Welcome!

Apache Authors: Pat Romanski, Liz McMillan, Elizabeth White, Christopher Harrold, Janakiram MSV

Related Topics: @DevOpsSummit, Microservices Expo, Linux Containers, Containers Expo Blog, @CloudExpo, Apache

@DevOpsSummit: Blog Feed Post

Monitoring for #Microservices By @AppDynamics | @DevOpsSummit #DevOps #Docker #IoT

New Pricing Model Introduced

AppDynamics Monitoring Excels for Microservices; New Pricing Model Introduced

It’s no news that microservices are one of the top trends, if not the top trend, in application architectures today. Take large monolithic applications which are brittle and difficult to change and break them into smaller manageable pieces to provide flexibility in deployment models, facilitating agile release and development to meet today’s rapidly shifting digital businesses. Unfortunately, with this change, application and infrastructure management is more complex due to size and technology changes, most often adding significantly more virtual machines and/or containers to handle the growing footprint of application instances.

Fortunately, this is just the kind of environment the AppDynamics Application Intelligence Platform is built for, delivering deep visibility across even the most complex, distributed, heterogeneous environments. We trace and monitor every business transaction from end-to-end — no matter how far apart those ends are, or how circuitous the path between — including any and all API calls across any and all microservices tiers. Wherever there is an issue, the AppDynamics platform pinpoints it and steers the way to rapid resolution. This data can also be used to analyze usage patterns, scaling requirements, and even visibility into infrastructure usage.

This is just the beginning of the microservices trend. With the rise of the Internet of Things, all manner of devices and services will be driven by microservices. The applications themselves will be extended into the “Things” causing even further exponential growth over the next five years. Gartner predicts over 25 billion devices connected by 2020, with the majority being in the utilities, manufacturing, and government sectors.

AppDynamics microservices pricing is based on the size of the Java Virtual Machine (JVM) instance; any JVM running with a maximum heap size of less than one gigabyte is considered a microservice. The purchase of one agent unit covers five microservices JVMs.

We’re excited to help usher in this important technology, and to make it feasible and easy for enterprises to deploy AppDynamics Java microservices monitoring and analytics. For a more detailed perspective, see our post, Visualizing and tracking your microservices.

The post AppDynamics Monitoring Excels for Microservices; New Pricing Model Introduced appeared first on Application Performance Monitoring Blog | AppDynamics.

Read the original blog entry...

More Stories By AppDynamics Blog

In high-production environments where release cycles are measured in hours or minutes — not days or weeks — there's little room for mistakes and no room for confusion. Everyone has to understand what's happening, in real time, and have the means to do whatever is necessary to keep applications up and running optimally.

DevOps is a high-stakes world, but done well, it delivers the agility and performance to significantly impact business competitiveness.

IoT & Smart Cities Stories
The deluge of IoT sensor data collected from connected devices and the powerful AI required to make that data actionable are giving rise to a hybrid ecosystem in which cloud, on-prem and edge processes become interweaved. Attendees will learn how emerging composable infrastructure solutions deliver the adaptive architecture needed to manage this new data reality. Machine learning algorithms can better anticipate data storms and automate resources to support surges, including fully scalable GPU-c...
Machine learning has taken residence at our cities' cores and now we can finally have "smart cities." Cities are a collection of buildings made to provide the structure and safety necessary for people to function, create and survive. Buildings are a pool of ever-changing performance data from large automated systems such as heating and cooling to the people that live and work within them. Through machine learning, buildings can optimize performance, reduce costs, and improve occupant comfort by ...
The explosion of new web/cloud/IoT-based applications and the data they generate are transforming our world right before our eyes. In this rush to adopt these new technologies, organizations are often ignoring fundamental questions concerning who owns the data and failing to ask for permission to conduct invasive surveillance of their customers. Organizations that are not transparent about how their systems gather data telemetry without offering shared data ownership risk product rejection, regu...
René Bostic is the Technical VP of the IBM Cloud Unit in North America. Enjoying her career with IBM during the modern millennial technological era, she is an expert in cloud computing, DevOps and emerging cloud technologies such as Blockchain. Her strengths and core competencies include a proven record of accomplishments in consensus building at all levels to assess, plan, and implement enterprise and cloud computing solutions. René is a member of the Society of Women Engineers (SWE) and a m...
Poor data quality and analytics drive down business value. In fact, Gartner estimated that the average financial impact of poor data quality on organizations is $9.7 million per year. But bad data is much more than a cost center. By eroding trust in information, analytics and the business decisions based on these, it is a serious impediment to digital transformation.
Digital Transformation: Preparing Cloud & IoT Security for the Age of Artificial Intelligence. As automation and artificial intelligence (AI) power solution development and delivery, many businesses need to build backend cloud capabilities. Well-poised organizations, marketing smart devices with AI and BlockChain capabilities prepare to refine compliance and regulatory capabilities in 2018. Volumes of health, financial, technical and privacy data, along with tightening compliance requirements by...
Predicting the future has never been more challenging - not because of the lack of data but because of the flood of ungoverned and risk laden information. Microsoft states that 2.5 exabytes of data are created every day. Expectations and reliance on data are being pushed to the limits, as demands around hybrid options continue to grow.
Digital Transformation and Disruption, Amazon Style - What You Can Learn. Chris Kocher is a co-founder of Grey Heron, a management and strategic marketing consulting firm. He has 25+ years in both strategic and hands-on operating experience helping executives and investors build revenues and shareholder value. He has consulted with over 130 companies on innovating with new business models, product strategies and monetization. Chris has held management positions at HP and Symantec in addition to ...
Enterprises have taken advantage of IoT to achieve important revenue and cost advantages. What is less apparent is how incumbent enterprises operating at scale have, following success with IoT, built analytic, operations management and software development capabilities - ranging from autonomous vehicles to manageable robotics installations. They have embraced these capabilities as if they were Silicon Valley startups.
As IoT continues to increase momentum, so does the associated risk. Secure Device Lifecycle Management (DLM) is ranked as one of the most important technology areas of IoT. Driving this trend is the realization that secure support for IoT devices provides companies the ability to deliver high-quality, reliable, secure offerings faster, create new revenue streams, and reduce support costs, all while building a competitive advantage in their markets. In this session, we will use customer use cases...