Organizations today are under pressure to stay ahead and maintain IT applications and infrastructure optimally. That means their IT teams are tasked to make sure that functions move along smoothly while minimizing downtime. To keep the lights on, enterprises add whatever domain-specific tools they need. However, these tools are often reactive, and not nearly robust enough to handle complex application topologies. This challenge has always been a critical goal—but now it’s become even more complex in an environment of modern architectures, such as microservices, containerization, hybrid-cloud deployments and newer development methods like agile DevOps techniques.
As dynamic systems architectures increase in complexity and scale, IT teams face mounting pressure to track and respond to conditions and issues across their hybrid environments. The technology stack continues to expand, and overcoming issues quickly becomes urgent—even emergent—for ITOps and DevOps teams. IT operations, DevOps and SRE teams are all seeking greater observability into these increasingly diverse and complex computing environments—and they need it all in real time!
The unforgiving demands of today’s always-on, hyper-connected environment present a daunting challenge. A long mean time to recovery (MTTR) can be catastrophic, with disruptions that spell downtime and damages accruing at digital speeds. Lost moments mean lost opportunities and missed competitive advantage. Data can disappear, while security vulnerabilities open up for quick exploitation. These can massively impact the bottom line. Amazon alone has lost as much as $100 million for every hour of Prime Day downtime!
In a digitally transforming ecosystem, manual processes are the culprits lurking behind many delays. Modern cloud and data center infrastructure radically expands the surface of failures, while prolonging time to detect, triage, and remediate a problem.
Observe to detect
Observability enables measurement of a system’s current state based on the data it generates, such as logs, metrics, and traces. Across the enterprise, every hardware, software, and cloud infrastructure component and each discrete container, open-source tool, and microservice generates records of every activity. The goal of observability is to understand what’s happening across all these, so you can detect and resolve issues to keep your systems efficient.
Observability is often applied to improving the performance of multi-layered, distributed IT systems. It uses three types of telemetry data: metrics, logs, and traces to provide deep visibility and allow IT teams to be notified about underlying issues. Often called the Golden Triangle of observability, these three types of data enable IT operations to identify and diagnose outages and other systems problems, regardless of where the IT infrastructure is.
Tools such as AppDynamics, LogicMonitor, New Relic and Dynatrace all produce a wealth of telemetry data that provide a clearer understanding of application and infrastructure performance, if you can harness it and act on it. But most often, organizations run into the alert noise challenge: it is common and a best practice to observe more than you need, since IT teams are trying to prevent incidents from occurring.
In an era where cloud-native applications and infrastructure are continually emitting increasing amounts of observability data. organizations that can efficiently use their data to drive positive outcomes will come out on top.
Observability is about achieving visibility across IT systems and elevating technical data to the business with metrics. AIOps —artificial intelligence operations—, on the other hand, is about extracting meaning from that visibility. While they can exist separately, AIOps should be part of an observability loop as the next step to better observability practices.
These tools help by combining machine learning, task automation, performance monitoring, and event correlations.
AIOps analyzes data from across disparate sources to give DevOps and SRE teams a holistic view of everything going on in a complex, distributed IT environment. These tools have the ability to surface significant events likely to cause interruptions to the business. With lagging indicators at their fingertips, IT operations can get ahead of issues and outages that are likely to occur and reduce the severity or completely prevent problems before they happen.
Even with observability and AIOps, a key piece of the puzzle is shifting from a “reactive” stance to a “proactive” stance.
Auto-remediate for sharp insights & quick resolution
There’s little more frustrating than arising at 3 a.m. to troubleshoot an incident. You have to scramble to identify the scope, engage the right experts, and remediate across clouds. Your company is depending on you to handle the response efficiently to minimize any consequences within the communicated service-level agreements (SLAs) while continuously reducing MTTR.
Operations needs to refresh sometimes decades-old tools, systems, and processes to take on today’s unrelenting challenges. They must deal with alert fatigue and difficulty in tracking down the underlying root causes of issues. Other pain points are all too well-known: tight deadlines and heavy pressure to analyze and collaborate while coming to an agreement on remediation strategies and tactics.
An important way to enhance IT operational efficiency is to automate alert remediation. When an IT endpoint is observed, and an alarm is raised for something not functioning according to spec, it can be validated and diagnosed, and an auto-correction workflow glide into action in the nick of time. For example, if a service in automatic mode is not running, an automation can automatically restart it. Or when a disk drive fills up, temporary files in well-known locations can be safely deleted. With the right auto-correction mechanisms in place, you can decrease the risk of unexpected service downtime and lower MTTR. And rather than putting the engineering effort into exceeding four or five-nines reliability, many companies prefer to protect themselves instead with fewer outages and less downtime.
The general idea behind self-healing IT is to aggregate or combine self-service technology, artificial intelligence (via AIOps), machine learning, remote monitoring, and human agents when needed to detect, analyze, and resolve emerging problems before the end-user is even aware of something amiss. This preemptive resolution is the most logical and cost-efficient approach: monitoring capabilities powered by or coupled with AI can identify an emerging issue earlier in the development process. Once revealed, the problem can trigger an automated script, duly executed by the system, that will fix the problem on its own.
Scenarios can even become self-healing end-to-end, wherein alerts or issues are auto remediated across the process without needing manual intervention at all. In a self-healing auto-remediation incident response system, an event triggers automated, well-documented, and pre-tested healing workflows that are comprehensive and more error-free than a manual procedure could ever have been. Root causes can be ascertained, launching secure, auditable, orchestrated infrastructure actions across complex IT environments, eliminating the need for you to respond. Even the notifications are automated. No more 3 a.m. troubleshooting!
IT automation can eliminate the constant threat of disruption and downtime to overcome the MTTR challenge. While observability allows IT to detect looming abnormal behaviors, auto-remediation ensures the potential issue is handled promptly and effectively.
Resolve allows you to:
To find out more about how Resolve addresses alert overload with auto-remediation, book your free demo today.
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