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Monthly Archives : May 2019

How to Predict and Avoid IT Problems and Outages with AIOps

By Sameer Padhye

Originally Posted on NetworkComputing.com

To succeed as digital companies, enterprises need to reconsider their IT ops strategy, including how they think about application and network uptime. System downtime, although common, is no longer acceptable. With business-critical applications as indispensable as the electricity powering office environments, it’s crucial to avoid system outages and the associated business impact.

We all know the difficulties of monitoring dynamic and ever-changing IT environments. Traditional IT operations management processes and assets are ill-equipped to address the challenges of today’s multi-layered, disparate hybrid IT infrastructure, with its extensive set of applications and services (including third party/outsourced ones) and multiple actors. Outdated domain-centric tools force manual data processing by human IT specialists to correlate thousands and thousands of disparate data points, creating painful bottlenecks that prevent the rapid diagnosis and resolution of system issues.

Improve the visibility of your IT environment and its activities with AIOps

Digital applications generate a huge volume, variety and velocity of data. This flood of data generates a vast number of alerts that need to be analyzed and addressed, with only a few requiring actions. How can an IT team find relevant information in so much system noise?

What if there was a solution that could automate big data analytics analysis and build an accurate, real-time view of all the moving parts across your hybrid IT environment? With the insight provided, you could minimize false alarms/redundant events (system noise), identify anomalies, and more accurately identify probable causes of system incidents.

That solution is Artificial intelligence systems for IT operations (AIOps) solutions — software systems that combine big data analytics solutions, visualization, and AI/machine learning functionality to automate IT operational tasks such as performance monitoring and event data correlations. The term was coined by analyst firm Gartner in 2017, and they recommend AIOps to organizations as an enhancement to application performance monitoring (APM) and network performance monitoring and diagnostics (NPMD) tools.

How does it work? By correlating millions of data points across all IT domains, and applying machine learning to detect patterns, AIOps provides a consolidated overview and interpretation of what’s happening across the entire stack. IT ops team can then use the information to uncover and resolve the root causes of outages and performance issues so system availability is increased.

Augment your IT operations with AIOps for better system reliability

Because of its underlying importance to the enterprise, IT teams are under pressure to maintain system availability and performance. With the average cost of system downtime approaching $300,000-400,000 per hour, many enterprises and service providers are adopting solutions such as AIOps to avoid network/server disruptions and minimize their impact. The insight provided by AIOps can help IT teams do their job better and more efficiently.

It’s important to note here that AIOps systems aren’t necessarily meant to replace existing IT service management tools and personnel. Rather, AIOps can augment IT environments, serving as the glue that binds disparate systems together and helps IT teams make sense of the constant flow of data. The goal is to simplify and streamline IT operations management, improve system reliability, and automate tedious manual processes for faster problem resolution.

Many AIOps solutions can work with legacy IT resources and tools, integrating with existing business applications such as ERP and correlating information previously locked in siloes. By ingesting and consolidating information across the IT environment, an AIOps platform can provide an updated, accurate, synchronized view of IT operations. Staff can then spot and react to pertinent issues in real time.

Identify and Resolve IT Problems Before They Happen

Some AIOps platforms can also aid configuration planning, helping IT teams anticipate how system changes might impact the IT environment. Whether you’re planning a technology upgrade, migrating to the cloud, or installing patches, an AIOps platform can maintain an accurate and updated view into system assets, applications, dependencies, and the underlying infrastructure. This information can help you plan for and mitigate potential issues with the updates – before they cause an outage.

Conclusion: Better IT and Business Performance with AIOps

AIOps can ease the difficulty IT teams have in managing their increasingly complex IT environment and keeping it running at peak performance. By providing an end-to-end view across all domains, AIOps solutions can enable rapid data anomaly detection and investigation of IT incidents, quicker root cause analysis, and automated data analysis, enabling optimized IT systems uptime for better business results.

Avoid “IT Firefighting” With AIOps

By Bishnu Nayak — CTO, FixStream

Originally Posted on DZONE

In today’s 24×7 IT environments, nothing is more important than avoiding system outages and slowdowns that impact business. Without ready access to the desired applications, frustrated workers and customers are unable to complete their transactions. Business grinds to a halt, revenue is lost, and corporate reputation is damaged.

But manually detecting and diagnosing system glitches across a multi-layered, siloed infrastructure is time-consuming and cumbersome. Outdated domain-centric tools leave IT specialists unable to proactively troubleshoot and repair system issues. Your IT team can end up “fighting fires” rather than working on the important projects that add value to the business.

