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Intelligent Ops: A Game Plan for Managing Complex Multi-Cloud Environments

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IT departments continue to harness the power of multi-cloud infrastructures. The ability to move workloads around on-premise data centers to various public-cloud services and software-as-a-service applications gives business unprecedented flexibility to meet the changing demands of digital businesses. But flexibility comes at a price—IT teams are struggling to effectively manage these highly complex IT environments using traditional, siloed tools, such as application performance monitoring platforms and programs for network performance monitoring and diagnostics. These resources don’t give IT staffs all the capabilities they need to fully manage costs, optimize performance and secure hybrid-cloud implementations.

Even more troubling is the inability of yesterday’s tools to help create secure, efficient and agile environments that align with digital business requirements. In short, without a modern strategy for managing multi-cloud environments, investments in digital transformation may never reach their full potential.

Fortunately, there’s an alternative emerging, known as artificial intelligence for IT operations, or AIOps. These platforms combine big data and machine learning algorithms to analyze performance trends, recommend optimizations and, when allowed, act without human intervention.

Automation and continuous insights can improve IT performance over time while keeping operational costs aligned with budgets. The same capabilities may also help enterprises more quickly identify and address security gaps.

“Traditional monitoring and alerting systems tend to be lit up like Christmas trees half the time with an awful lot of noise and false alerts,” says Richard Hebdon, vice president of technology, infrastructure and operations at Elsevier, a global information analytics company specializing in healthcare and science. “AIOps is valuable because it can make sense of all that data.”

Other organizations are coming to similar conclusions. Within four years, 40% of large enterprises will be using artificial intelligence—the AI in AIOps—to support and partially replace monitoring, service desk and automation processes, according to the technology research firm Gartner. That’s up from only 5% today.

While the potential is encouraging, AIOps is still evolving. To take advantage of it today and as it continues to mature over time, enterprises must create a comprehensive strategy for determining its place in their IT operations and implementing it in ways that ensure it delivers value for digital transformation in the years ahead.

40%Within four years,

of large enterprises will BE USING ARTIFICIAL INTELLIGENCE—THE AI

IN AIOPS—to support and partially replace monitoring, service desk and automation

processes.

Introduction



What Makes AIOps Intelligent?

AIOps platforms are gaining attention in IT suites based on their ability to combine large volumes of free-form and structured IT performance data, and then mine it for insights using sophisticated algorithms based

on AI and its subset, machine learning, as well as other analytics capabilities. This helps IT organizations better understand and manage today’s complex environments. AIOps adopters are using the platforms to support and, in some cases, replace traditional performance monitoring and service resources, and apply high degrees of automation that relieve IT technicians of routine but essential management tasks.

Where does the AI come in? The algorithms look for patterns in the flood of IT performance data coming from various onsite and cloud sources. This consists of information from traditional monitoring tools, including packet data that reflects speeds, and bottlenecks across networks. Service and ticket logs provide additional statistics, as well as snippets of text about performance that are logged by operating systems and applications. Cloud services also gather and deliver performance information using tools embedded in their offsite data centers to give customers insights into service levels and uptime.

AIOps then aggregates all this data and applies machine learning algorithms and analytics to understand anomalies that may reveal problems in current operations. A platform can also review historical information for root-cause analyses and predict future disruptions. Continuous performance improvements may be applied through automation tools within the platforms. All this happens with a minimum of hands-on intervention by IT administrators, who are then free to interpret and act on the results rather than wade through mountains of data.

UNLOCKING detailed insights from service desk tickets to address where the bulk of service problems are arising.

COMBINING the wide array of data to troubleshoot performance problems in their early stages, sometimes even before they impact users, which minimizes disruptions to the business.

UNCOVERING AND APPLYING performance improvements over time.

FILTERING out false alarms so IT staffs can, in turn, focus on actual incidents and improve mean time to repair (MTTR) rates, an industry metric for maintenance response times.

MONITORING performance from an end-user’s perspective, which enables IT administrators to better understand and optimize experiences.

HELPING IT organizations better understand charges from public-cloud providers to maintain close control over spending and budgets.


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THESE CAPABILITIES DELIVER A RANGE OF BENEFITS TO ENTERPRISES, INCLUDING:

How AIOps Is Delivering Value Today

A IOps addresses some of today’s biggest challenges. A prime use case for enterprises is to help IT departments manage modern technology

environments that are becoming too complex for humans to handle alone. Gone are the days when IT administrators had to worry only about the core servers, software, networking and storage resources in their onsite data centers. Today, a wide variety of additional assets must also be managed. “IT departments can feel like they are constantly in firefighting mode instead of having time to think strategically about their IT operations and adding new value to their companies,” says Peter Krockta, vice president and head of IT Operations at New York Life Insurance.

