Auto Scaling Predictive Analytics Nodes via Queue Depth
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current predictive analytics systems lack the ability to dynamically scale input data sources and adjust processing resources in response to changing workloads, leading to potential timeouts and inefficiencies in processing requests.
Innovation Solution
A computer-implemented method and system for auto-scaling predictive analytics models, which determines the queue size and processing time for requests, adjusts the number of processing nodes, and outputs an auto-scaled model based on these factors to ensure timely processing without over- or under-scaling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the number of processing nodes is increased to handle more requests, then the processing capacity and productivity improve, but the system complexity and resource consumption increase
Solution Approach 1:
The system dynamically adjusts the number of processing nodes based on real-time queue depth and workload conditions. The auto-scaling mechanism continuously monitors system state and modifies resource allocation accordingly, transitioning between different operational configurations to optimize performance while managing complexity.
Solution Approach 2:
The system implements feedback loops where the current queue depth, processing time, and node performance are continuously monitored and fed back to the scaling decision logic. This feedback mechanism enables intelligent adjustments to processing node count, ensuring productivity improvements without uncontrolled system complexity growth.
2Speed
If the number of processing nodes is increased to reduce processing time, then the speed and responsiveness improve, but the cost and resource usage increase
Solution Approach 1:
The system dynamically scales processing nodes based on actual workload demands rather than maintaining a fixed high-capacity configuration. During low-demand periods, nodes are reduced to minimize resource consumption, while during peak demand, nodes are increased to maintain processing speed, thus optimizing the trade-off between speed and resource usage.
Solution Approach 2:
The system changes operational parameters (number of active processing nodes) based on monitored conditions such as queue depth and processing time thresholds. This parameter adjustment allows the system to adapt resource consumption levels to match actual processing needs, preventing unnecessary resource expenditure while maintaining required speed performance.
3Reliability
If fixed timeout values are used for request processing, then the system simplicity is maintained, but the reliability and user satisfaction deteriorate under varying workloads
Solution Approach 1:
The system dynamically adjusts timeout values based on current processing conditions and queue depth rather than using fixed timeouts. This dynamic adaptation ensures that timeout settings remain appropriate under varying workloads, improving request completion reliability without requiring complex manual configuration for different scenarios.
Solution Approach 2:
The system automatically determines and adjusts its own timeout parameters based on monitored performance metrics and current load conditions. This self-service capability eliminates the need for external manual configuration of timeout values for different scenarios, achieving improved reliability without proportionally increasing system configuration complexity.
Data Source
AI summary
A computer-implemented method for creating an auto-scaled predictive analytics model includes determining, via a processor, whether a queue size of a service master queue is greater than zero. Responsive to determining that the queue size is greater than zero, the processor fetches a count of requests in a plurality of requests in the service master queue and a type for each of the requests. The processor derives a value for time required for each of the requests and retrieves a number of available processing nodes based on the time required for each of the requests. The processor then auto-scales a processing node number responsive to determining that a total execution time for all of the requests in the plurality of requests exceeds a predetermined time value and outputs an auto-scaled predictive analytics model based on the processing node number and queue size.