devopsy.kinsta.cloud

Author: Michael Fisher

  • Incident Resolution for Remote Teams

    Incident Resolution for Remote Teams

    People working in IT support and incident management right now are faced with unusual difficulties supporting large remote workforces and managing unpredictable workloads. On Reddit, system admins and other IT pros are bemoaning the hiccups and hassles of working in isolation while trying to resolve issues and maintain high SLAs. You can’t go grab your indispensable SME for troubleshooting, because that person is also home and inundated with messages and alerts from many different tools.

    Networks, VPNs and servers are being hammered extra hard right now from higher usage of SaaS apps and collaboration tools. User expectations are even more grandiose than normal: The home-based IT environment is a lifeline to whatever remains of human interaction. IT organizations are expected to make applications and services work flawlessly–overnight. Undoubtedly, IT pros are working with assiduity to keep the wheels of work moving for their employers during a time of crisis. Yet, working longer hours isn’t the answer. We need new ways of working—ones which may prove better than the current status quo for the long term.

    In many organizations, the process of incident management and resolution begins with people looking at screens full of metrics. You see a spike in something, such as network I/O, and immediately begin to investigate it, assuming the worst. A more adverse scenario is when users call out problems on Twitter, Reddit or other popular tech communities, creating a noxious PR situation which IT and the business must swiftly respond to and manage. This ad hoc, reactive fire drill takes time, and resolving that particular issue may not be the most important item of the day. In software development, it’s akin to the bad practice of designing the architecture of a system first and then the user experience.

    Given everything that’s at stake in the foreseeable future of stay-at-home mandates, SREs and IT support technicians need to flip the incident management process. Rather than beginning with the metrics and working backwards to uncover the problem, if there even is one, start by looking for the most important problems to solve. Then, discover the resources and components which support that end-user process and finally, analyze the metrics which are most relevant. For example:

    1. A B-to-C provider of a streaming video service is now seeing a 200% increase in daytime traffic. This is resulting in sluggish load times for users and glitches. 
    2. IT has tagged the components beneath that service, so IT operators know exactly which VMs, databases and load balancers come into play. 
    3. Next, the operations team can identify the most relevant metrics, for instance CPU idle time, to measure and take steps to correct. 

    The Problem-First Approach

    It sounds so simple: flip the process. Yet, this is not the case. IT operators and SREs are not accustomed to thinking in terms of which problems to solve or prevent. Being proactive or predictive hasn’t been the common workflow.

    Here is how IT operations and DevOps teams can take a proactive approach to managing the availability and health of enterprise services.

    Coordinate Across Departments

    Identifying the most critical problems to solve right now is the hardest step and the one which will take the longest. Common problems these days are related to cost and scale: How do I right-size my environment to reduce costs or handle more workloads? Broaden your field of inputs to narrow down to the specific issue, such as slow page loads on the financial reporting website. Product managers, account managers and business unit leads who are the closest to the customer experience can deliver feedback on the top issues affecting customer/user satisfaction. Teams should also review recent support tickets to identify common themes of pain.

    Fine-Tune Your Approach to Metrics

    The Utilization Saturation and Errors (USE) Method is one way to approach the problem-first incident management process. As detailed by Brendan Gregg, a senior performance architect at Netflix, this methodology begins by posing questions, seeking answers and working backwards to the metrics. For each resource that you want to measure, identify three metrics: one for utilization, one for saturation and one for errors.

    “The USE Method has made you aware of what you didn’t check: What were once unknown-unknowns are now known-unknowns,” Gregg explained.

    Standardize Workflows

    Create a common process for incident management across all your teams. Without the advantage of having almost everyone in the same room to huddle together in an ad hoc fashion when big issues crop up, it’s imperative to institute clear steps and roles. Doing so will prevent the frustrations, confusion and oversights that needlessly delay resolution. Since most incidents are composed of multiple contributing factors, teams need to adopt a small number of user-friendly tools to document and organize the information. At our company, we’re now using tools such as Miro, an online whiteboard application, to replace our physical whiteboarding sessions. Of course, there’s also Zoom, Slack, Jira and a host of other cloud-based tools already in place at many organizations. Mandate which tools everyone should use, with some guidelines on how to use them.

    Increase Automation

    In some organizations, scaling requirements in response to demand have increased tenfold. Automation is playing a critical role now; moving away from a web GUI, for example, is more scalable and aligns with modern tools such as Chef and Puppet. User tickets can be auto generated, for instance, from emails and linked to code management systems like GitHub. Modern development and operations teams are also expanding automation in unit testing and provisioning. 

    Watch for Burnout

    Whether there’s more work and/or a need to fill the hours during long quarantine days, many software engineers, testers and architects are working longer days right now. Yet, exhaustion and burnout can lead to errors and oversights along with low morale. It’s up to managers to make sure that employees are taking breaks, working reasonable days and having the time and energy to attend to personal needs.

  • 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