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Tag: IT operations management

  • BigPanda Provides Free 90-Day Access to IT Ops Platform

    BigPanda Provides Free 90-Day Access to IT Ops Platform

    BigPanda, a provider of an IT ops platform infused with machine learning algorithms, this week announced it will make available for free for 90 days an instance of its software available to IT professionals working from home to help combat the COVID-19 pandemic.

    Mohan Kompella, vice president of product marketing for BigPanda, said the IT Ops From Home program is intended to make it easier for IT teams to manage IT operations using a platform that was designed from the ground up to remotely manage highly distributed computing environments.

    BigPanda is also providing free 90-day support along with free training via a virtual classroom. Organizations that opt to participate in the program are under no obligation to renew.

    Kompella said the BigPanda platform is designed to correlate events in a way that automates IT incident management. It provides built-in integrations with software and services from Amazon Web Services (AWS), AppDynamics, Cloudwatch, Splunk, Azure, Jira and Slack.

    It may take a little effort for IT organizations to implement a new platform for managing IT in the midst of a pandemic, he conceded, but also noted there are organizations that are finding their existing IT platforms are not designed to enable IT teams to support an entire workforce that is now accessing distributed applications from home. The BigPanda platform makes it possible to unify the management of all the silos that now make up an enterprise IT environment—in fact, Kompella said managing IT environments remotely may soon become the “new normal.” In much the same way many organizations now employ remote workers to provide customer service, Kompella noted it’s possible many IT support staff will now simply work from home. That approach would, among other things, reduce the cost of office space for IT personnel.

    As organizations struggle to implement business continuity plans there’s no doubt many legacy IT operations platforms are being stretched to their limit. Many of the plans organizations had to slowly transition to a modern IT management platform now need to be accelerated, said Kompella.

    There’s no doubt the COVID-10 pandemic will have a profound impact on the way IT is managed. Many processes will be forever altered, especially as organizations embrace DevOps processes to manage application workloads that are increasingly likely to be deployed in the cloud, as part of a concerted effort to make business processes more resilient in the face of global disruptions such as the COVID-19 pandemic. After all, no one is quite sure when the current crisis might subside or return. There may even additional pandemics to contend with in the future.

    Whatever the motivation, an evolutionary approach to modernizing IT ops will be moving forward at a faster pace among organizations that have the wherewithal to stave off the current economic downturn caused by the pandemic. In fact, there may even be a high correlation between organizations that fail to modernize those processes and those organizations that don’t re-emerge stronger in 2021.

    — Mike Vizard

  • 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

  • Chaos Engineering for ITOps

    Chaos Engineering for ITOps

    Chaos engineering (CE) is the discipline of experimenting on a system in order to build confidence in the system’s capability to withstand turbulent conditions in production. This approach is becoming commonplace in software development and operations (DevOps) practices. But how would its application extend to ITOps? CE for ITOps offers a similar framework for stress-testing a technology platform to understand its weak points and performance pitfalls under heavy pressure.

    CE tends to be used primarily in DevOps during bug testing: setting up experiments to run software under different conditions, such as peak traffic, and monitoring how it functions and performs. This becomes increasingly necessary in cloud-based systems where failure to understand extreme load responses could result in runaway cascade failures or, worse yet, spinning up thousands of extra nodes handling error conditions while not doing any actual work. These same principles, applied to IT operations management (ITOM), help define a functional baseline plus tolerances for infrastructure, policies and processes by clarifying both steady-state and chaotic outputs when extremes are reached.

    Applications in IT

    The theory of CE in DevOps gained early traction at Netflix as they moved from physical to virtual infrastructure, with the team that implemented it on AWS breaking off to form Gremlin. However, CE is not typically used in ITOps, because ITOM has historically been separated from development (generally, IT monitors system dynamics, and when a problem occurs, engineering change management or ITSM is brought in to remediate the issue).

    With the growth of containerization in cloud applications today, IT infrastructure looks more like development environments than classical multi-tier architectures. But the limitless scale of the cloud means failures can also be limitless: Microservices are well-served by testing elasticity and scalability, data flows and resiliency through stressing the system to the edge of its tolerances and fixing their shortcomings before a public crash.

