Object Memory Fabric Stream API for Big Data Performance
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current commodity hardware and software solutions are inadequate for meeting the demands of Cloud Computing and Big Data environments due to their complexity, inefficiency in managing processing, memory, and storage, leading to limitations in performance and scalability.
Innovation Solution
The implementation of an object memory fabric system that uses a stream application programming interface (API) to manage processing, memory, and storage as a unified entity, allowing for native creation and management of memory objects without I/O instructions, and enabling seamless communication and operation across multiple nodes with any topology.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If separate management of processing, memory, and storage is used in current commodity hardware, then system complexity is reduced through modular architecture, but performance and scalability are limited due to inefficiency in data movement and access
Solution Approach 1:
The patent merges processing, memory, and storage into a unified fabric architecture where data and processing elements are interconnected through a common communication medium. This integration eliminates the traditional separations between CPU, memory, and storage subsystems, enabling unified resource management and eliminating data movement bottlenecks between separate components.
Solution Approach 2:
The fabric architecture provides a universal communication infrastructure that serves multiple functions simultaneously - data transfer, processing coordination, memory access, and storage I/O. This multi-functional approach replaces separate dedicated pathways for each function, reducing overall system complexity while improving throughput.
2Ease of manufacture
If separate management of processing, memory, and storage is implemented, then ease of manufacture is improved through standardized components, but loss of time occurs due to multiple layers of software management and data movement overhead
Solution Approach 1:
The patent extracts the management functions from multiple separate software layers and consolidates them into a unified fabric manager that operates at the hardware level. This extraction of management overhead from the data path enables faster data access by eliminating software-mediated coordination between processing, memory, and storage subsystems.
3Device complexity
If traditional commodity hardware architecture is used, then device complexity is manageable through standard components, but adaptability is insufficient for cloud computing and big data environments requiring flexible resource allocation
Solution Approach 1:
The fabric architecture implements dynamic resource allocation where processing elements, memory, and storage can be dynamically assigned and reassigned based on workload requirements. The unified fabric enables elastic scaling and flexible configuration of computational resources, allowing the system to adapt to varying demands in cloud computing and big data processing environments.
Data Source
AI summary
Embodiments of the invention provide systems and methods for managing processing, memory, storage, network, and cloud computing to significantly improve the efficiency and performance of processing nodes. More specifically, embodiments of the present invention are directed to an instruction set of an object memory fabric. This object memory fabric instruction set can include trigger instructions defined in metadata for a particular memory object. Each trigger instruction can comprise a single instruction and action based on reference to a specific object to initiate or perform defined actions such as pre-fetching other objects or executing a trigger program.