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Tag: AIOps

  • Build Better CX Across the Entire User Journey with AIOps

    Build Better CX Across the Entire User Journey with AIOps

    Ready or not, digital experiences are now the gateway to customers and the standard-bearer of a company’s brand. Yet, as apps become more distributed, more intelligent and more real-time, they continue to run on infrastructure that resides within on-prem data centers as well as multiple public clouds. This complexity is underscored by gaps across IT teams, leaving businesses, developers and operations teams working in silos without a single source of truth, exposing risks that can inflict not only severe damage to an application’s performance and reliability, but also eventually exposing the entire business. 

    For IT teams, understanding their applications is key. Not only are they the backbone of any organization, but they also hold invaluable data on customers and their decision-making habits. Having complete visibility is critical in today’s multi-cloud and hybrid cloud world.

    When IT teams are able to work quickly and intelligently, they consistently deliver a strong customer experience–avoiding the risk of creating opportunities for customer disengagement and abandonment. Recent research shows that nearly 84% of respondents said they have experienced problems with digital services. As a result, 40% reported switching suppliers due to a poor digital experience and 63% have actively tried to discourage others from using a service or brand if they have a bad digital experience.

    What’s the way forward and how can these existing silos be eradicated? 

    How to Build Better CX

    IT teams are faced with a conundrum–they’ve been asked to manage app complexity while simultaneously navigating operational silos that make collaboration, data exchange and problem resolution incredibly challenging. The way forward lies in a collaborative approach across traditionally siloed teams that leverages artificial intelligence (AI) and machine learning to uncover hidden bottlenecks and resolve issues before they can impact the end-user.

    Transformational CIOs need their teams–across app and infrastructure owners–to speak a common language rooted in what’s important to the application performance, user experience and the business impact. They need infrastructure decisions in service of the application, which will ultimately drive business performance. They also need application teams to be able to go deeper and quickly identify underlying infrastructure that may be contributing to poor application performance. 

    In order for true collaboration to take place, the first step is to increase visibility across the IT organization by providing a common view of correlated data across domains that provides contextualized insights to better collaborate and create a superior digital experience.  

    For application teams, this means gaining insight into the users’ most transited application journeys, helping to clearly identify application bottlenecks and abandonment and to triage efficiently. For the infrastructure teams, improved visibility means having insight into infrastructure constraints impacting app performance so that teams can correlate the user experience to any underlying infrastructure in real-time. Through this increased visibility and context, application and infrastructure teams can clearly identify respective action items and move swiftly to resolve the right issues, at exactly the right time.

    This notion of fill stack visibility and correlated insights that drive action is a mindset commonly referred to as AIOps.

    Enter AIOps

    While it may be true that the emergence of today’s modern application and infrastructure environments has provided the speed and flexibility professionals demand, the numerous services have unleashed a deluge of data on the enterprise IT environment. Automating tasks has helped teams to free up some bandwidth for operations and planning, yet automation alone is no match for today’s increasingly complex environments. What’s needed is a strategy focused on reducing the burden of mounting IT operations responsibilities, and surfacing the insights that matter most so that businesses can take the right action.

    So, what are forward-thinking IT professionals doing to stay ahead of the curve? 

    Many are applying an AIOps approach to the challenge of environment complexities. AIOps leverages advances in machine learning and AI to proactively resolve problems that arise in the application and infrastructure environment. This is so powerful because using AI to identify potential challenges doesn’t just help IT professionals get ahead of problems, it helps companies avoid revenue-impacting outages that jeopardize the customer experience, the business and the brand. What’s more, an AIOps strategy can provide:

    • Intelligent alerting that can be trusted to indicate an emerging issue. By ingesting data, AIOps platforms and technology can play a pivotal role in not just automating existing IT tasks, but identifying and managing new ones based on potential problems detected.
    • Automated root cause analysis and business impact assessment. With the help of AIOps technology, this can be achieved, providing increased agility in the face of potential service disruptions or threats, and without additional drain on resources.
    • Automated remediation for common issues. With an AIOps platform, it’s easy to not only automate remediation for known issues, but unknown issues, too. That’s because it can ingest data and also provide more intelligent insights as a result of it.

    The path forward is clear. Digital businesses are not only the future but they are today’s norm. Any IT leader looking to succeed needs to rethink their overall approach and embrace a future that includes complete visibility of their technology and application stack, and also a strategic approach that keeps their teams operating one step ahead of the curve.

    — Whitney Satin

  • A Deep Dive Into AI for IT Operations

    A Deep Dive Into AI for IT Operations

    Traditional IT administration and management techniques are reforming. By employing artificial intelligence (AI), IT operations are taking an interesting turn in the field of advancements. The term “AIOps” stands for Artificial Intelligence for the IT Operations. AIOps uses AI techniques and algorithms to monitor the data as well as reduce the blackout times. The dominance of digital businesses is introducing technological changes in their operations to streamline them and eliminate the overhead of traditional operational management. 

