Community Spark 💡: Orchestrating Parallel Processes with Intelligent Automation

Finding ways to efficiently manage multiple processes running in parallel is a common challenge in automation. When each process has unique input requirements and needs to run simultaneously, the task becomes even more complex. Here’s how I tackled this problem using Automation Anywhere. 

Problem Statement 

Managing 25 distinct processes that need to run in parallel, each requiring unique inputs, was a major challenge. Using a traditional workload manager wasn't feasible. Ensuring efficient use of client VMs and delivering outputs simultaneously added to the complexity. 

Solution Overview 

I designed an orchestration bot leveraging Automation Anywhere's APIs to handle this challenge. I created a mapping file defining the allocation of VMs for each process (e.g., Process A gets up to 2 VMs, Process B up to 3 VMs). The orchestration bot dynamically assigns processes to available VMs and queues tasks based on priority when dedicated VMs are occupied. This approach optimized resource utilization and ensured timely process completion. 

Implementation Considerations 

• VM Allocation Mapping: Create a mapping file to allocate VMs for each process, ensuring efficient distribution and prioritization. 

• Dynamic Process Deployment: Use Automation Anywhere APIs to dynamically deploy processes to idle VMs, maximizing resource utilization. 

• Batching and Queuing: Implement batching of input data and queuing processes based on priority to manage VM availability effectively. 

• Real-Time Monitoring: Continuously monitor VM performance and reallocate resources as needed to maintain optimal efficiency. 

• Compliance with Policies: Ensure adherence to organizational policies and guidelines for data handling and process execution. 

Key Benefits 

• Optimized Resource Utilization: 

  • Dynamic VM allocation minimizes idle times and maximizes efficiency. 
  • Metrics: Track VM utilization rates and process completion times. 

• Enhanced Process Management: 

  • Prioritizing and queuing processes ensure timely execution of critical tasks. 
  • Metrics: Monitor process queue lengths and execution times. 

• Scalability and Flexibility: 

  • Easily scale the solution by adding new processes or VMs as needed. 
  • Metrics: Measure scalability by tracking the number of processes and VMs managed 

Mohd Saqib is Lead RPA Developer at Autech ! Connect with Saqib in LinkedIn