From on-premise data centers to cloud and converged architecture, IT operations have
evolved exponentially over the last decade with the help of companies such as Amazon,
Microsoft, and Google that have eliminated much of the heavy lifting related to installing
data centers, servers, managing networks, storage, and more.
This has led to a more widespread embrace of the DevOps philosophy—saving time and
improving performance and bridging the gap between engineers and IT operations.
However, certain anomalous events and issue alerts still require the attention of engineers.
The emergence of artificial intelligence (AI) and machine learning (ML) technologies allows
companies to use intelligent software automation to make decisions on known problems,
predict issues, and provide diagnostic information to reduce the operational overhead for
The following are the top areas of concern for cloud IT operations where AI/ML solutions
can deliver the biggest improvements:
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
In addition, AI and machine learning can intelligently automate other areas of operations,
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
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.
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
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”:
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:
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