    Digital companies are generating abundant and information-rich data whose analysis and monitoring do not demand delays. From data collection to storage and data processing to analysis, all operations need to be frictionless to deal with them in an efficient manner. In the last couple of years, the AIOps market has exploded which is ultimately achieving the goal of reducing delays in problem-solving and identification.

    Paving the Road to AIOps

    The story starts with the need of AIOps and digital transformations. Cloud adoption and the shift toward other digital technologies are revamping the IT industry. It also includes the adoption of innovative solutions in the IT operations to make them frictionless. The technologies that are widely used in IT operations vary from AI to machine learning, big data to Internet of Things (IoT) and Application Programming Interfaces (APIs) for various use-cases to edge computing. All these innovations are penetrating into IT operations making them more smooth and sleek.

     AIOps platforms enhance and automate IT operations, using the following different approaches:

    • Using machine learning algorithms and neural networks for better analysis of gathered data. The data is gathered using IoT devices and tools connected with the source and sending data to the cloud for storage. 
    • The system automatically interprets if something is wrong in the operations. The errors are detected and solved immediately without introducing delays in the operations that are dependent on each other to complete.
    • Reducing human power by automating the processes that include the effort of individuals. 

    Components of AIOps

    The two basic components of AIOps that accelerate innovation in IT operations are machine learning and big data. Instead of spending human effort and time into cleaning, processing and analysis of the raw data collected from different platforms, IT operations are able to do all the tasks by employing machine learning algorithms and techniques to judge the nature of data and deduce necessary information from it.

    Factors Boosting the Emergence of AIOps

    AI is a promising technology that holds the confidence of building applications that can help the IT community. AI provides options for reliability, scalability and generic tools that can be deployed to promote the IT operations and boost them without utilizing human power.

    Factors boosting digital transformation in the IT industry:

    • Complex IT Infrastructure: Its operations face many challenges regarding data management and infrastructure. Manual IT operations are becoming harder to handle such as data collection, processing, cloud management, database efficiency, third-party management tools and SaaS integrations. Complexity management is a task that needs proper attention and the environment to deal in a better way. 
    • Handling Abundant Data: IoT technology is penetrating rapidly at an industrial level. IoT devices produce an abundant amount of data. Handling this much data is not the job of individuals. Therefore, by using AI-based algorithms the data is interpreted and analyzed. The performance is monitored using efficient algorithms. 
    • Increased Computing Power: The networking infrastructure has become robust. Therefore the IT industry is in dire need to enhance the processing speed also. By deploying IT solutions that harness AI and machine learning technologies, the computational power of the system can be increased. 

    Enterprises Using AIOps

    The IT companies that are complex and large to which human power is not enough for overall operations are using AIOps to automate the tasks.

    The adoption of technology has a huge impact on the success of IT companies:

    • Development Teams: Companies that have a DevOps model and struggle to manage the roles between the organization are using AI and other innovative technologies to smoothen their processes. With this, developers increase the responsiveness, agility and clarity in the IT operations.
    • Cloud Computing: Cloud computing has a big future. The companies store their huge data in the cloud and all the operations related to cloud data management need to streamline, as it has its own challenges. Therefore, to make the processes flow without errors and monitor the access, there needs to be automation.
    • Digital Transformation: Companies around the world are all active in adopting technology solutions. Whether it is a fintech, travel, healthcare or e-commerce company, all businesses are automating their processing by first digitizing them and providing the user’s facility of utilizing applications based on newly emerged technologies. Information handling can be done by making operations handled by robotics and blockchain technology. AIOps removes friction from the IT operations.

    — James Efron

  • OpsRamp Adds Recommendation Engine to AIOps Platform

    OpsRamp Adds Recommendation Engine to AIOps Platform

    OpsRamp added a recommendation engine to its artificial intelligence for IT operations (AIOps) platform that makes use of predictive analytics to suggest potential actions to either improve performance or avert downtime.

    The OpsRamp Winter 2020 Release also adds 19 cloud monitoring integrations for Amazon Web Services (AWS), Microsoft Azure and Google Cloud Platform (GCP), along with dynamic topology maps for Azure and GCP. OpsRamp currently provides integrations with more than 120 different cloud services.

    Finally, OpsRamp has added an ability to detect seasonality patterns that would otherwise generate alerts, an Alert Stats widget that makes it easier to keep track of raw alerts, synthetic monitoring and agentless discovery and monitoring of Windows Server instances.

