For many businesses, the transition to the cloud hasn’t exactly been smooth. While the cloud allows organizations to instantly deploy virtual machines (VMs) and other resources in the management of their cloud environment, it has also become a complex, hybrid environment spanning new and legacy technology that continuously generates metrics and log data.
Digitalizing businesses typically have monitoring tools for each container, microservice, component, and cloud services that figures into their overall cloud deployment, but they can’t correlate between them and see interconnected issues. The cloud environment has effectively become a black box with digital businesses struggling to access all of their data.
When it comes to operating, monitoring, and troubleshooting services, black boxes make common activities harder. Any and all downtime or performance issues can have an immediate and significant impact on the delivery of business services and the customer experience, not to mention workplace productivity and company revenue. IT Operations teams need to know more than just the mere existence of containers; they need deep visibility into containers to prevent cloud issues from affecting the organization, making comprehensive monitoring a critical piece of their cloud setup.
To prevent the cloud environment from becoming a black box, digitalizing businesses can look to another technology that’s soon to become just as mainstream as the cloud: artificial intelligence. Artificial intelligence, and specifically, AI-powered monitoring tools have shown early promise in mitigating the black box that’s typical of cloud environments.
Containers have gained prominence as the building blocks of microservices. The speed, portability, and isolation of containers have made it easier for developers to embrace the microservice model that has become the norm. But, those containers are typically black boxes to most of the systems that live around them.
In comparison, an AI-powered monitoring tool would have the ability to monitor the performance of all cloud service components without serious performance impact on any logs and metrics. It would reduce comprehensive monitoring to not just one tool, but one dashboard.
IT Operations teams would get central, instant, deep-level visibility across all of the components of their cloud deployment, including all applications, microservices, and the full picture of dynamic infrastructures in real-time, plus automatic updates on any changes to your environment like VM migrations.
Increasingly, software deployment requires an orchestration system to “translate” a logical application blueprint into physical containers. Some argue that the orchestration system is even more important than the containers. The actual containers matter only for the short time that they exist while your orchestration matters for the life of its usefulness.
AI-powered monitoring solutions leverage orchestration metadata to dynamically aggregate container and application logs and metrics. Depending on your orchestration tool, you probably have multiple layers that you’d like to drill into. With an AI-powered solution, you have access to the logs and metrics you need to aggregate container and application data. Your orchestration system would no longer be a black box.
In cloud environments, APIs are the only elements of a service that are exposed to other teams. As a result, API monitoring is critical for the organization’s SLA. An AI-powered monitoring solution would go beyond binary up-and-down checks. It would automatically understand the most frequently used endpoints and response times since these can be indicative of significant problems or point to areas that need the most optimization in your system. While APIs are among the more transparent systems in a cloud environment, an AI-powered monitoring tool would add contextual analysis to APIs and their relationships to containers and orchestration systems.
With an AI-powered monitoring tool, IT Operations teams would get full operational insights without any manual configuration. The AI-powered monitoring tool would be able to detect and isolate anomalies right away because it would instantly understand the organization’s deployment and therefore anything that veers from it. The instant setup means visibility; cloud environments never have to be a black box.
Root Cause Analysis
In addition to collecting, visualizing, and aggregating logs and metrics that are specific to cloud services, any AI-powered monitoring solutions’ anomaly-detection engine would correlate events with other systems or software in the stack and send contextual alerts. Plus, it could borrow from a proprietary database of issue resolutions to recommend a possible solution.
With instant visibility into the the relationship between the host logs and network metrics, teams would be able to see how problems on a given host affect the whole network (and possibly other hosts). IT Operations teams would be able to determine exactly which part of the system has an error and get a recommendation on how to resolve it. Digital businesses would see all of the relevant information in an interactive infographic that visualizes the root-cause analysis - the opposite of theory.
Loom Systems delivers an AIOps-powered log analytics solution, Sophie,
to predict and prevent problems in the digital business. Loom collects logs and metrics from the entire IT stack, continually monitors them, and gives a heads-up when something is likely to deviate from the norm. When it does, Loom sends out an alert and
recommended resolution so DevOps and IT managers can proactively attend to the issue before anything goes down.
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