    One, Two, Three…Chaos

    Implementing chaos engineering for ITOM provides a systematic approach to identifying weaknesses in a microservices-world. In a monolithic environment, you have visibility into performance and event metrics that may be lost with microservices designs. As a result, the need for operational insights becomes even more critical when scaling to unknown workloads.

    Netflix’s Chaos Monkey grew out of CE principles from their own cloud-native community, meant to address the gaps in common dev tools’ abilities to manage extreme complexities. This methodology is extendable to infrastructure and helps to set guardrails on platform behavior as a whole.

    Here are five fundamental steps to follow in order to bring this thinking into your team’s ITOM.

    Define the Current Steady State

    Performing baseline analysis is a standard concept in capacity planning, upgrade strategies and other high-impact functions. Start with something relatively simple (and small) so you don’t get overwhelmed by the data, or risk interfering with the business if something goes wrong (such as security Red Teaming). For example, monitoring CPU and network utilization, which are common bottlenecks in any IT shop.

    Define Optimal Conditions

    There’s how your system generally operates, and then there’s how it should operate; these typically aren’t the same thing. CPU utilization and network latency are always affected by application efficiencies, hardware conditions and a host of other factors. Create a standard that outlines what engineers should expect on a normal day, on an easy day and on a very hard day. These are the control groups, and the extreme day will be the stress test.

    Form a Hypothesis

    Where will the system break? If you’re running an application scenario such as doubling the peak traffic that even your worst day so far has seen, will your CPU maintain optimum utilization (or will the container provisioning engine smoothly deploy additional nodes) as in the variable control groups, or will it spike so severely that processes grind to a halt because there isn’t enough memory or network bandwidth left to manage the load?

    Execute a Real-World Event (But Contain the Blast Radius)

    Do something extreme, such as taking down a firewall that severs connectivity to one internet service provider. This will confuse the application as it tries to respond to requests with repeated failures, ramping up CPU processes as errors return from a dead network endpoint. Log events will mount, filling the database and saturating the backbone.

    Validate the Hypothesis

    What happened? Monitor utilization and network throughput during the test and see where the system fell over. Is it what you expected, or did something never previously considered take place? Did new chaos erupt from the fissures in your infrastructure? Stabilize, document and remediate.

    Never Stop Not Being Afraid

    Stressing a system to its absolute max—and a little bit further—to see where things go wrong allows you to understand steady-state behavior and error-handling, so you can fix it before something breaks in new and unexpected ways. What do traffic spikes look like? What are real-world events and their impacts on your organization?

    CE is not just for DevOps. It should be a systemic practice for load-testing (out of your comfort zone) to the point of failure. It’s a responsibility for more than microservices deployments and applies to all sorts of disciplines within the IT organization.

    — Michael Fisher

  • Why ITOps Is Moving to the Cloud

    Why ITOps Is Moving to the Cloud

    There’s been a lot of movement in the IT operations management (ITOM) business lately, from the acquisition of SignalFx by Splunk to the PagerDuty IPO, and all signs point to a Datadog IPO in the future. What’s with all this consolidation? I believe we’re seeing the rise of a future-state of ITOM; that is to say, it’s the rise of SaaS-based ITOM. And it’s easy to see why.

    In my previous consulting career as lead enterprise systems architect, our team had an impeccable record in designing and implementing well-architected hybrid infrastructure solutions. We maintained an immaculate record and near-flawless customer satisfaction record. By project sign-off, our job was always done. And yet, returning to the same solutions six months later told a different story entirely.

    Well-architected solutions are similar to human bodies: They are perfect when they’re born but need constant care and feeding. These same solutions that satisfied SLAs, exceeded expectations and transformed organizational efficiency can easily degenerate, and just like our bodies have their nervous systems to monitor, brains to send alerts and tissue to self-heal, well-architected systems need operational maintenance to keep them humming.

    The traditional approach for solving this eternal need was and still is to design and implement well-architected IT operation management (ITOM) solutions, around a well-architected infrastructure. And yet, this is a self-fulfilling paradox because the ITOM solution itself needs the same care and feeding.