    Bhanu Singh, senior vice president of engineering and DevOps at OpsRamp, said as IT continues to become more complex resistance to AI within the ranks of IT staffs is declining. Organizations realize that IT specialists are an expensive resource, so they need to maximize the capabilities of IT generalists as much as possible, he said. OpsQ Recommend Mode adds a predictive analytics module to the company software-as-a-service (SaaS) platform for managing IT that provides explainable and transparent analytical recommendations to help the average IT administrator head off issues that would require a specialist to solve later, said Singh.

    That visibility is critical because it still leaves the decision about what to automate in the hands of the IT administrator. However, as OpsRamp continues to learn the nuances of the IT environment, the more confidence IT administrators will have in the recommendations being surfaced, he said.

    It takes about 24 to 48 hours for the OpsRamp platform to learn enough about an IT environment to start surfacing recommendations, Singh noted, adding because OpsRamp is a SaaS platform, setting up the platform requires minimal effort.

    To some degree an increased reliance on AI among IT professionals is inevitable. AI may never eliminate the need for IT personnel, but many of the mind-numbing rote tasks that IT administrators perform today soon will be automated. The challenge that creates, however, is the IT staff needs to be able to manage IT at a higher level, which means many entry-level jobs in the IT sector may no longer be required. The challenge then becomes finding IT professionals who have the right level of skills, when many of the entry-level jobs for which they have been trained no longer exist.

    In the meantime, a race is clearly on to infuse IT management platforms with machine learning algorithms. Startups such as OpsRamp are looking to supplant legacy IT platforms that were designed for a previous era of IT. However, the providers of those platforms are clearly investing heavily in AI as part of their ongoing modernization efforts. Each IT organization will need to decide for themselves to what degree the rise of AI warrants switching management platforms.

    Regardless of the platform selected, however, the one thing IT teams can count on is that AI platforms are only going to get smarter as they continue to evolve.

    — Mike Vizard

  • Never Ignore the E-Factor

    Never Ignore the E-Factor

    When cloud burst upon the scene, we were treated to wild-eyed prognosticators telling us about the death of the traditional data center. This is a trend in IT that you would think we’d learn from, but we repeat every couple of years. 4GLs, no, XML, no…Were all going to end development, and yet here we are, with more lines of code and more programming languages in use. It just keeps happening.

    These all share two common failings. First, technology in use today may not be perfect, but it is getting the job done, so dumping it for the new silver bullet is an unlikely result either in the long or the short term. For example, we’re many iterations beyond the conception of the C programming language, and yet for some purposes it is still going strong–because it does the job.

    But that’s a little tangential to today’s discussion. Today, the other failing is where I’d like to focus. The E-Factor.

    Enterprises are a lot of things, but one of the things the successful ones are not is careless. Cloud use in the enterprise was always going to be both multi-cloud and hybrid. Why? Multi-cloud because sole supplier arrangements are riddled with possible issues, and hybrid because every enterprise has something they want to maintain sole control of or something that simply can’t be moved but they want to push toward newer environments.

    We were always going to have multiple choices, and successful enterprises were always going to use multiple of them. Containers facilitate this truth a lot. The ability to run containers pretty much anywhere makes them portable, as long as staff is familiar with the infrastructure of a given environment so they can support the container management system in that environment. But even without containers, a cloud portability solution was inevitable.

    There is a massive amount of automation going into IT right now, all for the good. Freeing up traditionally scarce IT hours to focus on more and better services is all to the positive. Inevitably, one or more of these automation tools will be hailed as “The End of X.” Ignore them, and keep rocking at your job. AIOps seems most likely to gather this type of following right now. It will offer a flood of new data and consolidated views of that data, but not much else in the near term. Even when decisions start to be automated, full-on automation based upon AIOps feedback still needs code to run against and infrastructure to run on, so no doom and gloom there.

    Apply the E-Factor to anything hailed as a silver bullet. If you’ve worked in the enterprise, you know there are so many variables in the datacenter that any silver bullet would have to be a splintering round, each splinter behaving in a completely different manner to address current IT needs. More options are always good, and I, for one, go after new tech because I’m non-stop curious. But it is indeed just more options for your IT needs. Use the ones that will help, ditch the ones that make things worse and ignore the cheering “This time we mean it!” crowd.

    — Don Macvittie

  • Five IT Ops-tackles that Stifle Business Growth

    Five IT Ops-tackles that Stifle Business Growth

    I was recently asked about the top five IT obstacles that stifle business growth, and I want to help clear a path too often lined with fear, uncertainty and doubt. In the not-so-distant past, IT was all about the tools. Solutions were more straightforward, teams were less globally connected, and the ever-present data deluge was not changing our business landscape by the millisecond.

    With this in mind, I have created a list of the five most common IT obstacles and will pinpoint the single tool you can implement to help alleviate these pain points.

