devopsy.kinsta.cloud

Tag: ITSM

  • SolarWinds Adds Service Management Platform to Portfolio

    SolarWinds Adds Service Management Platform to Portfolio

    SolarWinds has added a service management capability to its portfolio as part of an effort to make it simpler for IT teams to align support services with specific functions.

    At the same time, SolarWinds has updated its SQL Sentry observability tool for databases and DataOps workflows to add query-level wait statistics along with a revamped web portal and dashboard for tracking the overall health of a database environment.

    SolarWinds Enterprise Service Management (ESM) makes it possible for IT teams to manage IT services based on the ticketing systems, knowledge bases, service portals and request catalogs available for specific departments such as marketing or human resources.

    Kevin Kline, senior staff technical marketing manager for SolarWinds, said that as IT management continues to evolve, there is a greater need to streamline and optimize workflows based on the insight from observability platforms. As a result, there is now a need to apply observability more broadly to include database platforms alongside DevOps and other IT workflows, he noted.

    The overall goal is to eliminate the blame game that results when IT platforms are managed in a more siloed fashion, added Kline.

    Most IT teams are still relying on monitoring tools to track a set of predefined metrics in the hope that an alert will enable them to proactively address an issue before it leads to a major disruption. However, as IT environments become more complex, there is a growing need for observability platforms so IT teams can launch queries and surface issues that predefined metrics aren’t likely to identify. Initially, observability platforms were embraced by DevOps teams, but it’s rapidly becoming apparent that this capability needs to be applied to every IT process. In effect, organizations are on the cusp of democratizing observability, noted Kline.

    Achieving that goal will also become simpler thanks to the rise of machine learning algorithms capable of predicting issues and generative artificial intelligence (AI) tools that will make it simpler to craft queries.

    In the meantime, there is already no shortage of observability platforms, but the SolarWinds approach is different in that, in addition to DevOps workflows, the company has integrated its platform with multiple database platforms to provide a more comprehensive approach, said Kline.

    It’s not clear just yet how observability platforms might impact the way IT teams are structured, but the need for specialists such as database administrators (DBAs) isn’t likely to go away anytime soon, noted Kline. Instead, the level of collaboration across various IT teams should substantially improve.

    In the meantime, IT teams might be well-advised to define an observability strategy today before multiple teams start implementing approaches that might be optimized for a relatively narrow range of use cases. After all, trying to reconcile insights generated by multiple observability platforms may very well defeat the purpose of investing in observability in the first place.

  • The Role of AI in ITSM: SLAs, SLOs, SLIs and Error Budgets

    The Role of AI in ITSM: SLAs, SLOs, SLIs and Error Budgets

    In my recent blog Revolutionizing the Nine Pillars of SRE with AI-Engineered Tools, I classified SRE into nine pillars of SRE practices, a comprehensive framework that covers the full scope of SRE practices and suggested how AI can help with each of the pillars.

    For SLAs, SLOs, SLIs and error budgets—arguably the most important pillar of SRE—I indicated that AI can assist by providing accurate predictions of system performance and identifying potential bottlenecks. In this blog, I’ll explain in more detail how AI-engineered tools can be used to improve SLAs, SLOs, SLIs, error budgets and error budget policies within IT service management (ITSM).

    AI for SLAs, SLOs, SLIs, Error Budgets and Error Budget Policies

    Service Level Agreements (SLAs): An SLA is a contract between a service provider and the end user. It outlines the metrics by which the service is measured as well as remedies and penalties if agreed-upon service levels are not achieved. Setting the right SLAs is crucial, as they directly impact on customer satisfaction and the service provider’s reputation.

    The challenges in defining SLAs often involve understanding the trade-off between the cost of providing the service and the level of service that customers expect. AI can assist in this process by providing data-driven insights to inform these decisions. By analyzing historical performance data, customer usage patterns and feedback, AI models can predict the impact of different service levels on both customer satisfaction and operational costs. This can help service providers to set SLAs that meet customer expectations without unnecessarily inflating costs.

