Cloud Node Hardware Configuration Automation
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Solution Overview
Problem
Current cloud computing systems lack mechanisms for quickly configuring and deploying workload and workload container software across nodes, aggregating performance data, testing node processors with various architectures, modifying boot-time configurations, and selecting nodes based on desired network and hardware performance, leading to inefficient resource utilization and high costs.
Innovation Solution
A method and system that automate the configuration, deployment, and monitoring of workloads across a cluster of nodes, allowing for the selection of modified hardware configurations, deployment of performance tools, generation of retargetable synthetic test workloads, and modification of network configurations, enabling efficient resource use and cost optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cloud computing systems allocate more hardware resources to ensure performance, then reliability and performance are improved, but cost increases
Solution Approach 1:
The system dynamically modifies hardware configuration parameters (CPU frequency, memory allocation, storage capacity) based on monitored performance metrics. This allows the system to adjust resource allocation in real-time, ensuring adequate performance when needed while reducing resources during low-demand periods, thereby resolving the contradiction between reliability and hardware quantity.
Solution Approach 2:
The system implements a feedback loop where performance data from nodes is monitored, analyzed, and used to automatically adjust hardware configurations. This feedback mechanism enables the system to optimize resource allocation continuously, maintaining performance reliability while minimizing unnecessary hardware resources through data-driven decisions.
2Ease of operation
If manual configuration methods are used for deploying workloads, then ease of operation is maintained, but productivity and time efficiency deteriorate
Solution Approach 1:
The system enables self-service configuration where automated tools deploy workloads, monitor performance, and adjust hardware settings without requiring extensive manual intervention. Users can define high-level requirements, and the system automatically handles the complex configuration processes, maintaining operational simplicity while dramatically improving deployment productivity.
Solution Approach 2:
The patent replaces manual mechanical configuration processes with automated software-based systems. Configuration scripts, APIs, and automated monitoring tools substitute for manual hardware setup and manual performance tuning, eliminating time-consuming manual tasks while preserving ease of use through simplified interfaces.
3Reliability
If hardware configurations are optimized for specific workloads, then performance is improved, but adaptability to different workloads deteriorates
Solution Approach 1:
The system employs dynamic hardware configuration that can adapt to different workload characteristics in real-time. Rather than fixed configurations, the system continuously adjusts hardware parameters based on monitored performance data, allowing the same infrastructure to optimize for different workloads (e.g., compute-intensive, memory-intensive, storage-intensive) without manual reconfiguration, thus maintaining both performance and adaptability.
Data Source
AI summary
The present disclosure relates to a method and system for configuring a computing system, such as a cloud computing system. A method includes determining, based on a shared execution of a workload by a cluster of nodes of the computing system, that at least one node of the cluster of nodes operated at less than a threshold operating capacity during the shared execution of the workload. The method further includes selecting a modified hardware configuration of the cluster of nodes based on the determining such that the cluster of nodes with the modified hardware configuration has at least one of a reduced computing capacity and a reduced storage capacity.