Cloud Resource Allocation Using Log-Based Runtime Prediction
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Solution Overview
Problem
Users face inefficiencies in selecting cloud infrastructure configurations, leading to long run times and high costs due to sub-optimal resource allocation and scheduling, which is exacerbated by the NP-hard nature of compute job scheduling and the lack of consideration for application interactions.
Innovation Solution
A method and system that extracts information from log files to determine optimal cloud resource allocation and scheduling by analyzing task data, cloud settings, hardware information, and economic and reliability data, allowing for automated or user-guided selection of cloud resources based on predicted run times and costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual testing of all cloud infrastructure configurations is performed to find optimal settings, then resource allocation accuracy is improved, but time consumption and testing costs increase significantly
Solution Approach 1:
The system performs preliminary analysis of application log files to extract performance characteristics and resource usage patterns before actual deployment. This preliminary action creates a foundation for predicting optimal resource allocation without requiring exhaustive manual testing of all configurations.
Solution Approach 2:
Instead of physically testing all possible cloud infrastructure configurations, the system creates a predictive model that copies and simulates the behavior of different configurations based on log file data. This allows the system to evaluate multiple configurations virtually without the time and cost of actual deployment testing.
2Measurement precision
If exhaustive testing of all cloud configurations is performed, then optimal resource selection is improved, but testing costs exceed actual application running costs
Solution Approach 1:
The system uses log file data to create virtual copies and simulations of different cloud configuration performances. By analyzing patterns in existing log data, the system can predict how applications would perform on different configurations without actually deploying and running tests on each configuration, thereby avoiding excessive testing costs.
Solution Approach 2:
The patent replaces the mechanical process of actual deployment and runtime testing with an information-based predictive system. Instead of physically provisioning and testing resources, the system substitutes this with computational analysis of log files and predictive modeling, eliminating the need for costly actual testing runs.
3Reliability
If cloud resources are over-provisioned to ensure adequate performance, then application performance is maintained, but resource utilization efficiency decreases
Solution Approach 1:
The system dynamically adjusts resource allocation parameters based on actual application behavior patterns extracted from log files. By analyzing historical data, the system identifies the precise resource levels needed for optimal performance and adjusts provisioning accordingly, avoiding both over-provisioning and under-provisioning.
Solution Approach 2:
The system implements a feedback mechanism where log file data from actual application runs is continuously analyzed to refine resource allocation predictions. This feedback loop allows the system to learn from actual performance data and progressively improve resource allocation accuracy, ensuring reliable performance while maximizing utilization efficiency.
Data Source
AI summary
A method for performing scheduling includes extracting information from at least one log file for an application. The method also includes determining an allocation of cloud resources for the application based on the information from the log file(s).