    AI can also be used to proactively monitor the agreed-upon SLAs. By continuously analyzing performance data, AI tools can detect potential SLA breaches before they occur, allowing the service provider to take preventative action. This kind of proactive SLA management can help to improve customer satisfaction and reduce the risk of penalties for SLA breaches.

    Service Level Objectives (SLOs): Establishing SLOs involves making informed predictions about system performance, defining realistic yet challenging targets that align with user expectations and business goals. A common challenge in defining SLOs is dealing with the complex nature of distributed systems and their interdependencies, making it difficult to predict performance and set appropriate targets. AI can assist in this process by analyzing historical data to model system behavior and predict future performance trends. This allows SRE teams to set more accurate, data-informed SLOs that reflect the system’s true capabilities. For instance, machine learning models could identify patterns in traffic and use this information to predict periods of high demand, allowing SRE teams to define SLOs that account for these peaks.

    Service Level Indicators (SLIs): SLIs are quantitative measures of some aspect of the level of service such as latency, throughput, availability, error rates, etc. The challenge lies in identifying the right metrics that accurately reflect the user’s experience and the system’s health. Traditionally, these decisions are based on domain knowledge, experience, and sometimes trial and error. AI can assist by analyzing a broad range of metrics across the system, correlating them with issues, incidents and user satisfaction levels to identify which indicators provide the most meaningful measure of service quality. By doing so, AI can help teams select the most appropriate SLIs, thus providing a more accurate picture of their system’s health and performance.

    Error Budgets: An error budget represents the acceptable level of risk or unreliability in a service, providing a balance between reliability and the pace of innovation. Determining an appropriate error budget involves an understanding of system behavior, user tolerance for errors, and business needs. The challenge lies in predicting future performance and understanding the likely impact of potential issues. AI can help overcome these challenges by simulating different scenarios and predicting their impact on the error budget. Machine learning algorithms can learn from past incidents and understand the correlations between different factors, helping teams predict future performance, establish an appropriate error budget, and make better-informed risk decisions.

    Error Budget Policies: These are policies that dictate actions based on the remaining error budget. These decisions often involve human judgment, like deciding when to freeze releases due to an exceeded error budget or when to allow more risk for a new feature rollout. AI can support these decisions by using predictive models to estimate the likely impact of different actions on the error budget. For example, it could simulate the impact of a new release on system reliability, providing insights to help decide whether to proceed with the release or hold it back. By doing so, AI can help teams manage their error budgets more effectively and make more data-driven decisions.

    Implementation Roadmap

    Implementing AI tools for SLAs, SLOs, SLIs, error budgets, and error budget policies must be well-planned and executed systematically. Here’s a practical implementation roadmap:

    Assess the Current Situation: Start by examining the current situation, identifying the areas of need and understanding the existing process of how SLAs, SLOs, SLIs and error budgets are being managed. Also, evaluate the data you currently have available and its quality because AI tools rely heavily on data.

    Define the Goals: Clearly outline what you hope to achieve with the implementation of AI tools. This could include better prediction of system performance, quicker identification of potential bottlenecks, more effective allocation of error budgets, etc.

    Select the Right Tools: Research and select suitable AI tools. Examples of AI-engineered tools that could be useful include:

    • Nobl9 for SLO management: It can assist with setting, monitoring and managing SLOs with AI-driven predictions.
    • Dynatrace: This tool uses AI to detect and diagnose system anomalies, improving observability and helping to inform SLOs and error budgets.
    • Datadog: Offers machine learning-based features that can help forecast future infrastructure metrics, contributing to better SLA and SLO management.
    • New Relic: Its applied intelligence feature can help with incident detection and resolution, helping to better manage error budgets.

    Data Preparation: Aggregate data from various sources, clean it and possibly format it in a way that’s suitable for the AI tools.

    Integration and Deployment: Integrate the AI tools with your existing systems to ensure that the AI tools can effectively communicate with your existing systems.

    Training and Calibration: Many AI tools require a period of training and calibration where the tool learns from your data to make accurate predictions and recommendations.

    Monitoring and Evaluation: Once the AI tools are in place, continuously monitor their performance.

