Heavyweight Request Routing Using Timeout Prediction Models
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Heavyweight requests in cloud-based applications cause resource inefficiencies and timeouts, negatively impacting other users and needlessly consuming computing resources.
Innovation Solution
A model is generated to predict whether a request will timeout at an execution environment, using historical data, metadata, and operational metrics to distribute requests effectively, avoiding timeouts and resource waste.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the application continues to process heavyweight requests even after timeout errors are returned to users, then the application maintains processing continuity, but computing resources are needlessly consumed and other users' requests are deprived of resources
Solution Approach 1:
The system performs preliminary detection of heavyweight requests before they complete processing. By identifying requests that will exceed timeout thresholds in advance, the system can proactively terminate them before they consume excessive resources, while still maintaining processing continuity for other requests through automated detection and termination mechanisms
Solution Approach 2:
The system implements feedback mechanisms that monitor request processing status and resource consumption patterns. When a request is detected as heavyweight or approaching timeout thresholds, the system provides feedback to terminate or redirect the request, preventing further resource consumption while maintaining overall system reliability through automated control loops
2Loss of energy
If the gateway or network component enforces timeout thresholds to prevent resource waste, then resource efficiency is improved, but users experience extended waiting periods and timeout errors
Solution Approach 1:
The system performs preliminary detection and classification of requests as heavyweight or normal before they reach the timeout threshold. By identifying problematic requests in advance using machine learning models and request characteristics analysis, the system can terminate them proactively, preventing both resource waste and user waiting time associated with timeout errors
Solution Approach 2:
The system introduces an intermediary detection layer between the gateway and application processing. This intermediary component analyzes request characteristics, metadata, and operational metrics to predict timeout potential before requests are fully processed, enabling early termination of problematic requests while allowing normal requests to proceed without interruption
3Productivity
If a model is generated to predict timeout and distribute requests effectively, then resource utilization is optimized and timeouts are prevented, but system complexity increases
Solution Approach 1:
The system implements self-service through automated machine learning models that continuously learn from historical request data and operational metrics. The model automatically detects heavyweight requests, predicts timeout potential, and guides request distribution without requiring manual intervention or complex configuration, optimizing resource utilization while keeping operational complexity manageable through automation
Solution Approach 2:
The system utilizes parameter changes in request characteristics, metadata, and operational metrics as inputs to the machine learning model. By monitoring and analyzing changes in these parameters, the model can predict timeout potential and guide request distribution decisions, optimizing resource utilization through data-driven parameter analysis rather than complex system architecture
Data Source
AI summary
Systems and methods provide reception of a request to an application, determination of values of request characteristics based on the request, and determination that the request is a heavyweight request based on the values of the request characteristics. In response to determining that the request is a heavyweight request, execution environments capable of executing the application are determined, operational metric values of one of the execution environments are determined, and it is predicted that the request will not timeout at the one execution environment based on the values of the request characteristics and the operational metric values. In response to predicting that the request will not timeout at the one execution environment, the request is sent to the one execution environment.