How To Guide for a Fully Automated Financial Services Data Center - Special

Financial Services (Banking and Insurance)

 Project Overview:

In addition to automating infrastructure, we will now include the monitoring and management of all critical banking applications. This will ensure that everything from transaction processing to customer-facing applications is fully integrated into the automated system, creating a truly comprehensive solution.

Step 1: Integrating Application Monitoring into the Data Center

1.1 Identifying Critical Applications:

• Applications to Monitor:
• Transaction Processing System: Handles all banking transactions, including deposits, withdrawals, and fund transfers.
• High-Frequency Trading Platform: Used for algorithmic trading in the stock market.
• Customer Relationship Management (CRM): Manages customer data, interactions, and support services.
• Core Banking System: The backbone of all banking operations, managing accounts, loans, and compliance.
• Insurance Claims System: Processes insurance claims, ensuring accurate and timely settlements.
• Naming Convention:
• Transaction Processing System: FINCORP-APP-TRANSPROC
• High-Frequency Trading Platform: FINCORP-APP-HFT
• Customer Relationship Management: FINCORP-APP-CRM
• Core Banking System: FINCORP-APP-CORE
• Insurance Claims System: FINCORP-APP-INSURANCE

• 2.1 Application Performance Monitoring:

  • Objective: Continuously monitor the performance and availability of critical applications.
  • Setup:
  • Use Python scripts with specialized libraries like psutil and requests to monitor application health, including CPU usage, memory utilization, and response times.
  • Integrate with APM (Application Performance Management) tools such as Dynatrace or New Relic for deeper insights.

• 2.2 Incident Detection and Correlation with Moogsoft:

  • Objective: Detect and correlate application-specific incidents.
  • Setup:
  • Configure Moogsoft to receive application alerts and correlate them with hardware issues. For example, if high CPU usage on FINCORP-APP-HFT coincides with a hardware alert, Moogsoft will treat this as a single incident.

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• Step 3: Automated Incident Response for Applications

  3.1 AI Operator for Application Management:

  • Objective: Extend the AI Operator’s capabilities to manage application-specific incidents.
  • Setup:
  • Expand the AI Operator's logic to include application performance metrics. For instance, if the response time for FINCORP-APP-TRANSPROC exceeds a threshold, the AI Operator will automatically reallocate resources.

• 3.2 Automating Failover and Redundancy for Applications:

  • Objective: Ensure that applications continue to run smoothly even during server failures or high load.
  • Setup:
  • The AI Operator triggers failover procedures for critical applications like FINCORP-APP-CORE by automatically switching to backup servers using VMware vSphere.

  Example Failover Scenario:

  • Scenario: The primary server running FINCORP-APP-CORE experiences a hardware failure.
  • AI Operator Action:
  • Detects the failure.
  • Initiates failover to a secondary server in a different geographic location (e.g., London).
  • Updates ServiceNow with a detailed log of actions taken.

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  Step 4: Application Upgrades and Patching

  4.1 Automated Patch Management

  • Objective: Ensure all applications are automatically updated with the latest security patches, tested, and deployed with minimal downtime.
  • Setup:
  1. Create a Patch Management Schedule:

• Rolling Upgrades for Zero Downtime

  • Objective: Automate the upgrade process to ensure continuous availability without affecting user access.
  • Setup:
  1. Automate Rolling Upgrade Process:

• Step 5: Proactive Application Performance Tuning

  5.1 Predictive Analytics for Application Performance

  • Objective: Automate the prediction of performance degradation and adjust resources accordingly.
  • Setup:
  1. Collect Historical Data and Train AI Models:

• Step 6: Comprehensive Compliance and Security Monitoring

• Bonus

Simulating High CPU Usage:

• Objective: Test the system's ability to handle and respond to a spike in CPU usage.
  • Trigger: Simulate a scenario where the high-frequency trading application (FINCORP-APP-HFT-TEST) experiences a sudden surge in trading activity, causing CPU usage to spike above 90%.
• Scenario Setup:

python

Rohan code

import random

import time

def simulate_high_cpu_usage(app_name):

    cpu_usage = 50

    while cpu_usage < 95:

        cpu_usage += random.randint(5, 15)

        print(f"Simulating high CPU usage for {app_name}: {cpu_usage}%")

        time.sleep(5)

        monitor_system_response(app_name, cpu_usage)

def monitor_system_response(app_name, cpu_usage):

    if cpu_usage > 80:

        print(f"CPU usage alert for {app_name}: {cpu_usage}%. Triggering resource reallocation...")

        allocate_resources(app_name, "CPU", 20)

simulate_high_cpu_usage("FINCORP-APP-HFT-TEST")

• Expected Outcome: The AI Operator should detect the CPU spike, allocate additional CPU resources, and ensure the application remains responsive.