    Iterate and Improve: Based on the evaluations, make necessary adjustments to the tools, your data or your goals.

    Training the Team: They need to understand how to interpret the results and how to take action based on the recommendations of these tools.

    Summary

    In the complex landscape of ITSM, AI is emerging as a transformative force, enabling organizations to manage their systems with unprecedented precision and proactivity. Through smart analysis of historical data and predictive modeling, AI tools are elevating how we define and handle SLAs, SLOs and SLIs. Gone are the days of generic, one-size-fits-all service-level targets. Today, AI empowers us to set realistic, data-informed goals that accurately reflect a system’s capabilities and align with user expectations, ensuring high customer satisfaction and operational efficiency.

    Furthermore, AI brings remarkable improvement to the management of Error Budgets and Error Budget Policies. By simulating different scenarios and predicting their impact, AI helps teams proactively manage risk, making well-informed decisions about error allowance and guiding actions when error budgets are threatened. AI enables us to strike the perfect balance between reliability and innovation, allowing IT teams to continue pushing boundaries without compromising service quality. As the era of AI in IT service management dawns, we stand on the cusp of a new phase of efficiency, control, and excellence in system management. So, join us as we delve deeper into this fascinating intersection of AI and SRE practices.

  • BMC Adds Generative AI Capabilities to Helix Management Platform

    BMC Adds Generative AI Capabilities to Helix Management Platform

    BMC today revealed it is embedding generative artificial intelligence (AI) capabilities across its BMC Helix service and operations management platforms.

    BMC CTO Ram Chakravarti said the company is incorporating the open source GPT-J large language models (LLMS) to build generative AI models. Those models are optimized for specific domains using data collected by BMC and imported from other data sources, like the Jira project management software from Atlassian. That persistent layer of storage is designed to aggregate multiple types of data within what amounts to a Helix data lake.

    The generative AI capabilities added to BMC’s portfolio include natural language summaries of issues and events captured by BMC Helix Operations Management, along with an ability to create a historical analysis of a user’s specific environment to identify recurring issues more easily.

    BMC Service Management, meanwhile, can now correlate incidents and provide resolution summaries that can be stored. It’s also now possible to run clustering analysis on chat archives to find new or improved utterances that help virtual agents answer queries better and execute workflows.

    BMC initially opted to build its own LLMs because general-purpose LLMs—such as the one created by OpenAI—are subject to “hallucinations.” Hallucinations are created when incorrect data is included in a model. BMC is training its LLMs on a narrow range of trusted data to ensure the recommendations and subsequent automations it creates are valid.

    Over time, BMC will add additional LLMs; the company hasn’t yet decided if there are use cases where it makes sense to rely on an existing general-purpose LLM to add additional capabilities, noted Chakravarti.

    Organizations can also opt to use a containerized version of the BMC LLM if they prefer, but BMC will encourage most organizations to invoke generative AI via its Helix SaaS platform, he added.

    BMC Helix is an IT services management (ITSM) platform that integrates with a range of DevOps platforms. Helix is part of a larger existing BMC Connected Digital Ops initiative that seeks to unify ServiceOps and DevOps with AIOps, DataOps and AutonomousOps. Generative AI will automate a range of tasks from within Helix that can then be extended to DevOps platforms such as Jenkins.

    It’s still early days as far as applying generative AI to processes, so organizations should focus on use cases where it will add some immediate value, said Chakravarti. Once they develop a level of trust in the automation created and executed, they can then pragmatically expand the number of generative AI use cases where they make sense, he added.

    One way or another, generative AI is going to be applied to the management of IT. Many routine tasks that conspire to limit the productivity of IT teams today will be eliminated, but there will always be a need for a human to remain in the loop to ensure the right outcomes are achieved. However, it will also soon become possible for existing teams to manage IT at levels of scale that were previously unattainable.

    Image source: Photo_Credite_Indira-Tjokorda via Unsplash

  • Cisco Streamlines Licensing Options for IT Operations Platform

    Cisco Streamlines Licensing Options for IT Operations Platform

    Cisco has reduced the number of licensing options for its Intersight operations platform to two as part of an effort to make the software-as-a-service (SaaS) platform for managing on-premises and cloud computing environments more accessible.