    There’s a Problem On-Premise

    ITOM is a broad term encompassing application and operating system (OS) performance, alerts, log management, notification, asset configuration, incident management and more. It typically involves the purchase of a suite of on-premise point tools addressing each need, and then to develop an internal framework to help those tools interoperate in a meaningful way. While that is possible conceptually, the facts on the ground reflect a very different reality:

    • Multi-vendor tools are often not designed to work together.
    • Creating an internal logical framework that orchestrates various teams and technologies can be very complex in large enterprises.
    • It’s near-impossible to create technical integrations flexible enough to adhere to inevitable organizational and technological changes that will affect this logical framework.
    • Predicting cost-of-ownership is nearly impossible since each tool is controlled by a different vendor, and the internal integration effort is often unknown.
    • Predicting the cost of the manpower required is also very difficult, as each tool requires its own set of specialists, in addition to integration specialists to make it all work together.
    • Upkeep is often overwhelming, as vendors offload software patches, upgrades and on-premises hardware costs to the customer.

    In the face of all these challenges, the end result is often unrealized value, overwhelmed operational teams, loss of service and inability to accommodate new technologies resulting business service disruptions.

    Why Cloud? Why Now?

    It’s historically been near-impossible to build a traditional ITOM platform on-premise. Vendors typically sell a collection of white-labeled tools cobbled together by acquisitions, and this is far from a platform. The complexity and the rate of technological change make it difficult to provide consistent quality and value across the various product lines. This puts the ITOps team squarely in the middle of a pickle: How can they ride the wave of a changing environment without relying on static tool suites?

    The Future is Flexible

    Enterprise ITOps has been stretched now more than ever. There is a serious skills gap, shortage in IT workforce and ever-increasing technical complexity. Time and resources are precious, and Enterprise ITOps need simplicity and predictability along with flexibility and control. Enter SaaS ITOM. By moving the ITOM function to a SaaS orientation, the responsibilities, workloads and daily tasks can transform according to the needs of the organization:

    Keeping Up with the Business

    SaaS ITOM can keep up with technological change, and keep pace with cloud, DevOps, artificial intelligence and more. In the world of SaaS, change is an accepted constant and not an inconvenience. What’s more, SaaS ITOM is infinitely more consumable than the tool suites of legacy past, and that reduces the learning curves associated with running ITOps.

    Keeping Up with Industry Needs

    A SaaS ITOM platform will be able to deliver a framework that’s both flexible and governed and can accommodate technical and organizational complexities. This agility is a feature of modern SaaS. SaaS ITOM can also integrate features running on a single code base supported completely by the SaaS vendor, who will absorb maintenance and upgrade cycles, freeing considerable and valuable time back to the operator. All of this results in a more predictable total cost of ownership, improved service quality and more value to the business user.

    It’s not news the world of IT is moving to the cloud. It is news, however, that cloud can offer such transformational benefits in ways we’ve never seen before.

    — Wael Altaqi

  • 5 Trends Transforming Digital and IT Operations Management

    5 Trends Transforming Digital and IT Operations Management

    In 2018, digital transformation ushered in a radical shift in how enterprises harness customer insights, technology capabilities and rapid experimentation to drive revenue growth, profitability and market leadership. Enterprises spent $1.3 trillion in 2018 on digital transformation technologies such as public cloud platforms, microservices and containers, edge computing, machine learning and artificial intelligence to improve customer experiences, business agility and employee engagement.

    Heading into 2019, digitalization will accelerate business model innovation and drive the adoption of these emerging technologies for sustainable competitive advantage. Here’s a closer look at five technology trends that will change the way enterprises approach digital operations management in 2019:

    Multi-Cloud Adoption Goes Mainstream

    In FY18 Q3, the three leading cloud platforms (Amazon Web Services, Microsoft Azure and Google Cloud Platform) accounted for 65 percent of the market share of public cloud infrastructure and platform services. Enterprises are increasingly relying on multiple cloud providers to avoid lock-in and harness innovative product offerings, global presence and flexible pricing strategies. When it comes to multi-cloud adoption in 2019:

