Click here to close now.


@CloudExpo Authors: Pat Romanski, Liz McMillan, Elizabeth White, Ed Featherston, Lori MacVittie

Related Topics: @BigDataExpo, Microservices Expo, Containers Expo Blog, Agile Computing, @CloudExpo, Apache

@BigDataExpo: Article

Babies, Big Data, and IT Analytics

Machine learning is a topic that has gone from obscure niche to mainstream visibility over the last few years

Machine learning and IT analytics can be just as beneficial to IT operations as it is for monitoring vital signs of premature babies to identify danger signs too subtle or abnormal to be detected by a human. But an enterprise must be willing to implement monitoring and instrumentation that gathers data and incorporates business activity across organizational silos in order to get meaningful results from machine learning.

Machine learning is a topic that has gone from obscure niche to mainstream visibility over the last few years. High profile software companies like Splunk have tapped into the Big Data "explosion" to highlight the benefits of building systems that use algorithms and data to make decisions and evolve over time.

One recent article on machine learning on the O'Reilly Radar blog that caught my attention made a connection between web operations and medical care for premature infants. "Operations, machine learning, and premature babies" by Mike Loukides describes how machine learning is used to analyze data streamed from dozens of monitors connected to each baby. The algorithms are able to detect dangerous infections a full day before any symptoms are noticeable to a human.

An interesting point from the article is that the machine learning system is not looking for spikes or irregularities in the data; it is actually looking for the opposite. Babies who are about to become sick stop exhibiting the normal variations in vital signs shown by healthy babies. It takes a machine learning system to detect changes in behavior too subtle for a human to notice.

Mike Loukides then wonders whether machine learning can be applied to web operations. Typical performance monitoring focuses on thresholds to identify a problem. "But what if crossing a threshold isn't what indicates trouble, but the disappearance (or diminution) of some regular pattern?" Machine learning could identify symptoms that a human fails to identify because he's just looking for thresholds to be crossed.

Mike's conclusion sums up much of the state of the IT industry concerning machine learning:

At most enterprises, operations have not taken the next step. Operations staff doesn't have the resources (neither computational nor human) to apply machine intelligence to our problems. We'd have to capture all the data coming off our servers for extended periods, not just the server logs that we capture now, but any every kind of data we can collect: network data, environmental data, I/O subsystem data, you name it.

As someone who works for a company that applies a form of machine learning (Behavior Learning for predictive analytics) to IT operations and application performance management, I read this with great interest. I didn't necessarily disagree with his conclusion but tried to pull apart the reasoning behind why more companies aren't applying algorithms to their IT data to look for problems.

There are at least three requirements for companies who want to move ahead in this area:

1. Establish maturity of one's monitoring infrastructure. This is the most fundamental point. If you want to apply machine intelligence to IT operations then you need to first add instrumentation and monitoring. Numerous monitoring products and approaches abound but you have to get the data before you can analyze it.

2. Coordinate multiple enterprise silos. Modern IT applications are increasingly complex and may cross multiple enterprise silos such as server virtualization, network, databases, application development, and other middleware components. Enterprises must be willing to coordinate between these multiple groups in gathering monitoring data and performing cross-functional troubleshooting when there are performance or uptime issues.

3. Incorporate business activity monitoring (BAM). Business activity data provides the "vital signs" of a business. Examples of retail business activity data include number of units sold, total gross sales, and total net sales for a time period. Knowing the true business impact of an application performance problem requires the correlation of business data. When an outage occurred for 20 minutes, how many fewer units were sold? What was the reduction in gross and net sales?

An organization that can fulfill these requirements is capable of achieving real benefits in IT operations and can successfully apply analytics. Gartner has established the ITScore Maturity Model for determining one's sophistication in availability and performance monitoring. Here is the description for level 5, which is the top tier:

Behavior Learning engines, embedded knowledge, advanced correlation, trend analysis, pattern matching, and integrated IT and business data from sources such as BAM provide IT operations with the ability to dynamically manage the IT infrastructure in line with business policy.

Applying machine learning to IT operations isn't easy. Most enterprises don't do it because they need to overcome organizational inertia and gather data from multiple groups scattered throughout the enterprise. For the organizations willing to do this, however, they will see tangible business benefits. Just as a hospital could algorithmically detect the failing health of a premature infant, an enterprise willing to use machine learning will visibly see how abnormal problems within IT operations can impact revenue.