    IT Staff Lack Time to Learn New Technologies

    This is not to slam our tech teams or leaders; this is just the byproduct of condensed customer schedules, tight purse strings and managing the MOOSE (maintenance and operations of the organization, systems and equipment). The demand on IT teams is high, and we don’t always have the opportunity or the resources to restructure operations for a new tool, as upgrading requires learning new APIs, UI/UX and deployment architectures.

    Difficulty Supporting New Technologies Quickly

    It is hard to keep pace with IT complexity and manage it properly. New services, applications and infrastructures are constantly being added and there are plenty of vendors out there that will get you locked into solving a single issue versus addressing your macro problems. While the benefits of upgrading are there, the difficulty lies in understanding the complexity of these systems and how to leverage them most effectively.

    Lack of Support for Hybrid IT

    Hybrid by definition adds complexity, and multi-cloud, on-prem and private/public clouds are only a few ways to make your hybrid dream a nightmare. No matter the combination, understanding different architectures and their components is a must. Additionally, we need to understand how we manage our own data as well as how our vendors are managing it.

    IT Complexity

    We need to know our machines and architectures well enough to focus limited resources on what impacts the business most. IT will always be complex—there’s always something new, and the amount of information to process is insurmountable and exhausting. Adding context to IT data can help you identify the root cause of overly complex systems. The most difficult task will be to understand the technology well enough to know what to do next. 

    Inability to Understand Business Impact in Order to Prioritize Tasks

    Prioritization is by far the most important aspect of IT. There is a lot of noise, lack of visibility, lack of context, and—the squeakiest wheels of them all—legacy tools. Prioritizing means routing the right people to the right problem to get it solved quickly and elevating IT service visibility, health and risk into the business function.

    Let’s Get Digital

    IT pros can start to tackle these challenges through prioritization and then automation, or AIOps—the principal of AIOps being to add context to data, using machine processes to enrich the data and bring it to a business level. Understand what is important to the organization and think about the outcomes important to IT. To do this, you can create a realization plan to tackle the low-hanging fruit and create achievable milestones and outcomes. For example, look for opportunities and create a crawl-walk-run phased approach driven by business priorities. Each organization has different needs and different timelines—and this process can be applied to customers, too.

    Next, take into account how your team does work and focus on automation as a principle within how you do work. One of the biggest impediments to digital transformation is culture. Remove manual processes, capture tribal knowledge and figure out how to repeat this approach in a consistent way. Additionally, absorb feedback and learn from it. This will drive acceleration when done right, and we all know that better digital experiences boost enterprise value.

    Finally, automation is useless if your data is flawed, and accuracy is a key fault for legacy configuration management database systems. Data must be accurate for effective machine-enabled automation. Leverage the tech you have to build an interconnected ecosystem that is fully integrated and automated—and that avoids tool sprawl. Have an open mind to consider new approaches and be brave enough to shed the legacy mindset. Additionally, take into consideration your partners and vendors, and how that team collaborates, to determine your strategy.

    The Light at the End of the Tunnel

    The path for today’s CIO can be unclear. But you can take bite- (or “byte-”) sized steps to put machines to work and leverage processes that deliver on AIOps. By understanding business outcomes, prioritizing automation as a principle and leveraging trustworthy internal and external relationships, you will have more time in the day for more important tasks. I measure outcomes based on customer experiences, and I ensure great customer experiences by running a great system. While your mileage may vary, moving forward is the most important part.

    — Erik Rudin

  • The 2020 Digital Leader Priorities

    The 2020 Digital Leader Priorities

    When it comes to digital transformation, 2020 promises to be full steam ahead as many of the trends are already well underway. However, it’ll be those businesses that can execute at the quickening rate of technological change that will set the leaders and laggards apart for the years ahead.

    A survey conducted earlier last year by market research firm Vanson Bourne found that 50% of technology leaders have a challenging time managing their digital operations, and nearly 50% of respondents also said it’s their customers that identify problems before their technology and quality control teams do.

    Which brings us to our priority: Digital laggards need to step it up.

    Digital Transformation: Laggards Versus Leaders Packs Separate

    When enterprises shifted from their focus on e-commerce to a broader focus on their digital transformation efforts a few years ago, those who have been successful with their transformations are starting to reap results.

    Still, while the vast majority of enterprises do have digital transformation strategies that they are implementing, a Tech Pro Research report found 70% of respondent companies either had a digital transformation strategy in place or were developing such a strategy. While a separate study, the Kony Digital Experience Index Survey found a strong indication that those efforts aren’t paying off as well as many businesses may like—at least when it comes to their customers.