Solution: Predictive Analytics for Continuous Oversight of IT Operations

Predictive analytics, an emerging category of big data analytics, can help organizations predict future outcomes based on historical data. When reviewing the data, analysts can detect trends and patterns that may highlight risks, correlations, or current as well as future conditions.

Already used for applications such as inventory forecasts and customer service, predictive analytics can uncover abnormal trends, detect threats, and forecast issues before they impact operations and create emergencies. Examples include:

Multi-variate anomaly detection can identify anomalies in applications behaving abnormally. For example, utilization spikes on Monday are normal, while a similar surge on Sunday may indicate a security threat.

  • Capacity prediction — Don’t pay for unused servers or be caught short-footed by unanticipated server demand. Use analytics to forecast and optimize system resources usage, while minimizing your operational footprint.
  • Incident prediction — Predictive analytics, enhanced by data mining, can help analysts interpret the structured and unstructured data recorded in tickets. The results can be used to highlight and fix potential failures.

AIOps: Fixing IT Problems Before They Happen

Powered by Machine Learning, AIOps has advanced analytical capabilities to help IT organizations forecast and avoid system issues. Using its Artificial Intelligence capabilities, AIOps can be taught to observe and recognize patterns and anomalies over time. Then it can automatically analyze massive amounts of digital data, correlate leading indicators, and use historical behavior to help predict what could happen next. By delivering contextual operational insight, AIOps can help your team predict and prevent business outages before they cause actual problems.

AIOps can be taught to examine data trends and provide an early warning whenever it discovers possible issues. The application can detect trends and patterns within the “noise” of millions of system incident reports, highlighting potential risks and performance issues.

When system outages occur, the predictive insights from AIOps can speed up root-cause analysis and remediation. By quickly and accurately diagnosing the root cause, problems are fixed faster, often reducing downtime from hours to just minutes. The result is optimized system availability and enhanced business operations.

Conclusion

Depending on the platform, AIOps can quickly predict business application issues across an enterprise’s entire IT stack. Using its end-to-end view across all domains, AIOps helps customers rapidly identify issues and predict outages, so they can resolve problems proactively and avoid IT firefighting.

Stop IT “Brain Drain” with AIOps

By Enzo Signore, FixStream

Originally Posted on NSIGHTAAS

CIO’s face many challenges today – cybersecurity threats, limited budgets, and business transformation issues, to name a few. But is headcount the biggest worry?  According to a May 2018 Gartner survey, CEOs identified a lack of talent and workforce capability as the biggest inhibitor to digital business progress. There just aren’t enough trained IT professionals to go around, and the competition for available tech talent is fierce.

Hiring for artificial intelligence (AI) positions is especially difficult, as fewer than 10,000 people in the world are qualified to do state-of-the-art AI research and engineering. A recent article in the Silicon Valley Business Journal has highlighted just how significant the current AI talent shortage is. And a study by Ernst & Young  has revealed that over 50 percent of companies working with AI say the lack of qualified workers impacts business operations. “This year, as businesses strategized how to integrate AI into their operations, they were hampered by a shortage of experts with requisite knowledge of the technology,” Ernst & Young chief analytics officer Chris Mazzei said in a press release.

Other IT categories, including business intelligence and data analytics, artificial intelligence, and DevOps/agile processes, also face critical staffing shortages, according to CIO magazine. While recruiting outside talent is one way to fill these IT positions, another approach may be to develop internal workers. Many organizations find that the best way to fill job openings is to train existing IT staff in new job skills and in areas like data science and cloud security.

Get strategic about staff retention

Whether the decision is made to hire outside candidates or develop internal staff, filling IT job openings is just the first step. With so many talented people already employed, employee poaching is at an all-time high, making IT staff retention a big challenge as well. The tight job market forces recruiters to become aggressive, reaching out to workers who may not be considering a job change, but would do so for the right incentive.

After spending so much time and effort to hire/develop your IT staff, how can business operators keep these employees from walking out the door? With so many other suitors, how is it possible to stop the “brain drain” of IT workers?

One way to fight back is to keep current employees engaged with innovative projects that capture their interests and use their skills. These workers should be treated like the valuable assets they are. Their time and talent should not be wasted on menial work that can be better done by system tools. Investing in automation solutions and system upgrades that relieve IT workers of tedious system maintenance and testing duties will enable them to work on more valuable activities.

Modernize the organization to retain IT talent

Savvy CIOs realize that they will need to change their work culture if they want to retain talented employees. Implementing work-saving automation tools and cutting-edge technology can boost IT professionals’ morale and job performance. Freeing up the teams to work on the “fun stuff” — interesting, transformative projects with higher value to the business — will improve staff retention rates and deliver greater “bang for the buck” from investment in IT personnel resources.

