016.

Microservices iQ Performance Engine will be included in licenses of AppDynamics’ Application Performance Management solution for no additional cost.

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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 ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.