    According to Kony’s survey, customers are more satisfied when their digital experiences are effortless, and 63% said they would spend more money when their digital experiences are, in fact, effortless. That study concluded that businesses are not listening to the needs of their customers because only 28% of enterprise digital transformation initiatives are explicitly started with customer needs as the priority.

    The 2020 Priority: CIOs and chief digital officers are going to have to ensure that their organizations can execute against their digital transformation strategies. Those that create superior digital experiences are going to start to set themselves further apart from their laggard rivals dramatically. Those CIOs and CDOs at laggard companies are going to have to focus on turning themselves around as quickly as they can.

    The Continued Rise of Robotic Process Automation

    To advance their digital transformation efforts, most enterprises are increasingly turning to robotic process automation (RPA) and software bots to automate any cumbersome and costly manual processes they have in place. While the benefits of RPA and software bots are high, they don’t come without potential danger. These bots, if not managed properly, can open enterprises to increased security and regulatory compliance risks.

    The 2020 Priority: Effectively govern the security of their software bots so the associated risks are mitigated, and they can reap the most significant benefits from RPA.

    Low-Code Development Continues to Help Clear App Backlogs

    The demand for software is relentless, while the availability of professional developers isn’t limitless. And most enterprises have a backlog of application requests they can’t clear. According to a report from OutSystems, currently, 41% of enterprises use low code platforms, with an additional 10% about to start. That was up from 34% in the prior year report.

    Low code development platforms are popular because they help to make software development more accessible to non-developers by creating development more graphical. The low code development platform market is expected to grow to $27.2 billion by 2022.

    The 2020 Priority: focus on low code platforms for business users where it makes sense for them to be able to create their applications and capabilities. Not as a way to displace the development team, but to augment until those applications that warrant development and require professional developers can be built.

    AIOps Starts to Meet Its Promise

    As enterprises increasingly run their business-technology systems on a mix of clouds (both public and private) as well as on-premises systems, managing this infrastructure has grown more complex. The embracing of microservices, continuous delivery and DevOps has enabled organizations to deliver application enhancements more rapidly, automate testing and deliver their software with increased agility. However, interdependencies and the complexity of systems have also increased. This increased complexity shortened the amount of allowable time necessary to resolve system issues.

    When it comes to effective operations management, operations teams need to become as effective as possible since a breakdown anywhere in the environment can initiate thousands, and alerts can create multiple application and service disruptions.

    The 2020 Priority: This increased complexity will prove a catalyst for more enterprises to turn to AIOps. By using data gathered from operations management tasks with machine learning, organizations can become more effective at managing performance and availability and service management. AIOps has remained a buzzword for several years now, with very few actual deployments. That’s set to change.

    Optimizing Microservices Architectures

    Enterprises face a seemingly limitless demand for applications–customer-facing apps, back-office applications, APIs and digital services–and this demand has been a significant catalyst behind the popularity of microservices architectures.

    Microservices also help to ease budget constraints. Almost every survey shows microservices architectures are now in use by the majority of organizations, with benefits being cited, such as improved employee efficiency and customer experience, as well as infrastructure management. Microservices architectures also help enterprises to improve application scalability and bring new digital products and services to market more quickly.

    The 2020 Priority: Identify where microservices architectures can have the most significant impact on revenue-generating processes and prioritize the deployment of microservices to these areas first. It’s these areas where workflow improvement and application agility can have the greatest business impact.

    Looking out at a new year and imagining the possibilities is always exciting, but with the accelerated change in enterprise technologies and the level of digital transformation underway, this year is setting itself up to be more interesting than most.

    — George V. Hulme

  • Predictions 2020: Key Trends that Will Shape the Autonomous Digital Enterprise

    Predictions 2020: Key Trends that Will Shape the Autonomous Digital Enterprise

    As 2019 comes to an end, and we look toward trends driving innovation in 2020 and beyond, technology advancements in hyper automation, AIOps, edge computing and the convergence of IT service management and IT operations management will be at the forefront. 

    Autonomous Digital Enterprise

    In order to thrive in the 2025 futurescape, organizations will need to evolve to Autonomous Digital Enterprises (ADE), a state where intelligent, integrated, value-creating functions can operate with minimal (ideally zero) human involvement or interference, across every facet of the organization, and its ecosystem of partners.

    This concept is based on the notion that every company today is being disrupted by technology and that’s fundamentally changing their business model. The notion of every company being a technology company means that software becomes more than just a system of record to be the system of action, the system of insights and the system of engagement.

    Edge Computing and IoT

    Over the next couple of years, partnerships will prove to be the pathway to success in realizing value from edge computing and IoT solutions. By using seamlessly integrated, multi-vendor, commercial off-the-shelf solutions, enterprises will experience more success than they would with single-vendor niche products. At the end of the day, what we need is an ecosystem of integrated solutions as opposed to a single silver bullet. This ecosystem will be able to harness data and look at it in meaningful ways and translate information into insight that then translates into actions that businesses can take to optimize the use of IoT assets.