There are several ways automation such as AI for IT Operations (AIOps) can change culture.

Enable staff to be more efficient – Many IT employees feel underutilized and bored with ongoing responsibilities like patch management and system maintenance, and have no time to work on innovative projects that benefit the business. Eliminate waste by automating IT inventory discovery. In most IT environments, staff wastes significant time and resources performing manual service management tasks since their CMDB is inaccurate and they do not have visibility into CI relationships. This is especifically challenging when the organization has deployed dynamic environments (virtualized, containers, cloud). To avoid repetitious, work waste, automate the discovery of IT assets.

Make teamwork a priority – Break down internal silos by mapping applications to infrastructure. Traditional silos caused by IT monitoring tools force the IT team to manually correlate processes across multiple domains. This means long conference calls, manual collection of large amounts of data, and finger pointing when performance issues arise. By mapping applications to infrastructure, its possible to break down these barriers and drive constructive teamwork.

Become predictive – Increase productivity by automating root cause analysis. Instead of repeating the same manual, error-prone and wasteful troubleshooting process that can last 4 hours on average and consume 11 full time employees (in 15 percent of cases, according to the Digital Enterprise Journal), why not predict when the next outage will occur?  This will not only increase the uptime and performance of the business application, but it will free up the IT staff to work on new projects.

By modernizing recruitment/retention practices, and the IT operations process itself, CIO’s can succeed in hiring and keeping valuable IT personnel. Using AIOps is one way to enhance staff productivity and effectiveness so that these workers become a source of innovation.

FixStream 2019 Predictions: Digital Transformation Powered by AIOps

By Bishnu Nayak, Chief Technology Officer, FixStream

Original Posted in VMBLOG.com

Digital Transformation Powered by AIOps

In a recent press release, industry analyst firm IDC made ten predictions for 2019 which center on the ongoing digitization of the global economy. Underpinned by 3rd Platform technologies such cloud, mobile, and big data analytics, IDC reminds us that digital innovation is transforming industries and regions, and it’s imperative that organizations adapt. As Frank Gens, senior vice president and chief analyst at IDC, pointed out in the press release. “The ability to accelerate digital innovation volume and pace will be the most critical new benchmark for organizations competing in the digital economy”.

The need to keep pace with this digital acceleration means that yesterday’s toolsets and practices-which depend on human skills and manual processes-are no longer effective. Today’s interconnected hybrid cloud IT environments are too complex and siloed, not to mention mission critical, for traditional approaches. Users have very high expectations of system performance, and uptime of critical business applications, so IT Operations need new tools and approaches.

Let’s look at one example to illustrate the challenge. ITSM functions such as change management, asset management, and incident management heavily depend on the accuracy and richness of the data maintained in the underlying CMDB. Yet, when modern data centers leverage emerging technologies such as virtualization, containers, micro services, and/or hybrid cloud to deliver digital services, traditional CMDB’s struggle to keep up. Changes across the infrastructure, such as the addition or deletion of VM’s or containers, or new micro service deployments, may or may not be recorded. Often, the CMDB can’t manage the frequency of changes, or maintain the dependencies and relationships across CIs in an accurate, complete and timely fashion.

Artificial Intelligence for IT Operations (AIOps), the next generation of IT operations analytics, can address this issue and more. AIOps solutions can more efficiently and accurately implement changes and handle incidents that impact critical business services. For example, AIOps can discover interdependencies and relationships of CI’s across the infrastructure and business applications, ensuring the CMDB stays accurate and current. This in turn enriches and optimizes the ITSM functions required for successful delivery and operations of digital services.

Business impacts from new changes can be easily determined and forecasted using AiOps solution enabling enterprises to quickly roll out updates such as new technology, system patches, upgrades/maintenance updates, or new vendor applications. Better yet, AIOps event correlation can quickly identify the root cause of applications negatively impacted by system change done incorrectly.

Industry analysts also see the valuable role that AIOps plays in digital transformation. A Sept. 2018 blog by EMA Vice President Dennis Drogseth reviews the results of a study the firm conducted on a range of AIOps topics. In the survey of 300 ITOps professionals, 87% revealed they are already using an AIOps platform, with another 13% in the planning or testing stage. 54% of respondents identified the CMDB as ‘extremely important’ to their analytic strategy. Drogseth explains that investment in AIOps solution can reinvigorate their CMDB/CMS and application/infrastructure dependency mapping technology areas.

Studies like this make us feel confident in predicting that, in 2019, more and more organizations will adopt AIOps to accelerate their digital business transformation.

