AIOps for Dummies

AIOps is among the esoteric disciplines information technology managers encounter these days. Many would argue that is the case because information technology operations these days are complex and decentralized. 

To a large degree, that is because modern IT operations no longer are conducted “in house,” but also integrate cloud computing operations, virtual servers spinning up and down, containers sharing operating systems and providing microservices, mobile device and app support. New Internet of Things apps and devices, cloud storage, software as a service and platform as a service all contribute to complexity. 

Among the issues is that AIOps--like artificial computing generally--is not a product. It is instead a capability that enhances other products and services. “AIOps isn’t a specific off-the-shelf product,” say Tony Branton and Ted Coombs, authors of a ServiceNow edition of “AIOps for Dummies.”

Specifically, AIOps is about applying machine learning to supervision processes. Right now, that mostly means AIOps “sits on top” of existing management tools. “Machine learning is a branch of AI that uses the ability of computers to learn by analyzing data and improving answers to questions posed to it autonomously,” the authors say. 

Typically, that means applying supervised or unsupervised learning routines to security or asset management; root-cause analysis; change management; impact analysis, capacity management; performance and availability management. 

Is AIOps a Capability or a Product?

Some markets are hard to estimate because the definitions matter so much. Consider AIOps. A new research report predicts the global AIOps platform market will reach US $11.1 billion in sales by 2025, growing at about a 34 percent compound annual growth rate. 

Aside from the typical issues--how do we define platform; what is AIOps--we face some additional questions. Is artificial intelligence a capability of existing products or a new product category? If the former, then AIOps is a capability, not a product category. If the latter, is it only new products which count? 

source: Techtarget

“Major growth factors” include the growing demand for AI-based value in  IT operations, Cole Reports says. But how do we separate AI features that are embedded in existing platforms and software? Is it the full value of the retail product, or only the incremental value added by AI? 

To use one example, AIOps is not about automation in a direct sense, some would argue, but is a tool to achieve automation. AIOps essentially requires analytics conducted on big data sets, but is not directly big data. AIOps is normally part of some analytics solution for some IT operations function. 

source: dzone

The Cole Reports study suggests properly that AI will be used in information technology operations, which illustrates the definitional issue. If AI improves operations processes, can we cleanly separate AI from the value AI providers for existing operations management products, both embedded into other software and provided as stand-alone solutions?

It is not an unusual task. When estimating 5G revenue, a proper understanding of incremental revenue growth would also subtract the value of replaced 4G connections. Growing streaming revenue also has to be balanced against lower linear subscription revenue to evaluate actual net market changes. 

AIOps presents some similar issues. Definitions matter. If every analytics platform or operations system includes AIOps features, then AIOps becomes almost a meaningless term.

Netreo Unveils AIOps: Autopilot

Netreo, a supplier of  IT management announced the release of AIOps: Autopilot, said to be the first product featuring data models that combine artificial intelligence (AI) and machine learning (ML) technology with 20 years of network management system (NMS) configuration and monitoring data. 

This allows threshold baselines, event correlation rules, dependency mapping, and many other configurations to evolve and improve the longer Netreo is deployed. 

AIOps: Autopilot works with both the on-premises and native-cloud versions of Netreo’s solution, including Netreo Cloud. It runs in the background of a Netreo deployment and constantly scans the configuration to make sure it is always tuned properly, the company says. 

When issues or potential improvements are found, AIOps: Autopilot will either automatically fix the problem or provide engineers suggested remediations using AI and ML algorithms applied against previously gathered historical data. 

AIOps: Autopilot automatically learns from every successive execution and gets smarter, so that it can both reduce unnecessary alerts and preemptively correct more issues over time.

AIOps: Autopilot also comes with an array of extensions out-of-the-box to give operations teams a head start. These extensions provide the ability to:

• Automatically baseline thresholds against historical values and exceptions to minimize false-positives and alert noise.
• Model all metrics against best-practice key performance indicators (KPIs) to ensure there are no blind spots. 
• Learn system and environment changes, and change the monitoring infrastructure to automatically adapt.

Pricing for the AIOps: Autopilot add-on is $12,000 for customers that purchase a Professional Level license. Autopilot is included at the Ultimate License level.

OpsRamp Winter 2020 Release Adds More Analytics

OpsRamp announced its new OpsQ Recommend Mode for first-response and incident remediation. OpsQ Recommend Mode uses predictive analytics to reduce mean-time-to-resolution (MTTR). 

Other artificial intelligence for IT operations (AIOps) innovations in the release include visualization of alert similarity patterns and new alert stats widgets to provide transparency into machine learning-driven decisions, OpsRamp says. 

The OpsRamp Winter 2020 Release also introduces 19 new cloud monitoring integrations for Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP), along with dynamic topology maps for Azure and GCP. 

The Winter 2020 Release introduces the OpQ Bot and a new Recommend Mode for alert escalation policies. 

Visualization of Alert Seasonality Patterns allows IT teams to  visualize seasonality patterns that OpsQ has learned. 

The Alert Stats Widget shows the total number of raw events, correlated alerts, inference alerts, auto-ticketed alerts, and auto-suppressed alerts handled by the OpsQ event management engine. 

OpsRamp also added monitoring support for AWS, Azure, and Google Cloud services, including:

• AWS – Transit Gateway, AppSync, CloudSearch, and DocumentDB
• Azure – Application Insights, Traffic Manager, Virtual Network, Route Table, Virtual Machine Scale Sets, SQL Elastic Pool, and Service Bus
• GCP – Cloud BigTable, Cloud Composer, Cloud Filestore, Firebase, Cloud Memorystore for Redis, Cloud Run, Cloud TPU, and Cloud Tasks

Cloud Topology Maps support automated topology discovery and mapping for Azure and GCP. 

