Anomaly Detection in Data Centers

[pradeep-shukla]

Anomaly Detection in Data Centers

Use Case Description

Commercial data center environments consist of extremely large scale of physical devices, complex networks, critical applications and data. Individual applications like Hadoop MapReduce, data warehouse or SaaS applications can involve thousands of servers. Utility clouds like Amazon EC2 or Google Apps can serve more than 2 million businesses to run their own applications, each of which may have different workload characteristics. These facts make data center management a difficult task, especially in systems where malfunctions can lead to extensive losses in profit due to lack of responsiveness or availability. Some of the common challenges are as following:

1- Real-time Monitoring & anomaly detection
2- Capacity (Power, Cooling, Space) Planning
3- Energy Usage & Costs
4- Network security & performance
5- Productivity Management

Out of all the above challenges, this use case will focus on real time monitoring & anomaly detection. Solution design and additional details are discussed in the attached document.

AS-IS WORKFLOW, TO-BE WORKFLOW

Anomaly Detection in Data Centers.pdf

Other information about the use case

Industry categories for this use case: Compliance, Information Technology and Services, Operations

Skill level required: Intermediate

UiPath Products that were used: UiPath Studio, UiPath AI Center, UiPath Orchestrator

Other applications that were used: AWS DynamoDB, AWS Sagemaker, AWS Pinpoint

Other resources: https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.367.6557&rep=rep1&type=pdf

What is the top ROI driver for this use case?: Minimize risk and ensure compliance in operations