    Convergence of IT Service Management and IT Operations Management

    As IT service management and IT operations continue to converge in 2020, CIOs will rely on AI. More than ever, CIOs will need to address this convergence and work to break down silos between these departments. To obtain a comprehensive view of the IT landscape, CIOs will begin to adopt AI, cognitive and robotic process automation. This will bring end-user support and managed back-end infrastructure more closely together. 

    If we look beyond traditional IT and service management, in the near future we will have the ability to go to market with fully integrated solutions that enable boundary-less operations across AppDev, SecOps, ITOps and service management. This will further enable the use of DevOps in organizations using the power of AI to improve existing capabilities and create new capabilities. This doesn’t mean companies have to start from the ground up on AI efforts, rather they need to look for software solutions that can offer prebuilt AI/ML solutions that drive change.

    AIOps

    The demand for AIOps in the enterprise will continue to rise as AI and machine learning have taken the industry by the jugular. Due to an expansion in the number of workloads—both in public cloud and on-premises—and an increase in application complexity, investment in AIOps will increase and ultimately lead to better business outcomes. Today’s challenges place a premium on differentiated vendor solutions powered by AI/ML and big data analytics techniques that can help modern IT operations evolve from traditional monitoring to observability to actionability.

    All of these trends open realms of possibilities for what businesses can achieve in 2020—as long as they can harness the massive amounts of data enterprises have access to and the technology that will allow them to unlock those insights.

    Want to learn more about what to expect in 2020? Join us Jan. 23 for our Predict 2020 Virtual Summit  featuring discussions from some of the industry’s best and brightest offering up their visions for the future. Sign up today for this free daylong virtual event.

    — Ram Chakravarti

  • IoT, AIOps and OMG!

    IoT, AIOps and OMG!

    The task of keeping networking straight while expanding the definition of network to include both physical and virtual networking has fallen heavily upon the op-centric side of DevOps–or what I like to call devOPS. What is defined as networking has grown, sometimes at a ridiculous rate. While the terms are familiar, how you manage them in truly virtualized environments is different than traditional hardware management. More agile, but different.

    And that means those who deal with networking in DevOps have to know more, manage more and debug more–all while working on other parts of the DevOps toolchain. It’s been a slog through increasing capability with increasing workload.

    There is a confluence of new and different things that promise to set this lopsided progress right. We’ll look at them individually, then talk about the sum of the parts.

    Internet of Things (IoT)

    For most of us, when we hear IoT, we think of millions of devices slamming data into a centralized service. iPhones, for example, returning information to Apple. Or highly specialized devices such as utility meters that report far more frequently than humans could go out and read them. But IoT is many things, and increasingly IoT is networking devices. They hold a wealth of information about the state of the network at any given moment, and that information is increasingly being funneled into APIs that IT or management apps can take advantage of. Considering that some of those management apps are also getting data from application tooling and server health status, there is a wealth of information coming together in one place.

    AI Operations (AIOps)

    A networking device can be doing a lot in any given second, and generating data as it works. Servers can be on their way offline when they feed the information out, or maybe sitting idle and wanting more work shipped their way. Application bottlenecks might be identified in the wealth of application tooling information, but buried under the wealth of other response data being shipped back. AIOps aims to help the devOPS side deal with these issues by limiting what makes it to human attention and drawing correlations between issues. Finding that an over-burdened network connection is associated with an apps performance degradation is an easy one to talk about, even though it is pretty simplistic. AIOps can make the connection before a human gets involved, and then the staff member can decide what to do about the actual root cause.

    It’s not at all perfect yet–we’re not generally talking about AIOps making a ton of decisions, rerouting traffic or spinning up new instances to resolve issues at this point–but it is definitely in the “useful tool” category. The cost in person-hours to analyze and reduce problems is definitely cut by the use of AIOps as it exists today, and that benefit will only grow moving forward.

    The biggest problem is the one that bedevils all data interchange, and in fact is the biggest issue in the world of big data–data standardization.

    Enter OMG

    No, my OMG in the headline wasn’t what you thought. It was the Object Management Group (OMG). They’ve been around forever, and have done a pretty good job of developing useful standards, particularly in the area of data interchange. I’ve followed them for a long time, and used their standards since … well, anyone remember CORBA? That was my first set of OMG standards.