    In addition, Cisco has added a Cisco UCS X-series Modular System to its portfolio based on a two-socket server configured with 4th Gen Intel Xeon Scalable processors and an instance of Intersight that has been extended to make it possible to apply policies that limit energy consumption.

    Mahesh Natarajan, senior director of product management of cloud and compute for Cisco, said Intersight is a complement to the management platform the company already bundles with its Unified Compute Systems (UCS) platforms for on-premises IT environments.

    Essentials enables management of server profiles/policies, managing firmware updates and proactive health monitoring and security advisories along with many other capabilities. The Advantage option adds low-code/no-code orchestration, more advanced server operations, multi-domain visibility and third-party integration with, for example, third-party IT service management (ITSM) platforms.

    In the wake of the COVID-19 pandemic, there’s been a shift toward cloud-based platforms that make it simpler for IT teams to centrally manage multiple IT environments from anywhere. Cisco Intersight, in addition to supporting Cisco servers, has been integrated with cloud services from Amazon Web Services (AWS), Google and Microsoft to achieve that goal.

    In fact, as the management of IT continues to converge across multiple platforms, the concept of a data center has become something of a misnomer, said Natarajan. Integrated application workloads are now routinely distributed across on-premises and cloud computing platforms that are simpler to manage using a consistent set of policies, he added.

    A SaaS platform also provides the added benefit of reducing the total cost of IT because the need to deploy management platforms for each environment is sharply reduced, noted Natarajan.

    Very few organizations are running all their application workloads in one environment, so the need to reduce costs by finding more efficient ways to manage IT operations is becoming a more pressing issue. Most IT teams are trying to limit the number of full-time employees they might otherwise hire by trying to improve the overall productivity of the teams they do have.

    Of course, centralizing the management of IT is as much a cultural challenge as it is technical. Each IT team that deploys a workload on a platform has a natural bias toward using the management framework made available by the platform provider. Each of those platform providers is now extending its reach as part of an effort to become the one control plane that rules them all. Cloud service providers, for example, are extending the reach of their management frameworks into both local data centers and edge computing environments.

    Regardless of preferences, the cost of licensing multiple management platforms can quickly become prohibitive, especially if they provide redundant capabilities. Organizations looking to reduce the total cost of IT would much rather reduce licensing costs than lay off the IT staff that drive a wide range of digital processes. The challenge, as always, is getting everyone in the IT organization to agree on what that one management platform should be.

  • Cloudflare Adds Digital Experience Monitoring to CDN Portfolio

    Cloudflare Adds Digital Experience Monitoring to CDN Portfolio

    At an online CIO Week event, Cloudflare today added a digital experience monitoring capability to a portfolio of services layered on top of its core content delivery network (CDN).

    Corey Mahan, director of product management for Cloudflare, said the Cloudflare Digital Experience Monitoring service extends a Cloudflare Zero Trust platform based on agent software dubbed Cloudflare WARP. The solutions delivers a wide range of software-as-a-service (SaaS) offerings spanning everything from data loss prevention (DLP) to email security.

    He added that the overall goal is to reduce the total cost of IT by delivering SaaS application services more efficiently using a single agent rather than having to deploy multiple agents for each service.

    The Cloudflare Digital Experience Monitoring service, available as a private beta, will enable IT teams to leverage the Cloudflare CDN to measure and analyze controlled data flows to public or private resources by emulating traffic flows using synthetic data. Via integration with the Cloudflare Zero Trust platform, IT teams can also access real-time data visualizations highlighting anomalies in connectivity or performance that might, for example, be indicative of a security breach, noted Mahan.

    While there’s no doubt IT organizations are looking to streamline the number of IT vendors they need to manage, it’s not clear what the center of gravity will be for achieving that goal. Cloudflare is making a case for using the same global CDN that many organizations already rely on for networking services.

