Web-Based Clustered Math Engine Access via Queue Manager
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Solution Overview
Problem
The full power of grid-based or cluster-based computational systems is often inaccessible to entities without a computer grid or cluster of computers, limiting their ability to utilize the increased processing power and speed offered by distributed computing architectures.
Innovation Solution
A web-based interface is provided to a clustered math engine, allowing users to schedule and perform complex computations remotely without requiring ownership of a clustered math engine, utilizing a math queue manager and program host to manage computations and store programs, inputs, and outputs, enabling access to computational power through a standard web browser.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If a clustered math engine is deployed locally, then processing power and computational speed are improved, but device complexity and infrastructure requirements increase
Solution Approach 1:
A web server acts as an intermediary between users and the clustered math engine. The web server receives computation requests, forwards them to the clustered math engine, and returns results to users. This mediator approach allows users to access high computational power without directly managing the complex clustered infrastructure, resolving the contradiction between obtaining computational power and avoiding infrastructure complexity.
Solution Approach 2:
The system provides a web-based interface that replicates the functionality of a local clustered math engine through remote access. Instead of requiring users to deploy and maintain their own clustered math engine, they can access an identical computational capability through a web browser, eliminating the need for complex local infrastructure while maintaining full computational access.
2Productivity
If grid computing or clustered computing is used, then processing speed and parallel computation capability are improved, but accessibility and ease of operation deteriorate
Solution Approach 1:
The patent replaces the mechanical complexity of grid/cluster computing management with a web-based interface. Instead of requiring users to manually configure and manage distributed computing resources, the system uses standard web protocols and a graphical user interface to abstract away all complexity, allowing users to access high-speed parallel computation as easily as accessing any other web service.
Solution Approach 2:
The web server provides a universal interface that can serve multiple users and multiple types of computation requests through a single system. This multi-functional web-based access point allows any user with a web browser to access the clustered math engine, eliminating the need for users to have specialized knowledge or dedicated access to computing resources.
3Adaptability or versatility
If remote access to clustered math engine is enabled, then accessibility is improved, but system complexity increases
Solution Approach 1:
The system is segmented into distinct functional components: a web server handling user requests, a math queue manager prioritizing and routing computations, and the clustered math engine performing calculations. This segmentation allows each component to be optimized independently and simplifies the overall system architecture by clearly defining interfaces and responsibilities, making the remote access system more manageable despite its distributed nature.
Data Source
AI summary
A clustered computational system comprises a clustered computational engine, a program host, and a queue manager. The clustered computational engine comprises a plurality of clustered computers and is configured to perform computations. The program host stores a plurality of programs that define computations that can be performed by the clustered computational engine and a plurality of inputs to the programs. The queue manager is configured to determine when the program host has stored sufficient inputs to allow the clustered computational engine to perform a computation defined by the program and to schedule the performance of the computation by the clustered computational engine.