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7 Ways AI Can Come to the Rescue for Your OpenStack Monitoring



The cloud industry is rapidly changing and many companies are shifting to virtual private networks (VPNs). Managing cloud resources including storage and computing power has become easier due to software applications such as OpenStack. OpenStack is one of the best software out there which helps data center owners to deploy virtual machines instantly and monitor logs either through a dashboard or an OpenStack API (Application Programming Interface). The good Advantage is that OpenStack enables digital businesses to create, evaluate and control their computing environment easily. However, the constant need of making critical decisions, whilst dealing with a vast amount of log data generated in an OpenStack setting remains a serious challenge.

Challenges with OpenStack

Thousands of businesses around the world use OpenStack due to its rich ecosystem, low operational costs, and agility to cut their time to market. However, the users encounter major challenges while deploying this technology especially when it comes to identifying and monitoring resources. Below, we will focus on the challenges you can face with OpenStack monitoring tools and remedies to make OpenStack an easy solution for cloud computing and virtual machines integration.



Dynamic Environments

Most of the OpenStack services are completely static. A cloud computing environment is very flexible because it can grow and shrink depending on the number of services and applications deployed. Therefore, generating static logs in a dynamic environment limits the IT managers to a non-complete overview of what is happening inside their environments.


To refresh the statistics, human intervention is needed. in an environment where deploying containers and virtual machines occurs instantly, human intervention requires time. The magnitude of such challenges is even greater in legacy environments that run machines which aren’t rebooted for months or years. In such a scenario, there must be a capable team that which is required to constantly monitor new logs data. Naturally, this can be rather cumbersome and sometimes, a data center may lack the resources or the adequate manpower to do so.



To address the issue of a dynamic virtual machine environment, an AI-powered monitoring solution would be the best way to go. Artificial intelligence focuses on monitoring all OpenStack components in real-time. Also, any service that might have a crucial impact on the entire environment is automatically given the highest priority. At the end, IT experts are given an instant overview of what’s happening in each micro-service including logs of proprietary software applications as well as storage, network, computing and data-plan components. With such immediate and deep insights of the data logs solving any issue is a breeze because of the simplicity and connectivity across all micro-services.



Legacy Mixes

It comes as no surprise that most organizations are still running their old computing infrastructure on top of cloud services. In fact, more than 75% of organizations who use OpenStack deployments are yet to abandon their legacy applications or platforms from other organizations. These include Google and Amazon cloud services. With the legacy mixes, open source monitoring tools used by OpenStack may not be in a better position to completely generate enough logs to monitor hybrid environments. This compels companies to use multiple monitoring tools and remains a major headache for IT managers while still adding overhead to the organization.



Data centers are eager for hybrid and well-presented data logs which come from different nodes across a full virtual machine deployment. AI monitoring tools can evaluate and present data from disparate deployments especially if they have an API. In the end, tracing a problem across the network with AI- generated insights becomes straightforward and essentially effortless. AI can come to the rescue for IT managers to instantly view the relationships between different metrics and evaluate them in order to understand which components are causing the problem in case of an error.



Distributed and de-coupled nature of OpenStack

OpenStack’s environment compromises of micro-services designed to do many different tasks. However, each task exposes their end results using a REST (Representational state transfer) API. OpenStack's micro-components communicate with each other in order to achieve a common goal of delivering a (great) virtual machine using a messaging layer. The problem with such kind of approach is the isolation of failures from spreading to the entire infrastructure.

When errors occur in a single micro-service, monitoring and identifying the very service that is stopping the whole platform can be tricky and this can bring a whole machine to a partial or a complete halt making an entire service go offline. Can you imagine an entire web application fails due to a single micro-component that cannot be easily identified? Very alarming, to say the least. Isn’t it?



A non-monolithic service makes it very difficult for IT managers to assess the real impact on the entire computing environment when a single micro-component fails. To solve this uncertainty, one needs to understand how the cloud infrastructure works. You’ll need to invest heavily and learn all the functions of the different components that make up your could. Secondly, you’ll need to identify the relationships between the different components. Finally, you’ll have to deeply understand all services that can directly impact a specific cloud service. With this in mind, you will not only understand why a certain component has failed due to a non-functional micro-component, but you will also get meaningful insights on the other services which might be affected by the process. Therefore, you will minimize the risk of your cloud coming to a complete stop in case a few micro-services failures non-responsiveness.