    The Cloudflare Digital Experience Monitoring service leverages the company’s CDN to make it simpler to understand, for example, why users in one geography experience sub-optimal application experiences compared to another, said Mahan. That’s especially critical when more organizations than ever have employees working from home across multiple geographies, he added.

    In the longer term, more networking and security functions will be consumed as a service. The challenge then becomes integrating those services into the DevOps workflows that are employed to deploy applications across a highly distributed computing environment. In many cases, what might initially appear to be an application performance issue winds up being a network connectivity problem that is more easily solved.

    One way or another, the management of IT services in the months and years ahead will continue to converge. Organizations of all sizes will need to determine whether it makes economic sense to acquire platforms they need to manage versus consuming a service that is managed on their behalf. Most of the time, a service will be easier to consume and free up resources that can be applied to other IT functions, such as application development.

    Of course, organizations will also need to take care to avoid locking themselves into a specific services provider. Ultimately, however, the question is whether managing a specific IT function provides enough differentiated value to be worth the time and effort when there is so much more to do.

  • Building New Value in IT Systems Management

    Building New Value in IT Systems Management

    For decades, companies treated the IT system as a cost center not much different from facilities maintenance. The job of IT was to keep the lights on, ensuring that the technology a company used worked and that the costs of keeping it working were kept under control. Those days are well over. Not only is technology an operational necessity, but everyone now recognizes that the way companies strategically use technology—whether they buy it, rent it or build it—has a direct impact on company performance. This goes beyond efficiency. Technology is key for everything from customer acquisition and engagement to competitive differentiation and advantage.

    As it turns out, however, not every company has been able to rethink and reconfigure its IT system to meet these new demands. That’s not surprising. Not only does this require a mindset shift from the C-suite on down, but it also requires restructuring the IT function to ensure it supports business objectives in a consistent and measurable way.

    While transforming IT from a cost center to a profit center can hardly happen overnight, there are three things that any company can do to turn IT into both a business enabler and a business driver.

    1. Establish Full Transparency Over Your IT System

    A mission-critical IT system needs to have a thorough understanding of what the organization’s IT landscape looks like. Spending money on duplicate applications and unused licenses is a waste. Unknown SaaS applications pose a persistent security risk. Optimizing spend and improving security are table stakes for business-enabling IT.

    The fact that anyone in a company with a credit card can subscribe to a SaaS service is the main challenge. Effectively tracking down all SaaS applications and licenses, along with monitoring SaaS usage, can’t be handled manually. What is needed, as with other tedious tasks, is automation. For example, by integrating a SaaS management platform (SMP) with SSO and expense management solutions, organizations can automate the discovery of SaaS. When the SMP also integrates with the primary SaaS vendors companies rely on (Microsoft, Google, Salesforce, etc.), it allows organizations to keep track of usage.

    The goal here isn’t for a centralized IT function to exercise more control over the software used by organizations. Instead, the goal is leveraging automation strategically to produce the kind of data-driven insights that allow organizations to make better, more informed decisions about the technology it uses.

    2. Promote Engineering Efficiency

    All companies today are software companies. This means that the engineering function needs to be thought of us as part of the overall IT estate. Not only does the IT system need to support engineering as it would any other business function, but the technology created by engineering itself inevitably becomes part of the broader technology landscape managed by IT.

    Mission-critical IT supports engineering efficiency in at least two ways. First, IT should provide the tools and capabilities needed to drive engineering efficiency. For a long time, measuring the efficiency of engineering was either not done at all or was based on meaningless vanity metrics such as lines of code. Recently, DORA metrics have emerged as the scientifically validated method, widely trusted by engineering teams themselves, for tracking efficiency. IT needs to enable easy tracking of these metrics.

    IT should also facilitate the creation and maintenance of a service catalog for engineering teams. Maintenance of this catalog not only promotes the reuse of services, itself a hallmark of engineering efficiency but also makes it possible to quickly mitigate security issues associated with microservices (Log4j being just one example from the last 12 months).

