The digital transformation across enterprises and industries has not only accelerated the pace of business, but it’s also led to more organizations adopting more connected applications. In turn, it has increased reliance on the underlying enterprise networks that these critical business applications employ. The infrastructure is becoming more distributed, heterogeneous, intelligent, open, and virtualized to support the growth, agility and scale of the business applications.
Because of the pressure to deliver solutions quickly, application architecture is changing to adopt new technologies such as containers. Containers, with their ability to bundle applications and associated software libraries, enable developers to create “build once, run anywhere” code for portable applications. Their popularity is easy to understand. More than two-thirds of organizations that adopt containers increase developer efficiency, according to a Forrester study. It allows faster deployment of the application entities in multiple data centers and clouds, which can scale dynamically. (In October 2017, DockerCon Europe reported that 24 billion containers have been downloaded.)
Diverse Systems Create Complexity—and Problems
Business-network elements frequently come from a wide variety of hardware and software suppliers. And these networks are only becoming more diverse as business-networking professionals have moved to avoid vendor lock-in, embrace open architectures and use the best solutions.
In this heterogeneous, dynamic application environment, changes can happen abruptly and obliquely. Using legacy techniques to track the changes and correlate the events in this system environment is challenging. Manually processing massive amounts of data—which is dynamic across the stacks—to identify patterns, detect anomalies and predict capacity requirements is almost impossible. So much system noise makes it extremely difficult to uncover and resolve the incidents that are reducing system performance. The result is tremendous business risks and hindered business innovation.
Finding Insight in a Mountain of Data
Combing through all that data to find useful insights that could improve system reliability and performance is tedious and time consuming for IT operations. Instead, consider an artificial-intelligence platform for IT operations (AIOps) that combines machine learning with the ability to auto-discover and correlate entities across critical layers: business, application and infrastructure.
AIOps-powered auto-discovery and machine learning can uncover, correlate and analyze all the data from multiple enterprise applications and infrastructures quickly and accurately, providing visibility into their vulnerabilities. Using machine-learning algorithms to detect patterns and eventually predict outages, AIOps can help IT workers thwart system failures, security issues and performance bottlenecks so IT departments can keep the business running and satisfy customers.
Augment Your IT Staff With AIOps
Artificial intelligence and machine learning aren’t a replacement for people in this scenario. Rather, they help humans perform day-to-day IT operational tasks such as troubleshooting, capacity management, transition and planning. Also, AIOps can offload many menial error-prone tasks from your IT employees, allowing them to focus on more-strategic, higher-level activities that improve business operations.
“Most recent advances in AI have been achieved by applying machine learning to very large data sets,” notes McKinsey & Co. “Machine-learning algorithms detect patterns and learn how to make predictions and recommendations by processing data and experiences, rather than by receiving explicit programming instruction. The algorithms also adapt in response to new data and experiences to improve efficacy over time.”
Data Correlation Feeds Predictive Analytics
Machine learning can correlate and analyze data from multiple enterprise applications and infrastructures, dealing with the volume, velocity and varieties of data generated. It can uncover patterns to show what has occurred. It can use current conditions and past learning to spot exceptions and make predictions. Machine learning can even offer suggestions on what to do in various scenarios.
AIOps platforms employ machine learning to deliver AI capabilities for IT operations. Here are some interesting use cases:
- Multivariate anomaly detection can identify anomalies across various dependent entities. Such anomalies may signal that a planned or unplanned business event has taken place. For example, a multivariate anomaly group may represent an unplanned event such as a DDoS cyberattack or a planned business effort such as Black Friday.
- A time-series sequential-pattern-detection algorithm can predict business outages triggered by events anywhere business functions are deployed in the stack.
- AI and machine learning can also predict when you’ll run out of capacity. For example, they could signal a lack of storage-disk volume and excessive network-bandwidth use. Such information helps IT experts plan capacity to better meet business needs.
Improved System Performance
Machine learning automates IT operations and can notify the department of potential business outages before they happen. It also can detect security issues, identify infrastructure-performance bottlenecks, and recommend capacity augmentation and optimization. IT can then set systems to trigger actions for remediation. Executing remediation scripts or integrating with other orchestration and automation tools to take actions minimizes human tasks.
By actively detecting and fixing system issues with AIOps, you can ensure business continuity and customer satisfaction. In the age of digital transformation, such capabilities and AIOps solutions are a necessity.
With machine learning, IT staff members can continually and completely look for traffic exceptions. As a result, they can be far more effective in preventing and quickly responding to cyberattacks. Businesses can therefore stay up and running, and stay out of the headlines.
AI: Driving Digital Transformation
These are just a few reasons why AI and machine learning have become crucial components of digital transformation. And that trend will only accelerate. “During the next few years, the technologies associated with this [digital transformation] wave—including artificial intelligence, cloud computing, online interface design, the Internet of Things, Industry 4.0, cyberwarfare, robotics, and data analytics—will advance and amplify one another’s impact,” note PwC analysts Leslie H. Moeller, Nicholas Hodson and Martina Sangin.
Many businesses are already on board with AI, and others are planning to implement it. Forrester Research says more than half of organizations already have some form of AI project. And it says another 20 percent are planning AI projects soon.
Machine Learning: The Air-Traffic-Control System for Your Data
Machine learning is to network operations what air-traffic control is to airline operations. Consider that each hour of the day, about 5,000 airplanes are flying just in the U.S. With that much air traffic, using manual processes to track the planes as they move would be nearly impossible and just plain dangerous. Instead, we use air-traffic controllers to manage the chaos. The air-traffic-control system helps experts follow all the traffic (airplanes) among the different domains (various airports and airlines). By combining the various data points and presenting a complete view of what’s happening, air-traffic control helps avoid mishaps and enables a smoother traffic flow.
Machine learning likewise enables data correlation and analytics. IT can thus keep the network and its applications running safely and on time. And that allows organizations to deliver better and safer customer experiences, make better use of their human and technological resources, and keep their applications and businesses online.
About the Author
Sameer Padhye is founder and CEO of FixStream. Before FixStream he served 20 years at Cisco Systems in a variety of senior management roles. Most recently he was SVP Services, Customer Advocacy and Service Provider Line of Business at Cisco. He also chaired the company’s strategy board in this area. Earlier, Sameer worked as Vice President of Service Provider Marketing and was responsible for marketing Cisco products, services and solutions to a worldwide customer base. At different points he was responsible for enterprise and service-provider sales, including Vice President of Sales in the Cisco Europe, Middle East and Africa (EMEA); Japan; and AsiaPac region. Sameer is based in Cupertino, California, where he is responsible for FixStream’s global operations. He’s an avid badminton player and early in his career worked as a commercial-airline pilot.