Computational-Power Sharing Network Element for Distributed Task Allocation
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Solution Overview
Problem
In the era of big data, computational power is a critical resource, but centralized computing often requires significant time to complete tasks due to limited power, posing an urgent need for efficient computational-power sharing solutions.
Innovation Solution
A method for computational-power sharing is introduced, where a network element acquires demand and available power information to determine suitable cooperation sides for task completion, utilizing a transceiver, processor, and memory to facilitate control and data plane transmissions, enabling efficient task allocation and integration across multiple computing entities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If centralized computing is adopted, then computational power is concentrated and managed centrally, but computation time becomes considerable and efficiency decreases
Solution Approach 1:
The patent segments the centralized computational task into multiple sub-tasks that can be distributed to different computing entities. The network element divides the computing workload and allocates it to multiple cooperation sides, transforming a single-point bottleneck into a parallel processing system, thereby reducing overall computation time and improving efficiency.
Solution Approach 2:
The patent combines the computational resources of multiple independent entities into a unified distributed computing system. By merging the processing capabilities of multiple cooperation sides under coordinated management, the system achieves aggregated computational power that exceeds what any single entity could provide, thus improving overall productivity.
2Loss of time
If distributed computing is implemented, then computation time is reduced, but system complexity increases
Solution Approach 1:
The patent introduces a network element as an intermediary that manages the distributed computing system. This mediator handles task distribution, resource coordination, and result aggregation, shielding the complexity from both the computing entities and the end user. The intermediary maintains a simplified interface while managing the underlying complex distributed operations.
Solution Approach 2:
The network element performing computational-power sharing is designed with multi-functional capabilities, handling task allocation, resource management, communication coordination, and result integration. This universal design consolidates multiple management functions into a single entity, reducing the need for separate specialized components and thereby managing system complexity.
3Productivity
If multiple computing entities are coordinated, then computational power is enhanced, but information security challenges increase
Solution Approach 1:
The network element acts as a secure intermediary that mediates all communications and transactions between computing entities. It implements authentication, authorization, and encryption protocols, managing security policies centrally while enabling distributed computation. This intermediary approach maintains security controls without preventing the coordination of multiple computing entities.
Data Source
AI summary
A method for computational-power sharing is provided. In implementations of the disclosure, a computational-power sharing network element is connected with a network to perform control plane transmission and data plane transmission, or the computational-power sharing network element is connected with the network to perform control plane transmission. The computational-power sharing network element acquires computational-power demand information transmitted by a computing demand side and available computational-power information transmitted by at least one computing cooperation side. The computational-power sharing network element determines a computing cooperation side from the at least one computing cooperation side according to the computational-power demand information and the available computational-power information, and directly or indirectly indicates the computing cooperation side to complete a computing task of the computing demand side, where the computing cooperation side is able to provide computational power for the computing demand side.