More Stories By Richard Park

Richard Park is Director of Product Management at Netuitive. He currently leads Netuitive's efforts to integrate with application performance and cloud monitoring solutions. He has nearly 20 years of experience in network security, database programming, and systems engineering. Some past jobs include product management at Sourcefire and Computer Associates, network engineering and security at Booz Allen Hamilton, and systems engineering at UUNET Technologies (now part of Verizon). Richard has an MS in Computer Science from Johns Hopkins, an MBA from Harvard Business School, and a BA in Social Studies from Harvard University.

Comments (0)

Share your thoughts on this story.

Add your comment
You must be signed in to add a comment. Sign-in | Register

In accordance with our Comment Policy, we encourage comments that are on topic, relevant and to-the-point. We will remove comments that include profanity, personal attacks, racial slurs, threats of violence, or other inappropriate material that violates our Terms and Conditions, and will block users who make repeated violations. We ask all readers to expect diversity of opinion and to treat one another with dignity and respect.

@CloudExpo Stories
The last decade was about virtual machines, but the next one is about containers. Containers enable a service to run on any host at any time. Traditional tools are starting to show cracks because they were not designed for this level of application portability. Now is the time to look at new ways to deploy and manage applications at scale. In his session at @DevOpsSummit, Brian “Redbeard” Harrington, a principal architect at CoreOS, will examine how CoreOS helps teams run in production. Attende...
As-a-service models offer huge opportunities, but also complicate security. It may seem that the easiest way to migrate to a new architectural model is to let others, experts in their field, do the work. This has given rise to many as-a-service models throughout the industry and across the entire technology stack, from software to infrastructure. While this has unlocked huge opportunities to accelerate the deployment of new capabilities or increase economic efficiencies within an organization, i...
The buzz continues for cloud, data analytics and the Internet of Things (IoT) and their collective impact across all industries. But a new conversation is emerging - how do companies use industry disruption and technology enablers to lead in markets undergoing change, uncertainty and ambiguity? Organizations of all sizes need to evolve and transform, often under massive pressure, as industry lines blur and merge and traditional business models are assaulted and turned upside down. In this new da...
The Internet of Things (IoT) is growing rapidly by extending current technologies, products and networks. By 2020, Cisco estimates there will be 50 billion connected devices. Gartner has forecast revenues of over $300 billion, just to IoT suppliers. Now is the time to figure out how you’ll make money – not just create innovative products. With hundreds of new products and companies jumping into the IoT fray every month, there’s no shortage of innovation. Despite this, McKinsey/VisionMobile data...
There are so many tools and techniques for data analytics that even for a data scientist the choices, possible systems, and even the types of data can be daunting. In his session at @ThingsExpo, Chris Harrold, Global CTO for Big Data Solutions for EMC Corporation, will show how to perform a simple, but meaningful analysis of social sentiment data using freely available tools that take only minutes to download and install. Participants will get the download information, scripts, and complete en...
Containers are changing the security landscape for software development and deployment. As with any security solutions, security approaches that work for developers, operations personnel and security professionals is a requirement. In his session at @DevOpsSummit, Kevin Gilpin, CTO and Co-Founder of Conjur, will discuss various security considerations for container-based infrastructure and related DevOps workflows.
“All our customers are looking at the cloud ecosystem as an important part of their overall product strategy. Some see it evolve as a multi-cloud / hybrid cloud strategy, while others are embracing all forms of cloud offerings like PaaS, IaaS and SaaS in their solutions,” noted Suhas Joshi, Vice President – Technology, at Harbinger Group, in this exclusive Q&A with Cloud Expo Conference Chair Roger Strukhoff.
Containers are revolutionizing the way we deploy and maintain our infrastructures, but monitoring and troubleshooting in a containerized environment can still be painful and impractical. Understanding even basic resource usage is difficult - let alone tracking network connections or malicious activity. In his session at DevOps Summit, Gianluca Borello, Sr. Software Engineer at Sysdig, will cover the current state of the art for container monitoring and visibility, including pros / cons and li...
Between the compelling mockups and specs produced by analysts, and resulting applications built by developers, there exists a gulf where projects fail, costs spiral, and applications disappoint. Methodologies like Agile attempt to address this with intensified communication, with partial success but many limitations. In his session at DevOps Summit, Charles Kendrick, CTO and Chief Architect at Isomorphic Software, will present a revolutionary model enabled by new technologies. Learn how busine...