For example, an IT staff at a large enterprise may manage thousands of servers across the organization, in addition to all the associated networking, storage and software resources. Responsibilities may also include oversight of public cloud and SaaS systems. But maintaining high speed and availability across the various technology components is difficult with traditional tools that gather data separately for servers, operating systems, networks, storage resources and websites.

“As the environment continues to grow, the challenge becomes more and more difficult,” says Shailesh Deshpande, an independent consultant who specializes

in AIOps and who helped implement the technology at a large network-technology vendor. “There are so many different technology layers, it’s hard to manually keep tabs on everything. And because traditional monitoring systems are often very siloed, it’s hard to see a complete picture of all the factors that impact performance.”

One common problem arises with server configurations. Most organizations maintain a master blueprint of the enterprise’s standard hardware and software settings, written to achieve desired performance levels. But over time, as changes are made to the infrastructure, changes to individual configurations may go unnoticed in the crush of other responsibilities, potentially slowing application response times for business users.

“The conventional response is brute force—to throw more bodies at trying to manage the situation,” Deshpande says. But most IT organizations don’t have reserves of IT talent they can tap into; never mind that a brute force approach isn’t a viable or cost-effective long-term answer for managing infrastructures that will continue to grow and become more complex. AIOps promises more effective IT management without a runup in staffing levels. “Automation and continuous learning are becoming part of the fabric of IT operations,” Deshpande adds.

“ As the environment continues to grow, the challenge becomes more and more difficult. There are so many different technology layers, it’s hard to manually keep tabs on everything. And because traditional monitoring systems are often very siloed, it’s hard to see a complete picture of all the factors that impact performance.”

SHAILESH DESHPANDE, INDEPENDENT CONSULTANT

SPECIALIZING IN AIOPS

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Operationalizes security scans to identify and remediate misconfigurationsThese AIOps capabilities can also enhance cybersecurity efforts, sometimes even before servers are released to production environments. Operations staffs can use the platforms to scan for misconfigurations and make the necessary fixes during the prelaunch testing process, proactively closing any gaps before hackers get a chance to exploit them. The security protections continue after implementation, as AIOps ensures that the available patches for hardware and software keep the system up to date to reduce vulnerabilities and improve compliance.

To be clear, AIOps platforms aren’t designed to be used by the security staff. However, by validating configurations and performing ongoing assessments, the technology helps the operations teams adhere to security policies and identify performance events that may be caused by a security breach.

Allows IT staff to focus on solving real problemsAnother top use case identified by AIOps adopters focuses on mitigating “alert storms” that make it difficult for IT staffs to focus on and solve problems.

Today’s IT environment may contain a diverse set of services, but most are interrelated. So, if a problem arises in a server, for example, it may have ripple effects on networks, applications and storage units. Individual monitoring tools responsible for only one piece of this digital puzzle would each sound alarms, sending multiple alerts to the IT staff for what was actually a single incident.

James Zhang, senior manager for disaster recovery at a global technology-insurance underwriter, was formerly a consultant contracted by a major U.S. air carrier to manage an AIOps proof of concept designed to address alert overload. Within this air carrier, one database system alone was generating tens of thousands of warnings a day, and this was just one component of the airline’s complex IT environment, which included private and public clouds as well as numerous IT systems brought in from other companies after a series of mergers and acquisitions. “The IT department had to manage pretty much everything under the sun, including various types of applications and different monitoring toolsets,” Zhang says.

The proof of concept Zhang oversaw used the airline’s in-house testing lab to create a cross-

“ The IT department had to manage pretty much everything under the sun, including various types of applications and different monitoring toolsets....AIOps reduced the number to a handful so database administrators could solve real problems.” JAMES ZHANG,

SENIOR MANAGER FOR DISASTER RECOVERY AT A GLOBAL TECHNOLOGY-INSURANCE UNDERWRITER

platform view for operators and stakeholders. A central management console showed them the correlations among events and automatically filtered out duplicate alerts that would divert resources and improve MTTR rates. As for the database system that had been issuing thousands of alerts a day? “AIOps reduced the number to a handful so database administrators could solve real problems,” Zhang says.

Gets cloud services and costs under controlAIOps platforms are also being used to better manage cloud services and costs. Many IT shops are struggling to quickly and accurately perform what-if analyses to find the right cloud service for each application. AIOps may provide an answer.

