Resource Similarity Visualization via Minhash in Distributed Systems
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Solution Overview
Problem
The complexity of managing large numbers of resources in network-based services, such as virtual machine instances, data storage, and networking resources, due to the vast combinations of hardware and software configurations, makes it difficult to visualize and manage similarities and differences effectively.
Innovation Solution
A computer-implemented mechanism that generates a resource similarity visualization by collecting attribute values for hardware and software resources, using minhash values to indicate similarities and differences, allowing for proactive management actions like identifying potential failures or needed updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If network-based services utilize large numbers of server computers with unique hardware and software configurations to provide virtual machine instances and other computing resources, then the service can offer greater versatility and accommodate more customer needs, but the complexity of managing such a service increases extremely
Solution Approach 1:
The patent creates visual copies or representations of resource configurations through standardized profiles. Instead of managing the actual complex hardware and software configurations directly, the system creates simplified visual representations that capture essential characteristics, allowing administrators to manage resources through these copies rather than the underlying complexity.
Solution Approach 2:
The patent transforms complex configuration parameters into standardized, visual profile parameters. By changing the representation form from detailed hardware/software specifications to simplified visual profiles with key attributes, the system maintains versatility while reducing management complexity through standardized parameter sets.
2Adaptability or versatility
If the service includes tens or hundreds of thousands of unique combinations of hardware and software components, then more diverse resource instances can be provided, but it becomes extremely difficult to visualize and manage similarities and differences effectively
Solution Approach 1:
The system creates visual profile copies that represent resource configurations. These profiles serve as simplified representations that make similarities and differences visible through standardized visual formats, allowing administrators to detect and measure configuration variations without being overwhelmed by the underlying complexity.
Solution Approach 2:
The patent employs visual differentiation techniques where resources with similar configurations are grouped or highlighted together through consistent visual representations. This allows administrators to quickly identify similarities and differences across thousands of resources through visual patterns rather than examining individual configuration details.
3Measurement precision
If manual management methods are used for tracking and analyzing resource configurations, then detailed control can be maintained, but the process becomes extremely time-consuming and inefficient
Solution Approach 1:
The system enables automatic generation and maintenance of resource profiles without requiring manual intervention. Resources self-report their configurations, and the system automatically creates and updates visual profiles, maintaining precise tracking while eliminating the time-consuming manual processes of configuration management.
Solution Approach 2:
The patent replaces manual mechanical processes of configuration tracking with automated computational systems. Instead of administrators manually examining and recording hardware and software configurations, the system uses automated data collection and processing to generate visual profiles, maintaining precision while dramatically improving efficiency.
Data Source
AI summary
A distributed execution environment includes various resources, such as instances of computing resources, hardware resources, and software resources. Values for attributes of the resources are collected. The collected attribute values for the resources are utilized to compute minhash values for the resources that describe the similarity between the resources. The computed minhash values are then utilized to generate a resource similarity visualization that provides a visual indication of the similarity between the resources.