Agentless Discovery and Monitoring for Windows Servers, as its name suggests, allows automated monitoring. Synthetic Monitoring provides deeper insights and analysis for troubleshooting multi-step transactions.

AIOps for Telcos: Is it Automation or Insight? Ericsson Chooses

Whether AIOps is about insight or automation often is unclear, especially because the stated goal of AIOps to gain insights that lead to automated action, supervised or unsupervised by humans. Ericsson, for example, talking about applied artificial intelligence in communications networks, prefers the “automation” label. 

In large part, that might reflect the particular challenges of the connectivity service provider industry, where many revenue and business problems grow from unhappy customers and poor customer experience (dropped calls, no coverage, slow internet speeds, poor connection quality). 

“Previous Ericsson research shows that almost 50 percent of consumer network perception is based on personal experiences of the network, indicating the huge importance of network quality as the key to customer satisfaction,” Ericsson says. 

source: Ericsson

Customer satisfaction, in turn, is believed to be causally related to word of mouth referrals, prospect perceptions of quality and value, customer churn, customer acquisition and therefore revenue, profits and market share. 

“Whether customers give you US$2 or $20 ARPU, if you do not provide the quality they expect, they will churn,” says Vicente Cotino Director of Network Operation Maintenance, Orange Spain. 

To be sure, network performance is not the only driver of satisfaction. “At least one-third of the total NPS (net promoter score, a measure of customer willingness to recommend a product or service to others) score is derived from network performance.” 

Of the service providers surveyed by Ericsson, 80 percent use NPS as a key metric in operations. Also, several service providers indicate that 40 percent to 60 percent of operations key performance indicators are business-related.

Service providers, in other words, must do many things right, beyond ensuring that the network works as it is supposed to work. What is hard to untangle is the percentage of benefit that AIOps might provide, and whether it is automating changes or detecting issues that is “more important.” 

Clearly the two are related. Action is not possible without insight; nor is insight useful unless changes can be made fast. 

In surveys undertaken by Ericsson, 80 percent of respondents say automation is key for the cost and customer experience. About 90 percent of operations personnel say AI is important in protecting customer experience. 

As always, methodology matters. I do not know how the questions were framed. It is not clear how the responses might have been different if poll takers were asked about whether better network and consumer behavior insight and prediction would protect customer experience. 

But Ericsson frames the issue as one of “automation.” That might simply be a reflection of our general confusion about effectiveness and efficiency. People often speak about the importance of efficiency, which might be expressed as getting work done faster, with fewer people, at less cost or with fewer mistakes and rework. 

Less common are evaluations of effectiveness, which might be expressed as “doing the right things” to produce value or desired outcomes. A traditional way of illustrating the difference is to note that an organization gains little to nothing by automating things that should not be done. 

In principle, AIOps as applied to a communications network requires automated insight to produce proactive network responses. So far, we seem to have gotten better at insight, while IT managers still debate the value of unsupervised action by AI-driven systems to correct and modify network operations. 

Put simply, “nobody trusts the system to behave autonomously” at the moment.

Is AIOps About Automation or Insight?

For more than a year, it seems, there has been some debate about whether AIOPs fundamentally is about automation or insight. It can be hard to discern, as a fully-developed AIOps solution, capable of providing insights, also often also is said to include the ability to proactively diagnose and resolve issues. 

Part of the problem is that the approach is so new. Right now, developers are creating new capabilities around the ingestion of data, correlation of incidents and resolution of issues. AIOps is “about making sense of large volumes of information,” says Ciaran Byrne, OpsRamp VP. 

There is an element of remediation and automation, though, he notes. 

At the current stage of development, nobody is willing to entrust AI-enabled systems to reconfigure networks, servers and software autonomously. Beyond that, though, the fundamental value of AIOps arguably is not the automated response but the move “from hindsight to insight to foresight,” says Joris DeWinne, StackState solutions architect. 

Today, enterprises monitor the health of components and systems,  but the problem has been too many notifications, he says. At the next level, monitoring adds logs and metrics. Next-generation monitoring has been added by firms such as ServiceNow, AWS and Azure, using Kubernetes, for example.

AIOps will represent the next level after that, where prediction starts to be feasible, he argues. 

AI Helps, Says Deepsense.AI

Artificial intelligence can solve a number of IT management challenges, argues Andy Thurai, Deepsense.AI U.S operations head. 

Any anomaly can signal the existence of a problem, as the default state of the infrastructure ecosystem is stable, he says. So early detection of an anomaly is usually a sign of a problem that has yet to be fully understood, he says. 

Root cause identification another advantage, when most enterprises have multiple to scores of separate monitoring systems, Thurai says. 

Service tickets analytics are another advantage of applying AI to operations. When fed data on tickets submitted to a service desk, an ML-based model can predict seasonal spikes and requests. This can help the service desk owner deploy help desk personnel as needed. So we predict the number of call center agents you need to have on site, says Thurai. 

AI also can be applied to detect seasonality trends and adjust capacity accordingly. 

Machine-powered analysis delivers insights that are beyond the reach of humans. Frequent pattern mining is possible because AI canfind correlations that are impossible for humans to detect. 

AIOps solutions also eliminate noise and allow staff to concentrate on the real underlying problems.