Can call centers hang up the phones for good? Intuitive Solutions did. WebRTC enabled this contact center provider to eliminate antiquated telephony and desktop phone infrastructure with a pure web-based solution, allowing them to expand beyond brick-and-mortar confines to a home-based agent model. It also ensured scalability and better service for customers, including MUY! Companies, one of the country's largest franchise restaurant companies with 232 Pizza Hut locations. This is one example of...
IT data is typically silo'd by the various tools in place. Unifying all the log, metric and event data in one analytics platform stops finger pointing and provides the end-to-end correlation. Logs, metrics and custom event data can be joined to tell the holistic story of your software and operations. For example, users can correlate code deploys to system performance to application error codes.
SYS-CON Events announced today that VividCortex, the monitoring solution for the modern data system, will exhibit at the 17th International Cloud Expo®, which will take place on November 3–5, 2015, at the Santa Clara Convention Center in Santa Clara, CA. The database is the heart of most applications, but it’s also the part that’s hardest to scale, monitor, and optimize even as it’s growing 50% year over year. VividCortex is the first unified suite of database monitoring tools specifically desi...
There are many considerations when moving applications from on-premise to cloud. It is critical to understand the benefits and also challenges of this migration. A successful migration will result in lower Total Cost of Ownership, yet offer the same or higher level of robustness. Migration to cloud shifts computing resources from your data center, which can yield significant advantages provided that the cloud vendor an offer enterprise-grade quality for your application.
Cloud computing delivers on-demand resources that provide businesses with flexibility and cost-savings. The challenge in moving workloads to the cloud has been the cost and complexity of ensuring the initial and ongoing security and regulatory (PCI, HIPAA, FFIEC) compliance across private and public clouds. Manual security compliance is slow, prone to human error, and represents over 50% of the cost of managing cloud applications. Determining how to automate cloud security compliance is critical...
Manufacturing has widely adopted standardized and automated processes to create designs, build them, and maintain them through their life cycle. However, many modern manufacturing systems go beyond mechanized workflows to introduce empowered workers, flexible collaboration, and rapid iteration. Such behaviors also characterize open source software development and are at the heart of DevOps culture, processes, and tooling.
Saviynt Inc. has announced the availability of the next release of Saviynt for AWS. The comprehensive security and compliance solution provides a Command-and-Control center to gain visibility into risks in AWS, enforce real-time protection of critical workloads as well as data and automate access life-cycle governance. The solution enables AWS customers to meet their compliance mandates such as ITAR, SOX, PCI, etc. by including an extensive risk and controls library to detect known threats and b...
You have your devices and your data, but what about the rest of your Internet of Things story? Two popular classes of technologies that nicely handle the Big Data analytics for Internet of Things are Apache Hadoop and NoSQL. Hadoop is designed for parallelizing analytical work across many servers and is ideal for the massive data volumes you create with IoT devices. NoSQL databases such as Apache HBase are ideal for storing and retrieving IoT data as “time series data.”
Clearly the way forward is to move to cloud be it bare metal, VMs or containers. One aspect of the current public clouds that is slowing this cloud migration is cloud lock-in. Every cloud vendor is trying to make it very difficult to move out once a customer has chosen their cloud. In his session at 17th Cloud Expo, Naveen Nimmu, CEO of Clouber, Inc., will advocate that making the inter-cloud migration as simple as changing airlines would help the entire industry to quickly adopt the cloud wit...
Overgrown applications have given way to modular applications, driven by the need to break larger problems into smaller problems. Similarly large monolithic development processes have been forced to be broken into smaller agile development cycles. Looking at trends in software development, microservices architectures meet the same demands. Additional benefits of microservices architectures are compartmentalization and a limited impact of service failure versus a complete software malfunction....
The web app is agile. The REST API is agile. The testing and planning are agile. But alas, data infrastructures certainly are not. Once an application matures, changing the shape or indexing scheme of data often forces at best a top down planning exercise and at worst includes schema changes that force downtime. The time has come for a new approach that fundamentally advances the agility of distributed data infrastructures. Come learn about a new solution to the problems faced by software organ...