    Now they’ve started an Artificial Intelligence Platform Task Force (AIPTF). This group will be looking at all areas of AI to set standards, but the interesting bits for this blog will be in data standardization and communication. Standardizing data and how it is communicated is the next big step in this process, and hopefully the OMG will set us down that path. They’re not specifically looking to address infrastructure big data needs, but their work will likely guide AIOps moving forward. And there is huge potential in that movement. If AIOps gets some standards that help it to be more proactive, we may actually end up with auto-fixing algorithms for obvious and easy issues. We already have the systemic ability in container managers to increase the number of instances serving an application if the existing ones get over-loaded, if we could expand that type of auto-growth to include networking issues and consider cores used by instance to find the best solution. Members of the team that are more devOPS will have more time to focus on new development. And that’s a huge plus.

    The Future is Bright

    Whether the work at OMG benefits us will depend upon whom is involved in the various subgroups they have planned to implement, and cooperation with other ongoing efforts at standardization. Yet I’m hopeful that, like with that original CORBA spec, they will provide a roadmap that others can follow to put us on the path to the much-maligned self-healing network. Giving devOPS a chance to keep up, while leaving them ultimately in control of what happens. Current iterations of AIOps do this to a lesser extent, but more inputs and more automated analysis of those inputs will only make AIOps more powerful. Standardization will make AIOps more able to make use of data and better focused on their specific problem at hand. I see the confluence of all three trends to be a huge positive to the industry if we manage to take advantage of it. And some vendor out there will see the potential, taking advantage of it for us, we just have to find them and use their solutions.

    Meanwhile, paw through the data available to you by hand again, and keep knocking those walls down.

    — Don Macvittie

  • AIOps: The Path to Greater Resilience and Uptime

    AIOps: The Path to Greater Resilience and Uptime

    Large organizations with complex IT infrastructures are facing a significant challenge when it comes to data. The complexity is such that many IT operations teams are struggling to detect, or predict, the complex failure conditions and circumstances that can adversely impact resilience and uptime.

    Monitoring a multitude of systems is a complex task, as the data generated by any system can be vast and far too overwhelming for manual IT operations processes, particularly in relation to analyzing, correlating and prioritizing alerts and remediation.

    Companies have much to gain from a better understanding of the data being created from monitoring tools and practices: increasing the resilience of systems, and the speed of remediation cycles delivers significant cost and time savings. Harnessing advanced technologies to deliver more intelligence to teams in charge of performance monitoring is becoming increasingly important, which is where AIOps can deliver significant value on top of existing monitoring and automation tools.

    Cutting Through the Noise

    Telemetry tools deliver staggering amounts of data. The Microsoft Azure telemetry platform, for example, records 10 petabytes of data per day. When dealing with this volume, the ability to intelligently decipher the severity of alerts becomes vital, especially as many of them may be duplicates or not meaningful in any way. Yet it is IT operations teams that are tasked with understanding how systems are performing, and where resources should be diverted to ensure services are always up and running. To do this effectively, the root causes of alerts must be identified quickly and correctly.

    AIOps is increasingly becoming the go-to solution to the problems inherent in dealing with big data. By applying data collection, data modeling and data analytics techniques and using machine learning algorithms to establish patterns, the noise of the data is reduced as operations teams gain more intelligent insights into alerts. So, providing more context to issues flagged within systems is the goal. Eventually, somebody has to take action when an alert is created, so the more intelligence engineers have, the easier it will be to remediate.

    The less context delivered with alerts, the more time engineers will need to resolve the issue, as the manual remediation process will likely involve interaction with other teams. Take application performance monitoring as an example. In my experience, operations teams do not have an end-to-end understanding of the applications that are live in the production environment. If an alert is created, the ops team will typically seek assistance from a member of the developer team who built it, or another colleague with a deeper understanding of the application, which of course extends the lead time of the remediation process.

    Key Considerations for Deploying AIOps

    By establishing data patterns through machine learning and other advanced analytics technologies, the following key drivers for alerts assessment using AIOps can be established:

    Frequency: Discovering how regularly alerts are created and whether it warrants automation for analysis, correlation, prioritization and remediation.

    Coverage: Telemetry is of course vital to AIOps, as the machine learning algorithms will only be as good as the data they ingest. Putting in place the right telemetry tools is therefore vital to ensure the right data is being gathered. Failing to do so increases the risk of missing critical issues.

    Impact: Not all problems require equal energy and resources. Certain issues might only occur every few months and may not be critical. In such situations, it is not prudent to invest significant amounts of time and money into developing the machine learning algorithms that will identify where and when an alert will be created. Again, cutting through the noise of the telemetry will help to identify high-priority alerts.

    Probability: What is the likelihood of certain issues recurring? Can automation tools, informed by the machine learning algorithms, be implemented to deal with these, i.e. learn where and when they are likely to occur so they can be dealt with by the AIOps platform autonomously?