    Beyond efficiency, however, IT can also support in-house software creation by helping engineering teams with value stream management so they can track the business impact of the software they create. While efficiency can have a business impact (realized through time/cost savings), software drives the business forward when it effectively meets customer needs. IT can facilitate these insights by connecting the dots between customer feature requests, product changes and increased adoption (of products and features).

    3. Make Enterprise Architecture Strategic

    An IT function serving as a mission-critical part of the organization cultivates a comprehensive overview of the software landscape and supports engineering efficiency, as suggested, by viewing the development environment as an integral part of the broader technology estate. However, IT system leaders need to do something more to effectively collaborate with the business leaders and drive the business forward.

    Specifically, IT leaders need to provide the business with insight into all the ways technology influences business performance. This means highlighting risks that need to be addressed, uncovering opportunities that should be pursued, and suggesting options for future planning. Thoughtful and strategic enterprise architecture management here is the key.

    A strategic approach to enterprise architecture management means focusing less on governance (who gets to use what) and more on reducing complexity to promote agility and quick decision-making. The first step in this direction is the adoption of a common language that aligns the business and IT. Business capabilities and the creation of business capabilities maps for different departments serve this purpose.

    To make decisions about technology, business leaders need to know what they are working with. Reliable and current data is essential. For this reason, your enterprise architecture management needs to prioritize ongoing data collection and the maintenance of data quality.

    Finally, to make objectives and options transparent, IT needs to be capable of strategically displaying all enterprise architecture data in a way that can be easily understood and acted on.

    Where Do You Start?

    Taming an unruly SaaS landscape, making it possible to build better software and guiding technology strategy are all easier said than done.

    The good news is, ongoing innovation has created solutions that are easier to use, effectively leverage automation and integrate with all relevant systems. In other words, the tools are there. All that’s needed is the commitment to transforming the IT system and developing it into the critical business function it can and should be.

  • BMC Extends Scope of ServiceOps Initiative

    BMC Extends Scope of ServiceOps Initiative

    At the BMC Exchange conference this week, BMC revealed it has updated its Helix IT service management (ITSM) platform to include mapping and discovery capabilities. Those updates make it simpler for cross-functional teams to correlate incidents, alerts and data and pinpoint root causes faster.

    In addition, BMC is now applying pre-trained artificial intelligence (AI) models to observability data and the service models it tracks to assess service health, predict service impacts and proactively manage IT environments as the volume of metrics, events, and alerts continues to increase.

    Finally, BMC also announced that the BMC Helix Control-M orchestration engine now supports open source Apache Airflow, a workflow management platform for defining data pipelines using a Python programming tool.

    Margaret Lee, vice president and general manager for digital service and operations management at BMC, said these updates extend a unified ServiceOps initiative the company launched last year to unify ITSM, DevOps and customer service desk workflows. While each of these disciplines may have distinct objectives—such as speed and reliability—that are often in conflict with one another, Lee said it’s apparent IT organizations need to reduce the current level of friction that is often encountered when managing IT.

    As a software-as-a-service (SaaS) platform, Lee said the BMC Helix platform achieves that goal via a consumer-like user interface that can be easily personalized. The goal is to make it simpler to create virtual agents, knowledge bases, live chat and tickets that make it easy for customers and employees to request IT help, she added.

    Achieving that goal, however, will require IT organizations to rely more heavily on AI models to automate routine tasks as IT environments become more complex. There are simply not enough IT professionals available to manage IT environments using legacy processes, she noted.

    In addition, end users and customers are expecting service requests to be automated in way that allow them to resolve issues via a mobile device, added Lee. In many cases, how cumbersome IT processes now has a direct impact on both customers and employee retention. The primary goal is to reduce the number of interactions required to complete a task such as changing a password or provisioning a laptop whenever a new employee needs to be onboarded.

    While the pace at which IT management is evolving will vary greatly from one organization to the next, it’s apparent that many DevOps principles are being absorbed into traditional ITSM platforms. The challenge is that cultural divide between DevOps teams and centralized IT teams is still very wide. Most organizations are still trying to determine how far left they want to shift responsibilities toward application development teams versus continuing to rely on IT administrators using graphical tools to centrally manage IT workflows. It’s rarely going to be an all-or-nothing proposition. Rather, the issue is finding a way to strike a balance that enables organizations to be as agile as possible without introducing unacceptable levels of risk to resiliency.