    • Lift-and-Shift Will Give Way to Rebuilding. In the initial phase of enterprise cloud adoption, CIOs preferred “lift-and-shift” strategies for migrating complex, legacy applications to public cloud services. In 2019, enterprises will rebuild legacy portfolios to truly take advantage of the cloud’s elasticity, security and utility pricing. Rebuilding is not only the right approach to modernize legacy systems, but also a viable solution for reducing technical debt and unleashing enterprise agility.
    • The Rise Of Managed Private Cloud for On-Premises Requirements. While analyst firm Gartner predicts that 28 percent of enterprise spending will shift to public cloud offerings by 2022, the reality is that the vast majority of enterprise workloads still run inside the corporate firewall. Public cloud providers are taking aggressive measures to conquer the final bastion of data center gravity with private cloud offerings. Managed private cloud infrastructure will become essential for enterprise services that will need data sovereignty, latency or compliance, yet still want the flexibility and agility of cloud. Some recent managed private cloud announcements from the big three cloud platforms show how these providers are taking a pragmatic approach to meet customer demand:
      • In 2016, Andy Jassy, CEO of Amazon Web Services, declared, “Whether it’s 10 years or 20 years, very few companies will own their own data centers, and those that do will have a smaller footprint than they have now.” During his 2018 re:Invent keynote, Jassy sang a different tune by launching AWS Outposts to bring the power of cloud-native services to enterprise data centers for hybrid interoperability and seamless management.
      • During the 2018 Cloud Next conference, Google introduced its Google Kubernetes Engine (GKE) On-Premises that delivers an abstraction layer for running container clusters either on the public cloud or in a corporate data center.
      • In 2017, Microsoft announced the general availability of Azure Stack, which offers the same programmable interface and deployment tooling of Microsoft Azure in a private cloud environment for a consistent hybrid cloud experience.

    The Cloud-Native Skills Gap Widens

    Software containers have fast emerged as the standard deployment vehicle for agile, reliable, scalable and highly available enterprise services. The transition from virtual machine management to container orchestration and monolithic development patterns to agile microservices will transform the way enterprises build, deploy and optimize cloud-based applications. IT operations teams will need to develop a new set of competencies to manage the health and performance of containerized services. IT practitioners can longer rely on manual scripts to manage large, distributed systems but will need to embrace infrastructure as code to provision, deploy, monitor, alert and auto-scale cloud-native services.

    Commodity Monitoring Evolves to Strategic Observability

    DevOps teams will use multiple data sources (metrics, logs, and traces) to understand, verify and debug cloud-native systems. Modern observability requires the ability to consolidate and extract relevant insights from observability pipelines using tools such as Prometheus for monitoring, Jaeger for distributed tracing and Fluentd for logging. While IT teams will need new tools to manage autonomous cloud-native services, it is still critical to understand relationships between individual components and the overall system to effectively troubleshoot microservices in production.

    AIOps Emerges as the Way to Tame Alert Storms

    Artificial intelligence for IT operations (AIOps) is a clear necessity for modern IT infrastructure teams to keep up with the diversity, velocity and volume of IT operational and event data. IT teams can proactively prevent service outages, analyze real-time incidents using contextual knowledge and remediate problems using AIOps tools. Here’s how AIOps will help enterprises run and optimize mission-critical systems:

    • Improve Response Time For Digital Interactions. AIOps has the power to boost key metrics for incident management including mean-time-to-detection, mean-time-to-response, mean-time-to-restoration and incident volume handled within a service window. The combination of machine learning and data science techniques in AIOps not only delivers faster incident coordination and response, but also reduces the human time spent per alert with advanced analytics and probabilistic root cause analysis.
    • Eliminate Siloed/Redundant Processes. AIOps offers the ability to consolidate event and incident insights from different IT management tools across on-premises and public cloud environments. A shared AIOps platform offers centralized visibility, faster impact analysis and improved collaboration for a diverse set of stakeholders, including application owners, infrastructure teams and business sponsors.