Elsevier was an early adopter of cloud services, dating back to 2011. Over the last three years, the company has been aggressively consolidating on-premise data centers and migrating tens of thousands of associated workloads to public cloud services, along with 18 petabytes of data (enough to fill more than a million 16-gigabyte thumb drives). The company is now evaluating AIOps platforms for its internal IT operations and taking advantage of the types of tools provided by public-cloud vendors to

optimize workloads within their environments.

As this infrastructure evolves, workloads move fluidly throughout the environment. Each day, about 20% of them may be moved to new servers or replaced by new types of workloads as business requirements change. “We’re finding that some classical management tooling doesn’t deal well with that level of volatility,” says Elsevier’s Hebdon. “Those systems are easily tripped up when you recycle lots of nodes on a frequent basis.”

AIOps, however, offers a way to move away from rigid key performance indicators manually set by the operations staff. “It’s difficult to allocate the time to adjust performance thresholds and key performance indicators to make the alerts more accurate,” Hebdon says. Machine learning algorithms that continuously monitor baseline performance and make configuration adjustments can provide a more effective alternative.

By analyzing large volumes of performance data over time, the algorithms within AIOps platforms learn what thresholds and metrics are most effective for signaling problems within the infrastructure. This can help companies like Elsevier detect small changes that


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18 PETABYTESOF DATA

Over the last three years, the company has been

aggressively consolidating on-premise data centers and

migrating tens of thousands of associated workloads to public

cloud services, along with

(enough to fill more than a million 16-gigabyte thumb drives).

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may signal significant events. “AIOps is critical for making sense of that sea of information that’s coming at us, without vastly increasing the amount of people we would need to sort through the data,” Hebdon says.

Managing cloud costs is a problem, however. Because cloud services can be easily spun up or reallocated, it’s difficult for IT managers to track ongoing expenses. When they don’t have detailed information, they risk over- or underprovisioning public-cloud resources, which may lead to unnecessary costs. And traditional finance systems, which were designed to control more stable environments, struggle to handle the volatility of infrastructures where workloads are continuously moving and morphing, Hebdon adds. Because AIOps provides a comprehensive and centralized view of onsite and cloud services, though, the IT department gains a clearer picture of expenditures.

Automates performance enhancements Enterprises can also use AIOps to automate performance enhancements and continuously improve service levels over time. New York Life runs primary and secondary data center locations

in a highly virtualized environment. In addition, it relies on several SaaS applications, including ones for sales, ERP and human resource management. The company is now expanding a cloud-first strategy with three main pillars: “modernize our applications, minimize our technology debt and get closer to the cloud,” Krockta says.

As this multi-cloud approach expands, gaps can emerge in a company’s traditional management tools. “From an operations perspective, teams can be very infrastructure-centric versus being line-of-business-centric,” he explains. “There needs to be better visibility into what end-users are actually experiencing when they use a company’s applications, and what improvements may be made to optimize those experiences.”

A self-healing architecture based on AIOps can recognize early signs of service degradation and then automatically re-route workloads or take other actions to minimize the impact on end-users. “Leveraging AI to minimize MTTR, perhaps even before an end-user experiences a problem, would be a big win,” he says. “IT departments should always be working to become more proactive and less reactive.”

“ Leveraging AI to minimize MTTR, perhaps even before an end-user experiences a problem, would be a big win. IT departments should always be working to become more proactive and less reactive.”

PETER KROCKTA, VICE PRESIDENT AND HEAD OF IT OPERATIONS,

NEW YORK LIFE INSURANCE

6 Considerations When Choosing an AIOps Platform



CONSIDERATION #1: Separate hype from reality to understand what AI and machine learning can deliver today and over timeIn a recent guide to the AIOps market, Gartner notes that while AI is now becoming more widely adopted in commercial applications, it has been around in research labs for decades. “It is important to note that there is very little that is actually new from an algorithmic perspective,” Gartner analysts point out. “What is new is the lowered cost of computing and communications, which has made what was impractical in the late 1980s eminently practical now.”

IT executives who are actively evaluating AIOps platforms should focus on the accessibility of the underlying machine learning algorithms. “Many vendors say they work with AIOps, but what they really have is some type of operations or trend analysis tool, not something that is truly AIOps,” notes Zhang. He examines how well the machine learning capabilities enable unsupervised learning.

“Some applications have the algorithms inside, but they’re either hidden or you cannot tune them. I would also not call that true AIOps,” he says. “Companies need to be able to train the platform to support their individual needs.”

