Cloud Resource Allocation Model for High-Throughput Computing
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Solution Overview
Problem
High-Throughput Computing (HTC) systems face challenges in optimizing resource allocation due to the dynamic nature of computing demands and the need for efficient scheduling in cloud environments, particularly in bioinformatics and high-energy physics fields, where large task histories require proper analysis to identify performance correlations.
Innovation Solution
A cloud resource allocation model that analyzes job histories to extract influential factors, applies these to subsequent resource allocations, and adapts through reanalysis, using statistical techniques like principal component analysis to normalize and select optimal task profiles for efficient resource allocation and task execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If job histories are analyzed to identify performance correlations, then resource allocation optimization is improved, but system complexity increases
Solution Approach 1:
The system performs preliminary analysis of job histories to extract influential factors before actual resource allocation occurs. By pre-identifying which factors (CPU, memory, disk, network) most significantly impact task performance through statistical analysis of historical data, the system can make optimized allocation decisions without complex real-time calculations during task execution.
Solution Approach 2:
The system continuously monitors actual task execution results and compares them against predicted performance based on allocated resources. This feedback loop allows the system to refine its factor extraction and influence index calculations over time, improving allocation optimization while managing complexity through iterative learning rather than requiring overly complex initial designs.
2Adaptability or versatility
If dynamic resource configuration is implemented to meet user demands, then service adaptability is improved, but provisioning time increases
Solution Approach 1:
The system pre-identifies and extracts the most influential resource factors from historical job data before users make allocation requests. By maintaining a pre-computed understanding of which factors drive performance for different task types, the system can rapidly configure resources without performing complex analysis during the provisioning process, thus maintaining both adaptability and speed.
Solution Approach 2:
The system changes its approach by focusing on a reduced set of key parameters (influential factors) rather than considering all possible resource configurations. By identifying and concentrating on the most impactful factors such as CPU capacity, memory size, and storage I/O characteristics, the system can quickly determine optimal resource allocations without exhaustively evaluating all possible configurations, thereby reducing provisioning time while maintaining high adaptability.
3Measurement precision
If multiple task profiles are analyzed and compared, then allocation accuracy is improved, but processing time increases
Solution Approach 1:
The system extracts only the most influential factors from the comprehensive set of available task profile parameters. Through statistical analysis of historical data, it identifies which factors (such as CPU usage patterns, memory requirements, disk I/O characteristics) have the greatest impact on task performance. By focusing analysis and comparison only on these extracted key factors rather than all possible parameters, the system achieves high allocation accuracy while significantly reducing the processing time required to analyze and compare task profiles.
Data Source
AI summary
Disclosed is a method of efficiently allocating resources in a cloud computing system of the High-Throughput Computing (HTC) field. In more detail, a cloud resource allocation model is proposed in which accumulated job histories are analyzed, a correlation between a performance factor and the job properties is extracted, a factor which is greatly influential in a task performance is identified based on the result of the analyzing and the extracting and is applied to subsequent resource allocations, and reanalysis is performed adaptively.