Introduction to Artificial Intelligence For Infrastructure
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.
Challenges for Building Artificial Intelligence Enabled Infrastructure
- 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.
Meaningful and iterative dashboards to monitor the IT stack.
Technologies for building AI based Infrastructure Platform
Diverse and extensive data sources are used such as events, metrics, logs, different job data, tickets, monitoring, etc.
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
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.
Artificial Intelligence based methods to influence the Infrastructure
With the use of AI, the workload can be mapped to the right configuration of servers and virtual machines.
- 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 resources are monitored continuously for optimum utilization and performance.
- By predictive analytics, the capacity of storage is automatically adjusted by adding new volumes proactively.
- 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
Features of AIOps
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.
Better decision making based on real-time and historical data.
The better organization to achieve end-to-end visibility into infrastructure and applications.
- Deploy automated actions for known events with embedded business logic.
- Increase the speed of monitoring and performance issues.
- 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.
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
Artificial Intelligence For Infrastructure Use Cases
- Web-scale globalized infrastructure
- Hybrid clouds
- Heterogeneous technology stacks
- More than ten monitoring tools
- Managing the web-scale and hybrid cloud infrastructure
- Managing millions of events per month
- Event Analysis and Correlation
- Real-time Machine Learning Algorithms
- Operational Noise Reduction
- Advanced Event Correlation
Managing and Monitoring the IT Ecosystem
- Lack of multi-tenancy for domain experts
- Operational Noise and alert fatigue
- Thousands of tickets per month
- 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
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
A Cognitive Approach
To know more about Artificial Intelligence Solutions offerings for Infrastructure Industry we advise taking the following steps –
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