CONSIDERATION #2: Define the needs and initial use cases that would create tangible business valueBefore organizations can select AIOps platforms, they first need to clearly define their goals for the technology, whether it’s to improve performance, increase availability, enhance end-user experiences, reduce the number of service tickets or achieve a variety of results. By prioritizing these goals, IT managers can create a framework for discussions with vendors to determine whose toolsets may address the areas that matter most. Similarly, when it’s time to pilot an AIOps platform, the IT staff can evaluate the technology against performance criteria that reflect these goals.

TO ACCURATELY EVALUATE COMMERCIAL AIOPS PLATFORMS FOR YOUR COMPANY, CONSIDER THE FOLLOWING:

CONSIDERATION #3: Determine how to leverage available management resources in the current environmentIT managers say AIOps platforms should not replace existing monitoring tools but instead provide an uber-monitor that ties everything together for accurate status insights. “Consider what monitoring tools you already have in place for capturing the relevant information,” Deshpande says. “Then look to AIOps to help close any gaps, based on your business requirements, in your existing toolset.”

CONSIDERATION #4: Get control of historical and real-time dataIt’s now table stakes for AIOps platforms to gather and manage all relevant information from diverse sources, including current and historical log data sources and various real-time performance monitoring systems. To do this, the platform must correlate all the information to analyze current conditions and train itself to uncover ways to improve performance over time. But all this data is flowing in from multiple technologies designed by different

companies, so an AIOps platform must be able to communicate with them all and handle different data formats.

“An AIOps platform should be vendor-agnostic,” Zhang says. “Companies that force you to just use their agents and tools for managing data are of limited usefulness. Some companies are good at log ingestion or web services but aren’t strong in terms of machine learning.” What’s required is a common data schema for aggregating data across departments and data sources, as well as restructuring and organizing it prior to running analyses.

CONSIDERATION #5: Carefully evaluate the learning capabilities of an AIOps platformLook at how each platform helps users teach the AI model to distinguish noise from indicators that portend significant events. The training utilities must help users effectively educate the system over time to filter out noise. The best platforms learn in two ways. First, they must support supervised learning, which enables the IT staff to set predefined

“ An AIOps platform should be vendor-agnostic. Companies that force you to just use their agents and tools for managing data are of limited usefulness. Some companies are good at log ingestion or web services but aren’t strong in terms of machine learning.”



JAMES ZHANG, SENIOR MANAGER FOR DISASTER RECOVERY AT A

GLOBAL TECHNOLOGY-INSURANCE UNDERWRITER



performance thresholds that teach the platform when to send alerts about impending problems. IT shops will have already developed these scripted definitions for their traditional monitoring tools.

Second, the AIOps platform should support so-called unsupervised learning that doesn’t rely on predetermined metrics to know when something is amiss. “Unsupervised learning is important because organizations can’t script everything,” Zhang says. “Many problems that arise today will be ones that IT staffs are seeing for the first time. With hundreds of thousands of data sources and millions of events coming in, it’s impossible for the human staff, no matter how experienced it is, to understand and make correlations among all of those data points.”

Zhang looks for AIOps platforms that support a combination of human insights and unsupervised learning with AI. The IT staff provides a feedback loop that trains the AI algorithm how to prioritize issues. “If the system raises an alert about a low-

priority issue, someone can tell it it’s insignificant enough to ignore, so the tool knows to suppress it next time to reduce noise and alert fatigue,” Zhang explains.

An AIOps platform should also be mature enough to know the relative power of the person giving it instructions. The assessment of a less-experienced, first-line support operator shouldn’t carry as much weight as a more-experienced administrator, whose input should outweigh earlier instructions. “Many AIOps platforms have some, but not all, of these features,” Zhang says.

CONSIDERATION #6: Determine whether the platform incorporates monitoring, service desk and automation disciplines After this, evaluate the quality and clarity of the reports, including such capabilities as real-time visualizations and analyses. This ensures that various types of stakeholders, whether technology-oriented or focused on business operations, will be able to make sense of the information.


A Practical Implementation Plan

Once an AIOps platform has been chosen, IT managers must have a clear strategy for implementing it successfully. Because AIOps introduces some new ways

of managing IT infrastructures, enterprises should follow a staged implementation that focuses on a single area of the environment first and then apply the lessons learned through that experience to expand the technology to other areas. “The deployment of AI in an IT operations context is difficult and must be approached gradually,” advise the authors of Gartner’s AIOps market guide. “Resist, at all costs, the temptation to do it all at once. Only after these core ‘manual’ disciplines have been mastered should AI or machine learning be approached.”