    Take, for example, the issue of CPU or memory going beyond a certain threshold. Rather than manually contacting customers when an issue is flagged to inform them that services are about to go down before being spun up again, an AIOps platform can automate this process, so remediation happens seamlessly.

    Latency: Automation of the remediation process for an alert deemed critical requires increased investment in AIOps. Getting good data from the early stages of AIOps processes will reduce the amount of time taken to both flag and deal with a problem. Containerized microservices, for example, take very little time to recover, so very little investment is needed when applying AIOps tools that automate remediation. Database recovery, however, is a more complex process, the automation of which would require significant investment.

    What’s the Customer Value?

    For service providers, AIOps is essential if they are to attain the highest levels of resilience, the measure of which all comes down to the Service Level Agreement (SLA). The SLA is measured by a percentage that corresponds to the time systems and are up and running over a year. So, 99.99% uptime is good, 99.999% is great, but 99.9999% is where service providers want to be. To put this into context, downtime for an entire year in the latter scenario amounts to just 32 seconds.

    Increased automation is the only way to achieve optimal SLA. To do this, service providers are increasingly turning to AIOps solutions.

    Operations teams are in charge of the IT strategies that align with business objectives. For service providers, a key part of this is, of course, ensuring maximum availability and optimal performance of the customer-facing environment. Understanding when and where faults appear, and improving resilience against them, is the best way to ensure this. That is where AIOps truly adds value.

    — Prashant Jain

  • An Embarrassment of Riches

    An Embarrassment of Riches

    History says people rarely appreciate what they have in the heat of the moment. Our highly connected and high-speed world is no exception to this rule. The things that frustrate us are in our faces, while the things that make nearly everything better/safer/faster are taken for granted.

    That is every bit as true in IT as it is in life. We expect our deploys and releases to go easily, because life is far easier in that regard than it was even 10 years ago. We get frustrated when things don’t work out, because they so often do.

    The one area Agile has addressed is that most shops don’t beat the person responsible for an issue until they leave. That’s not to say no one is judgmental, it just isn’t official policy to brow-beat even stupid mistakes. The goal is to get better, not to bash the person responsible for the mistake. Most of the time this is a downright positive change. Particularly since, in complex systems, it is often not one individual’s error, but many.

    Before Agile, I rushed out a fix to a piece of software used by several large banks. They needed the fix, and I was most qualified to do it. I used the file handle for the printer to open a printer, and wrote to it. All went well in test and we shipped it out. The results were catastrophic corruption of one set of databases for several large banks.

    Unknown to me, someone had re-used the printer file handle if certain conditions were met, and opened it on the database. It being reserved for printers, that never occurred to me, and I simple reopened it and used it. The strange conditions that pointed it at the database were never encountered in test, and I was responsible for some serious damage to our reputation and the banks in question’s confidence in our software.

    Thankfully, our development team was already on “do it fast, don’t point fingers,” so it was mentioned that better testing could have been done, and we implemented plans to do just that, using scrubbed customer data. And the root cause–using a reserved file handle for access to something else–was fixed. My own angst as having created the mess made me sit with operations while they rebuilt the destroyed databases one at a time, using some astounding Unix command line wizardry. In the end, the software and the team were better off for the experience. And I got an early lesson in not trusting that code would behave as-expected.

    The point being, bashing me for a mistake that put us all on high-stress because our biggest customers were rightfully angry would not have solved the problem. Letting me work through, identify root causes with the test team and get things straight worked better.

    We’re facing a similar hill in DevOps today. Do you test in production? How much do you test in production? What if you have similar totally unforeseen issues crop up?

    The answer is you must test in production. Inevitably, there will be errors in production, it is far better to find them early and fix them than to test in production because the app is live and there were things you couldn’t test outside of production. Either way, you’re testing in production, the question is how much control do you have.

    This is one of the strong suits of DevOps. Using things such as feature flags, monitoring and AIOps, you can spin up a small percentage of production servers with changes. Or deploy parallel servers in production for low-server-count apps, then just stream a few customers to them to test out new changes. Yes, those customers might have a terrible experience if there is some unforeseen issue, but better a few customers than all of your customers if you rolled out without testing. For some industries, building a list of people willing to be used to test new features like this can be a marketing ploy.

    Our frustration at things going wrong will still be there, but the joy of catching those problems before major catastrophe hits our production servers should be there, too. Not long ago, you rolled it out to everyone and dealt with the consequences. Crashes were big and even more frustrating. Better to have a few customers impacted and the ability to fix issues before it’s an emergency.

    I’ve said it before, we work in astounding times with giant leaps forward in responsiveness. Take advantage of them. And take time away from making and running great software to appreciate them, if only for a moment.

    Then add more features and fix more bugs. We are in the heat of the moment after all.

    — Don Macvittie