Subclustering Genealogical Data for Custom Cloud Resource Allocation
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Solution Overview
Problem
Existing historical content systems are inflexible and computationally inefficient due to their one-size-fits-all approach to storing and processing genealogical content items, leading to poor performance and wastage of computational resources.
Innovation Solution
The system generates data subclusters based on content type and allocates computing resources, such as virtual machines, on a per-subcluster basis, allowing for customized resource allocation and independent management of subclusters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a one-size-fits-all approach is used to allocate computational resources to all content items, then resource allocation is simplified and system complexity is reduced, but computational efficiency deteriorates and resource wastage increases
Solution Approach 1:
The patent segments content items into different subclusters based on their characteristics (e.g., access patterns, data types, computational requirements). Each subcluster is then allocated computational resources independently, allowing high-demand content to receive more resources while low-demand content receives fewer resources, thereby resolving the contradiction between simplified allocation and computational efficiency.
Solution Approach 2:
The patent applies local quality by allocating different levels of computational resources to different subclusters based on their specific needs. Instead of uniform resource distribution, each subcluster receives tailored resource allocation matching its actual computational demands, improving overall system efficiency while maintaining manageable complexity through automated segmentation.
2Ease of operation
If computational resources are uniformly allocated to all content items, then system management is simplified, but performance deteriorates for content items with varying computational demands
Solution Approach 1:
By automatically segmenting content items into subclusters based on their characteristics, the system maintains ease of operation through automated classification while improving performance through differentiated resource allocation. The segmentation process is handled by the system itself, preserving management simplicity while enabling performance optimization.
Solution Approach 2:
The patent implements dynamic resource allocation where computational resources are adjusted based on the specific needs of each subcluster. This dynamic approach allows the system to adapt resource distribution to varying demands while maintaining simplified management through automated decision-making algorithms.
3Reliability
If excessive computational resources are allocated to all content items, then system performance is improved, but resource wastage increases and computational efficiency deteriorates
Solution Approach 1:
The patent applies local quality by allocating computational resources according to the specific needs of each subcluster. High-performance content items receive adequate resources to maintain reliability, while low-demand items receive minimal resources, eliminating wastage. This localized resource allocation resolves the contradiction between performance and energy efficiency.
Solution Approach 2:
The patent changes the allocation parameter from uniform resource distribution to differentiated allocation based on content characteristics. By adjusting resource parameters dynamically according to subcluster needs, the system maintains performance for critical content while reducing resource consumption for less demanding content, thereby resolving the performance-wastage contradiction.
4Productivity
If computational resources are reduced for content items with low demands, then resource efficiency is improved, but system flexibility deteriorates
Solution Approach 1:
By segmenting content into subclusters, the system maintains flexibility through the ability to independently manage and allocate resources to each segment. Even though resources are optimized for each subcluster, the segmented structure allows the system to adapt to varying demands by adjusting resources at the subcluster level, preserving overall system flexibility while improving efficiency.
Solution Approach 2:
The patent implements dynamic resource allocation at the subcluster level, allowing the system to adapt resources to actual demands. This dynamic approach maintains flexibility by enabling independent adjustment of each subcluster's resource allocation based on changing conditions, while achieving efficiency through reduced resources for low-demand content.
Data Source
AI summary
The present disclosure is directed toward systems, methods, and non-transitory computer-readable media for generating data subclusters for content items stored in a genealogical content database and allocating computing resources to the subclusters on an individual, customized basis. For example, the disclosed systems segment stored content items according to content type to generate a number of type-specific subclusters of content items. In addition, the disclosed systems allocate different (numbers of) virtual machines of a cloud computing system to process data for each of the subclusters independently. The disclosed systems can allocate duplicate virtual machines (along with corresponding processing power and memory) to some subclusters for redundancy while allocating only a single set of virtual machines for others. In some embodiments, the disclosed systems can perform updates and/or other operations on data within the various subclusters using allocated virtual machines on an independent basis, irrespective of processes or operations for other subclusters.