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Disparate Structures and Folders

An OpenStack Cloud management setup relies on traditional monitoring tools which have lots of problems, to list one of them, The known“black box” issue: the end users see only the input and output, but not the process. In a real world, deployments of OpenStack are made in different geographic locations and structures. This means that numerous components such as hosting, cloud services, and network providers are involved in the process. On the other hand, different software applications are installed and each one of them requires tracking to ensure that the cloud services flow smoothly. With such a diversity of structures and applications, drawing meaningful insights with OpenStack is very challenging for IT managers.



Every organization needs to invest in Artificial intelligence (AI) The good thing without AI is the way it reduces complex logs to just one dashboard. This enables IT managers to get centralized data in order to get complete visibility over the entire computing environment. In simple terms, an end user of OpenStack technology will get every aspect of the logs generated by the computing, networking and storage components of the technology in a simplified and easy to understand manner. The data will be represented as a full picture in real time and automatic-updates on the environment can be easily monitored. This will assist to mitigate possible loss of data since all services will be up and running without possible deviations. Artificial intelligence relies on different combinations and permutations of algorithms to help IT mangers easily identify OpenStack monitoring problems.



Monitoring default metrics

OpenStack metrics and logs over-rely on default metrics for problem detection. These include memory leaks, file problems, deadlocks, performance results and others. Although such logs are very good when it comes to identifying common problems, they may fail in detecting complex predicaments that require specialized monitoring tools. Failure detection and prediction through use of common metrics may not always work as desired and sometimes, it can lead to false alarms. An example of ad-hoc problem detection is an alert raised when disk space is used to a certain limit e.g. 80%. This is not completely full proof because problems may not be detected until a serious performance degradation occurs.



You will need to utilize a complete failure identification methods and run different tests to make sure that certain services are okay. Don’t just rely on open source metrics, think outside the box and run all the tests that can help you identify if certain components have failed, degraded or if they simply need to be restarted.



TCP-Based services bottle necks and connection pool exhaustion

OpenStack is a distributed service and all of its core services expose themselves via a REST API. On the other hand, OpenStack's messaging services is TCP based and as such, it is very susceptible to major problems which include network and/or connection problems. Also, most of the OpenStack services connect to SQL (Structured Query Language) databases. Sometimes, the vast amount of data logs and metrics and can exhaust the connection pool available with each deployment.



OpenStack needs to have the right connection states in order to generate the right monitoring solutions. Also, make use of the right command line tools to check the state of end-point services in order to get the real state of what’s happening in the background. In other words, you need to develop a habit of extending monitoring solutions with tailor-made metrics which can be designed either through OpenStack APIs or other market-available open source tools. Most IT managers go wrong only when they over-rely on default metrics and logs.



And One Final Comment About Human Failures

As an IT manager, you need to follow the right procedures while managing a cloud or virtual machine infrastructure if you want it to run without bottlenecks. Sometimes, failure in an OpenStack environment might occur because the IT manager is failing in one way or another. Badly designed procedures or ignorance due to human factors can contribute to a colossal failure of the entire system.


Always test different scenarios if a problem occurs. Also, if there is a problem with a micro-component make sure you investigate the scenario in order to understand how it can largely affect the system as a whole. Remember, ignoring a single service may later cause loss of revenue or even cost you that job that you love dearly. Also, always respond to red-alarms in a monitoring dashboard. For instance, if the system displays that 95% of the disk is utilized, act immediately to add more space or simply upgrade your service if you are in a rented service.




OpenStack is a great technology that can help your business grow and keep data applications safe in the cloud. Such technologies have helped us immensely to deploy virtual machines in stable environments that are completely reliable. However, we need to remember that a machine on its own doesn’t “think” and we need to do our very best and take appropriate actions using metrics, logs and proper use of artificial intelligence based tools.


Monitoring without properly-timed actions to fix problems is vague in the computing world. As an IT manager, you need to be proactive and make sure you detect problems as early as possible. Then, apply the right steps to isolate the problem from spreading to other areas to avoid affecting the entire system. An OpenStack environment that has proper monitoring tools that connect well with the system’s micro-services to generate the right information for a creative and disciplined IT manager will win the battle of cloud computing. This also makes it easier to run and maintain a virtual cloud-environment without going above your budget.


reading to other areas to avoid affecting the entire system. An OpenStack environment that has proper monitoring tools that connect well with the system’s micro-services to generate the right information for a creative and disciplined IT manager will win the battle of cloud computing. This also makes it easier to run and maintain a virtual cloud-environment without going above your budget.


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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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