IT managers say a good starting point is to enhance performance monitoring for machine learning and statistical analysis to automate pattern discovery, pattern-based prediction and root-cause analysis. Next, move to service desks and IT automation processes.

Creating a solid foundation for this new way of managing IT is also important. “Operations teams must aggressively work to figure out what metrics are important for identifying system bottlenecks and for determining the actions needed to optimize performance,” says Edwin Yuen, an analyst at Enterprise Strategy Group. “Skipping this preparatory work will just slow down the AIOps implementation process and may ultimately

keep organizations from realizing its full potential.”

Both the operations teams and business managers should be involved in scoping out these key metrics, Yuen adds. “As digital transformation progresses, and especially as enterprises move more workloads to the cloud, the true measure of performance is end-user experiences,” Yuen says. “In the end, organizations are not just monitoring system speeds or whether a resource is available or not. The important measure is how well applications and services are being delivered to the business.”

The IT staff at Elsevier is also starting its own prep work by upgrading traditional tools, such as its log management capabilities, which will eventually feed data to an AIOps platform. “The fundamental building blocks for gathering the source data that feeds into the AIOps platform has to be in place before the AI layer can become useful,” Hebdon says.

AIOps isn’t just a technology play though. Enterprises need to assess their internal talent and organizational structures to ensure they fully support these platforms. “Fundamentally, managers need to consider whether they have the right skill sets within their organizations,” Deshpande says. “Many stakeholders just haven’t been trained in the appropriate skills. For example, a long-time systems administrator can’t be asked to start writing infrastructure code or quickly develop machine learning expertise.”



“ As digital transformation progresses, and especially as enterprises move more workloads to the cloud, the true measure of performance is end-user experiences....The important measure is how well applications and services are being delivered to the business.”

EDWIN YUEN, ANALYST,

ENTERPRISE STRATEGY GROUP



While partnering with third-party consultants and systems integrators can help, Deshpande also recommends developing these skills in-house, whether through training or by acquiring new talent. “Third parties will have their own approaches to AIOps, and unless internal people can understand what is or isn’t appropriate for their operations, there may be a disconnect between teams. This can lead to frustration,” Deshpande says. “My recommendation is for organizations to develop the right skills in-house so they can work more effectively with outsiders.”

Organizational models may also need revising, he adds. “Organizations will want to pair people who have AIOps skills with people who are comfortable with change, whether they be operations managers, systems administrators or executives,” Deshpande advises. “In short, it should be anybody in the organization who is willing to help lead the exploration of a new technology and willing to apply a different approach to managing IT operations—and, above all, be willing to make a difference.”

Managers should also be prepared for pushback from those who may not be as open to change. Moving to a more automated, AIOps environment could be a shock to IT people who have grown comfortable being digital firefighters rather than individuals whose roles require them to find new ways for IT to support the business. “Looking beyond current problems to what will be important

tomorrow is a difficult mindset for some people to adopt,” says New York Life Insurance’s Krockta. “At the end of the day, it’s a culture change, and any formal program for adopting AIOps should be accompanied by an organizational change management strategy that recognizes this.”

That strategy should also acknowledge that getting operations people to trust a new AIOps platform will take time. Given the hype that has surrounded AI for years, some technical people may be skeptical about its current level of maturity and effectiveness in real-world applications. Similarly, because IT staffs have an accumulated wealth of institutional knowledge, they may be inclined to follow established processes rather than accepting something as new as AI. Taking an incremental approach that allows the platform to be trained and demonstrate its capabilities to the staff can bring skeptics into the fold.

Conversely, be prepared to encounter people on both the IT and business staffs who have overinflated expectations about the power of today’s AI-based technologies. “They have to understand AIOps isn’t magic,” says Elsevier’s Hebdon. “These platforms are part of an evolution to gather and process information more effectively.” Fortunately, even though AIOps isn’t magic, if implemented correctly, it can be a game changer for complex, hard to manage multi-cloud environments.

Forbes Insights and BMC would like to thank the following individuals for their time and expertise:

• Shailesh Deshpande, Independent Consultant Specializing in AIOps

• Richard Hebdon, Vice President of Technology, Infrastructure and Operations, Elsevier

• Peter Krockta, Vice President and Head of IT Operations, New York Life Insurance

• Edwin Yuen, Analyst, Enterprise Strategy Group

• James Zhang, senior manager for disaster recovery at a global technology-insurance underwriter

Acknowledgments



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