  • OpenText to Create Software Powerhouse – Acquires Micro Focus for $6B

    OpenText to Create Software Powerhouse – Acquires Micro Focus for $6B

    OpenText this week announced it will acquire Micro Focus for $6 billion. The acquisition is part of an effort to reinvigorate a broad portfolio of information management, application development and testing, integration and cybersecurity platforms by accelerating the rate at which they are transitioned to OpenText’s managed private cloud.

    The acquisition is expected to close in the first quarter of 2023, and the combined company expects to reduce costs by an additional $100 million on top of the $300 million Micro Focus previously committed to cutting as part of an ongoing effort to return to profitability and growth.

    OpenText CEO Mark J. Barrenechea told analysts the ultimate goal is to enable organizations to accelerate their digital business transformation initiatives using a set of application programming interface (API)-centric offerings that reside on a private cloud.

    Digital transformation represents the largest opportunity in the history of computing, he said, but Barrenechea also acknowledged that a lot of companies are still struggling with making that transition. “There has to be a better way to get digital transformation right,” he said.

    The Micro Focus portfolio includes everything from a robotic process automation (RPA) offering, a recently-launched IT service management (ITSM) offering—dubbed Service Automation Management X—along with the Vertica database and LoadRunner testing tools. There is also a slew of cybersecurity platforms that include ArcSight, Fortify, IDOL Zenworks and Sentinel along with legacy COBOL application development tools and even the remnants of Novell.

    In effect, OpenText now has one of the broadest software portfolios in the industry after previously acquiring Documentum, Carbonite, Hummingbird and Zix. “The strategy appears to be to simply gather up all the pieces on the chess board,” said Mitch Ashley, principal analyst for Techstrong Research, an arm of Techstrong Group that is also the parent company of devopsy.kinsta.cloud.

    Despite the disjointed Micro Focus portfolio, OpenText has no intention of selling off any of those offerings, said Barrenechea. Going forward, the strategy is to not only accelerate the transition to the cloud but also expand application license renewal services, he added. OpenText also sees an opportunity to expand the appeal of the Micro Focus portfolio into the small-to-medium business (SMB) market, noted Barrenechea.

    All told, it will take six quarters once the deal is closed to align the Micro Focus business model with the one OpenText currently employs, which Barrenechea said should result in growth starting eight quarters after the deal closes.

    There’s no doubt that, once the deal closes, IT organizations that rely on Micro Focus will be closely watching the level of ongoing investment in the portfolio for any signs of one platform being deemphasized in favor of another. It’s also probable that many customers will be relieved to see the stewardship of those platforms assumed by a company that is on firmer financial footing.

    Regardless of how the OpenText portfolio evolves in 2023, however, one thing that is certain is IT organizations have no shortage of alternatives to consider.

  • ServiceNow Adds Lightstep Notebooks to Visualize Observability Data

    ServiceNow Adds Lightstep Notebooks to Visualize Observability Data

    ServiceNow today added a visualization tool to its Lightstep observability platform that will make it simpler for DevOps teams to correlate metrics, logs and traces.

    Ben Sigelman, general manager for Lightstep at ServiceNow, said Lightstep Notebooks will make it easier for DevOps teams to make sense of the massive amounts of data collected by the observability platform.

    Lightstep Notebooks is an extension of the Change Intelligence analysis engine that ServiceNow already makes available within an observability platform it acquired in 2021.

    Included with Lightstep Notebooks are a set of ad hoc charts that include access to heat maps and time-series data with trace exemplars using the database at the core of the Lightstep observability platform.

    An instantly generated investigative path also makes it simpler to identify the root cause of any issue, and each instance of Lightstep Notebooks can be shared with other members of the IT organization via an embedded link. Finally, 100% of trace data can be retained for up to three days.

    Sigelman said the ability to share Notebooks via embedded links will also help bridge the divide between DevOps and IT service management (ITSM) teams that often need to collaborate to resolve an IT issue.

