Robot Auto-Scaling Using CPU and Memory Workload Feedback
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Solution Overview
Problem
The existing manual processes for provisioning robots in network infrastructure are costly and inefficient, especially when system requirements fluctuate due to changes in business process volume or demand, necessitating a need for automated scaling based on average task handling time and current work backlog to ensure Service Level Agreements (SLAs) are met.
Innovation Solution
A method and system for automatically scaling robots within a system infrastructure by monitoring CPU and memory utilization, adjusting the number of robots dynamically based on identified demand, and modifying infrastructure-as-code configurations to request additional servers through a continuous delivery pipeline, ensuring SLAs and key performance indicators (KPIs) are satisfied.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual processes are used for provisioning robots, then flexibility in handling system requirements is maintained, but operational costs increase and efficiency decreases
Solution Approach 1:
The system automatically provisions and scales robots based on monitored workload metrics (CPU utilization, memory utilization, work backlog) without requiring manual intervention. The automated robot scaler continuously monitors system infrastructure and dynamically adjusts robot provisioning to match demand, enabling the system to serve itself and eliminating the need for manual operational management.
Solution Approach 2:
The system implements continuous monitoring of CPU utilization, memory utilization, and work backlog metrics, using this feedback to dynamically adjust robot provisioning. The automated scaler receives feedback from performance metrics and SLA compliance status, then automatically scales robot resources up or down to maintain optimal system performance and meet service level agreements.
2Reliability
If the number of robots is increased to handle higher demand, then service level agreements are satisfied, but infrastructure costs increase
Solution Approach 1:
The system dynamically adjusts the number of robots based on real-time workload conditions and predicted demand. Rather than maintaining a static fleet size, the automated scaler continuously monitors performance metrics and SLA compliance, adjusting robot provisioning dynamically to match actual system needs, thereby maintaining reliability while optimizing resource utilization.
Solution Approach 2:
The system changes the parameter of robot quantity based on monitored workload metrics and SLA requirements. The automated scaler adjusts the number of active robots as a variable parameter, increasing when demand and work backlog require additional capacity, and decreasing when resources can be optimized, thus maintaining SLA compliance while controlling infrastructure costs.
3Speed
If manual robot provisioning is used, then infrastructure costs are easier to control, but response time to demand changes increases
Solution Approach 1:
The system automatically detects workload changes and provisions additional robots without manual intervention. The automated robot scaler continuously monitors system infrastructure metrics and work backlog, and when thresholds are exceeded or additional capacity is needed, the system self-provisions robots through automated server requests and infrastructure-as-code modifications, dramatically reducing response time to demand changes.
Solution Approach 2:
The system performs preliminary monitoring and preparation for scaling operations. The automated scaler continuously tracks workload metrics and maintains readiness to provision additional robots by pre-configuring infrastructure-as-code templates and maintaining relationships with server provisioning systems, enabling rapid deployment when demand increases without requiring manual setup procedures.
Data Source
AI summary
A method for automatically scaling a number of robots leveraging interactive sessions to be used within a system infrastructure, dynamically based on workload, is provided. The method includes: receiving a request for a number of robots to be provisioned within the system infrastructure; validating an availability of the requested number of robots; monitoring a CPU utilization and a memory utilization within the system infrastructure; adjusting the requested number of robots based on the CPU utilization and/or the memory utilization; and releasing the adjusted number of robots for facilitating use thereof to perform corresponding tasks within the system infrastructure.