Transforming IT Operations Through AIOps-Powered Data Correlation and Visualization

By Sameer Padhye

Originally Posted in Data Center Journal

The digital transformation across enterprises and industries has not only accelerated the pace of business. It’s also led to more organizations adopting more connected applications. That has created greater reliance on underlying enterprise networks on which these critical business applications run. The infrastructure is becoming more distributed, heterogeneous, intelligent, open, and virtualized to support the growth, agility, and scale of the business applications.

With pressure on to deliver solutions as quickly as possible, application architecture is changing to adopt newer 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. It’s easy to understand their popularity. Over 2/3’s of organizations who adopt containers achieve greater developer efficiency, according to a Forrester study. That allows faster deployment of the application entities in multiple data center and cloud environments, 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 given the movement by business networking professionals to avoid vendor lock-in, embrace open architectures, and use best-of-breed solutions.

In this heterogenous dynamic application environment, changes can happen very abruptly and obliquely. Using legacy techniques to track the changes and correlate the events in this type of system environment is very challenging. Manual processing of massive amounts of data – which is dynamic across the stacks – to identify patterns, anomaly scenarios, and predict capacity requirements is almost impossible. So much system noise makes it extremely difficult to uncover and resolve the incidents that are impacting system performance. That, in turn, poses tremendous business risks and hinders business innovation.

Finding Insight in a Mountain of Data

It’s tedious and time-consuming for your IT Operations teams to comb through all that data to find useful insights that could improve system reliability and performance. Instead, consider an Artificial Intelligence platform for IT Operations (AIOps) solution that combines the power of machine learning with the ability to auto-discover and correlate entities across critical layers of digital business – business, application, and infrastructure.

AIOps-powered auto-discovery and machine learning can uncover, correlate and analyze all the data from multiple enterprise application and infrastructure domains quickly and accurately, providing visibility into application and infrastructure vulnerabilities. Using machine-learning algorithms to detect patterns and eventually predict potential outages, AIOps can help IT workers thwart system failures, security issues, and performance bottlenecks, so IT departments can enable business continuity and customer satisfaction.  

Augment your IT Staff with AIOps

Artificial intelligence and machine learning are not a replacement for people in this scenario. Rather, they help humans perform day to day IT operational tasks such as troubleshooting, capacity management, migration, and planning. Also, AIOps can offload many menial error-prone tasks from your IT employees, enabling 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 application and infrastructure domains, 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 predict the future. Machine learning can even offer suggestions on what to do in various scenarios.

AIOps platforms leverage machine learning to deliver AI capabilities for IT operations. Here are some interesting use cases.

  • Multivariate anomaly detection can identify anomaly scenarios 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 like a DDoS cyberattack or a planned business effort such as Black Friday event.
  • A time-series sequential pattern detection algorithm can predict business outages triggered by events anywhere in the stack business functions are deployed.
  • It’s also possible to use AI and machine learning to predict when you’ll run out of capacity. For example, it could signal a potential lack of storage disk volume and excessive network bandwidth use of a router. Such information helps IT experts do proactive capacity planning to better meet business needs.

Improved System Performance

Machine learning automates IT operations and can notify operations teams of potential business outages before they happen. It also can detect security issues, identify infrastructure performance bottlenecks, and recommend capacity augmentation and optimization.

IT teams 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 proactively detecting and fixing system issues with AIOps, you can enable business continuity and assure customer satisfaction. In the age of digital transformation, such capabilities and AIOps solutions are an absolute must.

With machine learning, IT staff can continually and completely look for traffic exceptions. As a result, IT experts can be far more effective in preventing and quickly responding to cyberattacks. So, businesses can stay up and running, and stay out of the headlines.

AI – Driving Digital Transformation Forward

These are just a few reasons why AI and machine learning have become key components of digital transformation. And that’s only going to accelerate moving forward.

“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 implemented some form of an AI project. And it says another 20 percent are planning AI projects in the near future.

Machine Learning – the Air Traffic Control System for Your Data

Machine learning is to network operations as air traffic control is to airline operations. Consider that each hour of the day, there are about 5,000 airplanes flying in the sky just within the U.S.  With that much air traffic, using manual processes to track the planes as they move around would be nearly impossible and just plain dangerous. So instead, we use air traffic controllers to manage the chaos. The air traffic control system helps experts keep track of all the traffic (airplanes) among the different domains (various airports and airlines). By bringing together the various data points and presenting a complete view of what’s happening, air traffic control helps avoid mishaps and enables smoother traffic flow.

Machine learning likewise enables data correlation and analytics. That way, IT experts can 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 moving forward.

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