    Naturally, the level of collaboration between those teams tends to vary widely by organization. However, as DevOps best practices are more widely embraced, the number of organizations that are trying to achieve that goal continues to steadily increase. Earlier this year, ServiceNow added an incident response offering to orchestrate on-call escalation, group alerts and provide analytics in further pursuit of that goal.

    Observability platforms aggregate the collection of logs, metrics and traces in a way that makes it possible for DevOps teams to query that data. The goal is to make it easier for DevOps teams to both identify the root cause of an IT issue and pinpoint any anomalies that might disrupt an application environment. The goal is to eliminate the need to convene a “war room” meeting that requires IT teams to suffer through the painstaking process of elimination to identify the root cause of an issue.

    Of course, before an IT environment can be observed, it needs to be instrumented. Thanks to advances in the form of open source agent software being advanced via the OpenTelemetry project managed under the auspices of the Cloud Native Computing Foundation (CNCF), it’s becoming less costly to instrument IT environments. That’s especially critical at a time when more DevOps teams are deploying microservices-based applications that are more complex to manage than legacy monolithic applications. While generally more resilient, the dependencies that exist between the microservices that make up an application can be difficult to identify without the aid of an observability platform.

    As a core DevOps tenet, the need for observability and instrumentation has always been apparent. The challenge has been finding the best way to achieve that at an acceptable cost, Fortunately, there is now no shortage of observability platforms. The only remaining issue is to determine which one best fits within the construct of the organization’s defined DevOps workflow.

  • Kyndryl Aligns with Microsoft in the Cloud

    Kyndryl Aligns with Microsoft in the Cloud

    Following its spinout from IBM, Kyndryl has allied with Microsoft to increase the scope of IT services it provides on the Azure public cloud.

    Stephen Leonard, leader of global alliances and partnerships for Kyndryl, said that as part of this initiative, Kyndryl will be expanding the scope of the services it provides to both organizations building and deploying applications as well as those looking to migrate existing applications to the Microsoft Azure cloud.

    Over time, Kyndryl will expand its cloud services to include additional public clouds as part of a larger effort to enable organizations to embrace a hybrid cloud approach to enterprise IT that spans multiple on-premises and cloud computing environments.

    In the meantime, as part of the first partnership established by Kyndryl, the two companies are committing to providing services to address data modernization and governance, security and application workloads infused with artificial intelligence (AI) capabilities. The two companies also plan to establish a co-innovation lab. Microsoft is also committing to establishing a Kyndryl University for Microsoft.

    Leonard said given the overall shortage of IT expertise, more organizations than ever are relying on external IT services to, for example, accelerate application development. Kyndryl plans to work with those organizations to best evaluate where specific classes of application workloads might best be deployed, he noted.

    It’s become increasingly clear that more organizations are attempting to integrate third-party IT services within DevOps workflows. In some cases, that means standardizing on an opinionated DevOps workflow defined by the IT services provider. In other cases, the IT services provider is focused on a specific task such as securely provisioning infrastructure on behalf of developers.

    Regardless of the approach, however, DevOps workflows are evolving into a team sport made up of both internal and external IT professionals that are trying to bridge the cultural divide that always exists between different organizations. Which IT services provider organizations ultimately select often has as much to do with how flexible they are from a cultural perspective as much as it does their technical expertise.

    For its part, Microsoft is counting on IT services providers such as Kyndryl to increase the total number of workloads running on its cloud. Now that Kyndryl is independent of IBM, it is free to establish partnerships that previously would have been more complex to navigate.

    Of course, it’s up to each organization to determine the degree to which they prefer to work with a larger IT services provider versus a smaller rival that may specialize in one specific area or that is simply a better fit for a smaller company. The one thing that is certain is that as IT continues to become more complex, it’s not feasible for organizations to hire all the IT talent they require on a full-time basis. Organizations will need to carefully evaluate when it makes more sense to consume certain IT services as a function provided by someone else versus attempting to hire and retain talent to maintain that service themselves.