Distributed Storage Framework for Seamless Version Upgrades
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Solution Overview
Problem
Traditional database structures and computing systems are inadequate for efficiently processing and analyzing large volumes of varied, sometimes unstructured, data due to hardware limitations such as insufficient processing capabilities and available memory, making it difficult to handle big data effectively.
Innovation Solution
A distributed computing cluster system is used to install and manage multiple versions of an information server platform on separate pools of nodes within the same physical cluster, allowing for concurrent operation and seamless transition between versions, while enabling efficient data sharing and storage using unique file directory locations distinguished by a resource manager tool.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a single version of the information server platform is installed on the distributed computing cluster, then resource usage is optimized, but updating to a new version requires uninstalling the old version which causes downtime and service interruption
Solution Approach 1:
The system segments the distributed computing cluster into multiple pools of computing nodes. Different versions of the information server platform are installed on different pools simultaneously. This segmentation allows version 1 to run on pool 1 while version 2 is installed on pool 2, enabling seamless updates without service interruption.
Solution Approach 2:
The system performs preliminary installation of the new version on a separate pool of computing nodes before the old version is removed. The new version is pre-configured and tested in the second pool while the first version continues to serve clients in the first pool, ensuring no service downtime during the transition.
2Reliability
If multiple versions of the information server platform are installed on separate pools of nodes, then seamless updates and concurrent operation are enabled, but the system complexity and resource allocation management increase
Solution Approach 1:
The system introduces a pool manager as an intermediary component that automatically manages resource allocation across multiple pools of computing nodes. The pool manager handles version distribution, node assignment, and load balancing, simplifying the management of multiple platform versions and reducing the operational complexity despite having multiple versions running concurrently.
3Productivity
If traditional database structures are used, then system simplicity is maintained, but hardware limitations prevent efficient processing of large volumes of big data
Solution Approach 1:
The computing system is segmented into multiple independent computing nodes organized in pools, each capable of processing data independently. This distributed architecture allows the system to handle large volumes of big data by dividing the processing workload across numerous nodes, overcoming the hardware limitations of traditional single-database systems.
Solution Approach 2:
The distributed computing cluster provides multi-functional capabilities by running multiple versions of the information server platform on different pools of nodes. This universal architecture can simultaneously handle diverse data processing tasks, analytical workloads, and service requests that would be impossible for traditional database structures to manage efficiently.
Data Source
AI summary
Embodiments of the invention are directed to a system, method, and computer program product for leveraging and improving distributed computing cluster architecture by installing a first version and a second version of an information server platform on separate pools of nodes within the same physical distributed computing cluster. The system generates unique file directory storage locations within the cluster allowing for the separate binaries associated with each of the versions to be accurately distinguished from one another by a resource manager tool. The two versions are able to coexist within the same physical storage location inside the distributed computing cluster. Concurrent version installations within the same cluster are advantageous for testing and verifying a newer version before ultimately uninstalling the older version in that a seamless transition between versions is achievable with uninterrupted service. Additionally, the storage structure allows for efficient sharing of data between domains within the cluster itself.