Configurable Distributed Computing System Configuration Analysis
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Solution Overview
Problem
Configuring a distributed computing system (DCS) to optimize resource utilization and performance across multiple configurations is challenging due to the need for expertise from various specialists and the potential for sub-optimal or error-prone decisions, especially when involving cloud resources and complex user tasks.
Innovation Solution
Implementing virtual farm simulations to analyze and select the most efficient configuration by measuring performance metrics across different setups, including server load balancing, response times, and hardware utilization, using a configuration analysis module to automate the selection and configuration of resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple specialists are involved in configuring the distributed computing system, then the configuration can cover various expertise areas, but the decision-making process becomes complex and error-prone
Solution Approach 1:
The system performs self-configuration by automatically analyzing performance metrics from virtual farm simulations and selecting optimal configurations without requiring manual intervention from multiple specialists. The configuration module autonomously determines resource allocation, server settings, and system parameters based on simulated performance data.
Solution Approach 2:
The patent replaces the manual expert-based configuration process with an automated computational system. Virtual farm simulations generate performance metrics that feed into an algorithmic configuration selection process, substituting human expert judgment with data-driven automated decision-making.
2Manufacturing precision
If virtual farm simulations are performed to analyze multiple configurations, then the optimal configuration can be identified, but the analysis time and computational resources increase
Solution Approach 1:
The system performs preliminary virtual farm simulations to evaluate multiple configurations before actual deployment. By simulating performance metrics in advance across different configuration scenarios, the system identifies optimal settings beforehand, avoiding iterative trial-and-error after deployment.
Solution Approach 2:
The patent creates virtual copies of the distributed computing system (virtual farms) to simulate different configurations. These virtual replicas allow performance evaluation without affecting the actual production system, enabling parallel simulation of multiple configurations to accelerate analysis.
3Ease of operation
If manual configuration selection is performed without automation, then flexibility in decision-making is maintained, but the potential for errors and sub-optimal decisions increases
Solution Approach 1:
The system incorporates feedback loops where virtual farm simulation results provide performance metrics that feed into the configuration selection process. The configuration module continuously refines its selections based on simulated performance data, ensuring data-driven decisions that balance flexibility with accuracy.
Solution Approach 2:
The configuration process is segmented into distinct modular components: virtual farm simulation module, performance metric collection module, and configuration selection module. This segmentation allows each component to operate independently with defined interfaces, maintaining flexibility while reducing errors through modular design.
Data Source
AI summary
The subject matter of this specification can be implemented in, among other things, a method that includes accessing a plurality of target tasks for a computing system, the computing system comprising a plurality of resources, wherein the plurality of resources comprises a first server and a second server, accessing a plurality of configurations of the computing system, wherein each of the plurality of configurations identifies one or more resources of the plurality of resources to perform the respective target task of the plurality of target tasks, and performing, for each of the plurality of configurations, a simulation to determine a plurality of performance metrics, wherein each of the plurality of performance metrics predicts performance of at least one of the plurality of resources executing the plurality of target tasks on the computing system.