Make cloud operations efficient with AI/ML techniques and solutions

How to Keep Employees Motivated and Engaged

November 15, 2021

Health, wealth, knowledge, and collaboration: 4 essential areas where COVID-19 has accelerated digital adoption

February 20, 2022

1. Automating processes in an intelligent way

Static tooling has been a source of frustration for engineers responsible for production operations (from IT Ops to DevOps). Dynamic tooling enabled by machine intelligence and deep learning allows the implementation of automatic remediation steps and alarm diagnostics, which enables teams to focus on utilizing code to solve problems. Simple solutions like these may save hours of effort after each deployment by quickly resolving errors.

In addition, AI and machine learning can intelligently automate other areas of operations, such as:

Deployment operations with cluster management and auto-healing tooling
Application performance management by detecting potential failures and identifying the root causes
Log management through auto-detection of relevant anomalous events in real-time log streams
Incident management by suppressing noise from different alerting systems and identifying priority issues

Let’s take a look at how AI/ML-driven process automation impacts a few important KPIs in cloud operations:

MEAN TIME TO ACKNOWLEDGE:

When an issue is detected, IT teams must acknowledge the problem and identify who will resolve it. Machine learning algorithms can automatically decide who will address the issue and ensure the right people are up and working on it.

AUTOMATED VS. MAUAL RESOLUTION:

Machine learning algorithms can identify patterns, learn from past remediation measures taken, such as previous scripts executed, and automatically remedy the problem. This reduces the need for manual intervention.

USER REPORTING VS. AUTOMATIC DETECTION:

IT teams must detect and resolve issues before the end-user becomes aware of them and reports them to the company. AI/ML leverages dynamic thresholds for automated alert generation and escalations to remedy problems before the end-user is affected.

TICKET-TO-INCIDENT RATIO:

Often, tens or hundreds of tickets are raised for the same issue, especially if the anomalous event has a cross-stack impact. AI/ML correlates and groups the data generated from multiple IT environments to reduce the number of tickets, logs, and events. It also helps diagnose problems swiftly, which improves the efficiency of the service desk.

MEAN TIME TO RESOLVE/REPAIR (MTTR):

MTTR measures the average time required to repair faulty equipment. By diagnosing the root cause of the issue and escalating the problem to the right team of IT professionals, AI/ML solutions reduce the MTTR. Machine learning enables the system to identify the repeated issues and suggest or execute actions to resolve them.

MEAN TIME BETWEEN FAILURES (MTBF):

MTBF is the average time between system breakdowns. AI/ML helps improve MTBF by rectifying current issues and predicting potential future outages. AI/ML can also keep track of the number of assets that don’t meet configuration standards (e.g., wrong VM type, location, image, OS, tagging) by comparing the newly provided assets to the previous configuration and help stay compliant and maintain standards.

2. Prioritizing tasks - done smartly

Addressing significant issues is a multi-step procedure involving several departments, including the conventional network operations center, the IT support staff, and engineers. The complexity of managing and resolving difficulties grows as the number of problems grows. Fortunately, many of these "noisy notifications" are produced by well-known occurrences or trends.

AI and ML can filter out unnecessary warnings, suppress duplicate alerts, automate actions for known events, and identify trends to ensure effective alert management.

AI/ML-driven smart prioritization of tasks impacts a few important KPIs in cloud operations:

MEAN TIME TO DETECT:

Working on the correct alert will reduce the mean time to detect the issue. AI/ML detects patterns and groups events, sifts signals out of noise, and reduces event streams. This helps identify the critical alerts related to IT infrastructure performance.

SERVICE AVAILABILITY:

Machine learning algorithms analyze past data to predict and resolve potential network downtime and prevent business-critical outages, which increases service availability.

3. Reducing costs through resource optimization

Dynamic provisioning, auto-scaling of support, and a lack of garbage collection for idle cloud resources significantly impact cloud resource costs.

AI/ML solutions can detect cost spikes, provide deep visibility into who used what, and assist firms in intelligently handling these issues. For example, companies can automate the purchase of reserved cloud instances based on certain patterns and rules. AI/ML can also help in automating development instances which involve shutting them off over the weekend and switching them on again at the start of the week.

AI/ML-driven resource optimization impacts a few important KPIs in cloud operations:

EFFECTIVE COST PER RESOURSE:

For organizations, staying under their budget and running their infrastructure most efficiently is a major concern. AI/ML can help with garbage collection alongside continuous auto-scaling of the resources. This would directly affect the cost of the infrastructure in use.

PERCENTAGE OF INFRASTRUCTURE RUNNING “ON-DEMAND”:

AI/ML-driven automated solutions can control dynamic provision and infrastructure scaling through patterns and rules, which reduces the need for reserved instances.

4. Complying with security through better vigilance

How can businesses verify that every cloud resource has the right security compliance configuration while also satisfying regulatory standards like PCI-DSS, ISO 27001, and HIPAA?

By utilizing real-time event configuration management data from cloud providers, AI Ops assists enterprises in remaining compliant and reducing business risk. It can send out immediate notifications to provisioners (within milliseconds) and even perform an action like shutting down computers if compliance isn't fulfilled.

Let’s look at how AI/ML-driven vigilance solutions impact an important KPI in cloud operations:

NUMBER OF SECURITY LAPSES:

Most common security failures pertain to very common areas like open ports, Identity & Access Management (IAM), etc. AI/ML solutions can be modeled in several ways to cover all aspects of security checks multiple times in a day. They can also help the infrastructure remain compliant with laws governing data and privacy by tracking when and what data was used at what location and by whom.

As we have seen above, AI/ML helps prioritize activities in different areas such as process automation, service prioritization, cost reduction, and security compliance. AI/ML can impact different key areas of concern for cloud IT operations and provide significant organization-wide improvements and process enhancements. Organizations can benefit by tracking the relevant KPIs in different areas while also setting the appropriate targets and milestones to ensure process improvements are achieved through AI/ML.

RTS [

] offers solutions that help organizations undertake such process improvements and ensure continued progress in these areas. RTS has made significant investments over the years in building repeatable solutions to improve IT & Cloud operations.
RTS built Juno [

],an intelligent cloud management platform, to simplify the process of multi-cloud management and enable traditional IT operations to transform into DevOps and Site Reliability Engineering (SRE)-based operating models. Juno leverages AI/ML solutions to automate tasks and resolve ambiguity. Juno also:

Juno - an intelligent cloud management platform

1. Provides an intelligent automation layer that enables ITOps to automate the most repeatable tasks such as patching and vulnerability remediations. This increases overall availability and delivers a better experience.

2. Integrates a massively scalable data pipeline architecture that enables the deployment of ML models on Cloud operations data. This allows the creation of automated decision-making on IT Ops data, enabling quicker isolation and resolution of incidents.