Artificial Intelligence : AI in IT Infrastructure Management

Overview of AI in IT Infrastructure Management

With the growth in technology and infrastructure, rapid advancements are there in software-defined infrastructure and Cloud Computing. This has enabled the IT Infrastructure to be flexible, intangible and on-demand. On the other hand, IT Infrastructure is not yet intelligent enough to understand the correlation between the IT elements, recognizing the data trends and further take the appropriate decisions. Therefore, Artificial Intelligence is introduced. It enables to access and manage the computing resources to train, test and deploy AI algorithms.

AI is all about making computers think like humans with customer interaction solutions. Taken From Article, Artificial Intelligence Adoption Best Practices

What are the Challenges for building AI Enabled IT Infrastructure?

The below highlighted are the major challenges of building Artificial Intelligence enabled IT Infrastructure.

Onboarding Data

Organize the data into logical partition applications and services.
Mapping the data into correct source types.
Reviewing the parsing process.

Personalize Artificial Intelligence

User Experience Alerts

Perform Anomaly detection.
Use the discovered anomalies and previous domain existing knowledge to create custom alerts based on factors affecting the customer’s experiences.

Customize Dashboards

Meaningful and iterative dashboards to monitor the IT stack.

Measuring employee performance and the hiring process also plays a vital role in identifying the right person for the right job.Taken From Article, AI Platform for HR and Recruitment Management

Technologies for Building AI-based IT Infrastructure Platform

The main technologies which are necessary for building AI based IT infrastructure platform are below:

Data Sources

Diverse and extensive data sources are used such as events, metrics, logs, different job data, tickets, monitoring, etc.

Big Data

Aggregation of IT data for historical analysis and real-time reaction and insights.

Computation and Analytics

Enable to generate the new data and metadata from given existing IT data.

Eliminate noise
Identification of patterns and noise
Isolate probable causes
Expose underlying problems

Artificial Intelligence Algorithms

Apply computation and algorithms efficiently and appropriately to expertise the machine and get desired outcomes.

Unsupervised Machine Learning

Automatic alteration and creation of new algorithms based on the output of the algorithmic analysis.
Introduction of new data into the system

Visualization

Presenting insights and recommendations in an easily consumable way.

Automation with Artificial Intelligence

Automatic identification of issues with the use of outcomes obtained from analytics and machine learning .

A new label for the tools that took machine learning capabilities and applied them to IT Operations space.Taken From Article, AIOps: Artificial Intelligence for IT Operations

AI-based Methods to Influence the IT Infrastructure Automation

The various AI based methods to influence the IT infrastructure automation and management are defined below:

Capacity Planning

With the use of AI, the workload can be mapped to the right configuration of servers and virtual machines.

Resource Utilization

With the combination of AI, it becomes possible for the system to predict the scaling in which the infrastructure will automatically adjust itself based on historical data.
No rules and configurations are required to enable elasticity.

Storage Management

Storage resources are monitored continuously for optimum utilization and performance.
By predictive analytics, the capacity of storage is automatically adjusted by adding new volumes proactively.

Anomaly Detection

Advanced machine learning algorithms are used to determine outliers effectively.
Real-Time root cause analysis.
Prevention of potential outages and disruptions faced by the infrastructure.

Threat Detection and Analysis

With the use of application of machine learning algorithms and heuristics, anomalies and risk events can be detected and avoided.

Impact of Artificial Intelligence on Information Management Services

The demand for greater resources
The necessity for AI in security
Intelligent Monitoring
Automated Support
Intelligent Storage
AI-defined Infrastructure Management

Infrastructure services ensure the plans, designs, and implement organizational IT strategies and manage mission-critical IT infrastructure.Taken From Article, IT Infrastructure Management Services

Key Features of AIOps

The Key feature of AIOps are listed below;

Automated behavior prediction

Based on the analysis of infrastructure, applications, and users, issues are predicted in advance that affects the availability and performance.

Root cause analysis

Identification and correlation of issues across the higher amount of data.

Data-driven recommendations

Better decision making based on real-time and historical data.

Digital Transformation

The better organization to achieve end-to-end visibility into infrastructure and applications.

Faster Deployment

Deploy automated actions for known events with embedded business logic.
Increase the speed of monitoring and performance issues.

Real-Time Analysis

Real-time analysis and diagnosis of issues using various algorithms.
Perform actionable insights.

Alerts and Notifications

Reduce the operational noise across the production stack.

DevOps and CloudOps Automation

Automatic monitoring the deployment of metrics.
Quickly invoke the issue detection rollbacks.

What are the Key Components of AIOps Platform?

Monitoring Ecosystem
Engagement Ecosystem
System of Record
System of Automation
Data Lake
Artificial Intelligence
Time Series Database
Time Series Analysis

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Artificial Intelligence for IT Infrastructure Use Cases

Use Cases for Artificial Intelligence in IT Infrastructure:

Incident Management

Artificial Intelligence for Incident management

Environment

Web-scale globalized infrastructure
Hybrid clouds
Heterogeneous technology stacks
More than ten monitoring tools

Challenges for Incident management

Managing the web-scale and hybrid cloud infrastructure
Managing millions of events per month
Event Analysis and Correlation
Mean-Time-To-Detect
Mean-Time-To-Resolve

Solutions for Incident management

Real-time Machine Learning Algorithms
Operational Noise Reduction
Advanced Event Correlation

Managing and Monitoring the IT Ecosystem

Challenges for managing and monitoring the IT Ecosystem

Lack of multi-tenancy for domain experts
Operational Noise and alert fatigue
Thousands of tickets per month

Solutions for managing and monitoring the IT Ecosystem

Automatically catch million of events
Automatic dispatch the hundred of solutions to the right experts without dependency on rules and topology of models.
Automate ticket generation
Kubernetes and Amazon Web Services
Smarter Architecture-Elegant Architecture
Central Data Collection and Analytics Engine
Optimize the distribution of servers automatically across the entire infrastructure
Automatic determination of correlation from the wide variety of sources along with infrastructure information

Cost Reports

Real-time Cost Analysis

Prediction of Issues

Automatic identification of issues that can impact the business.
Pattern detection to predict and prevent business outages, increase revenues, improve customer satisfaction, and provide business agility.
Dynamic threshold and multivariate anomaly detection.
Prediction of resources that will run out of capacity.