    Business-Aware, Service-Centric Delivery Can Optimize Customer Experiences

    To help the business remain competitive, IT teams simply must transition from managing hybrid IT infrastructure assets to prioritizing and supporting business-critical services. Service-centric operations management combines business insights, customer knowledge and relevant metrics to improve digital performance, enhance customer interactions and support business goals. IT teams will need to focus on the business value of IT services while offering holistic infrastructure visibility and control at the business process level.

    Clear strategy and focused execution will separate the digital winners from losers in 2019. How enterprises embrace and respond to these key trends will be key to unlocking disruptive innovation, operational transformation and business value.

    — Deepak Jannu

  • Cost Per IT Ticket: Think Beyond Opex in Your Cloud Journey

    Cost Per IT Ticket: Think Beyond Opex in Your Cloud Journey

     Globally, organizations have embraced cloud computing and delivery models for their numerous advantages. Gartner predicts  the public cloud market is expected to grow 21.4 percent by the end of 2018, from $153.5 billion in 2017. Cloud computing services provide an opportunity for organizations to consume specific services with delivery models that are most appropriate for them. They help increase the business velocity and reduce the capital expenditure by converting it into operating expenditure.

    Capex to Opex Structure

    Capital expenditure refers to the money spent in purchasing hardware and in building and managing in-house IT infrastructure. With cloud computing, it is easy to access the entire storage and network infrastructure from a data center without any in-house infrastructure requirements. Cloud service providers also offer the required hardware infrastructure and resource provisioning as per business requirements. Resources can be consumed according to the need of the hour. Cloud computing also offers flexibility and scalability as per business demands.

    All these factors help organizations move from a fixed cost structure for capital expenditure to a variable cost structure for the operating expenditure.

    Cost of Assets and IT Service Management

    After moving to a variable cost structure, organizations must look at the components of its cost structure. They include the cost of assets and the cost of service or cost of IT service management. Cost of assets show a considerable reduction after moving the entire infrastructure or assets to cloud. The cost of service remains vital as it depends on the day-to-day IT operations and represents the day-after-cloud scenario. The leverage of cloud computing can only be realized if the cost of IT service management is brought down.

    Growing ITSM or ITOPs Market and High Stakes

    While IT service management (ITSM) has taken a new avatar as IT operations management (ITOM), incident management remains the critical IT support process in every organization. The incident response market is expanding rapidly as more enterprises are moving to cloud every year. According to Markets and Markets, the incident response market is expected to grow to $33.76 billion by the year 2023 from $ 13.38 billion in 2018. The key factors that drive the incident response market are heavy financial losses post incident occurrence, rise in security breaches targeting enterprises and compliance requirements such as the EU’s General Data Protection Regulation (GDPR).

    Service fallout or service degradation can impact key business operations. A survey conducted by ITIC indicates that 33 percent of enterprises reported that one hour of downtime could cost them $1 million to more than $5 million.

    Cost per IT Ticket: The Least Common Denominator of Cost of ITSM

    As organizations have high stakes in ensuring that business services run smooth, IT ops teams have additional responsibility in responding to incidents faster without compromising on the quality of service. The two important metrics for any incident management process are 1) cost per IT ticket and 2) mean time to resolution (MTTR). While cost per ticket impacts the overall operating expenditure, MTTR impacts customer satisfaction. The higher the MTTR, the more time it takes to resolve the tickets and, hence, the lower customer satisfaction.

    Cost per ticket is the total monthly operating expenditure of the IT ops team (IT service desk) divided by its monthly ticket volume. According to an HDI study, the average cost per service desk ticket in North America is $15.56. Cost per ticket increases as the ticket gets escalated and moves up the life cycle. For an L3 ticket, the average cost per ticket in North America is about $80-$100+.

    Severity vs. Volume of IT Tickets

    With our experience in managing the cloud IT ops for our clients, we understand that organizations normally look at the volume and severity of IT tickets. They target the High Severity and High Volume quadrants to reduce the cost of the tickets. However, with our experience, we strongly feel that organizations should start their journey with the low hanging fruits such as the Low Severity tickets, which are repeatable in nature and can be automated using bots.

    In the next blog, we will elaborate on this approach that can help organizations in measuring and reducing the cost of IT tickets.

    — Neeraj Deuskar