Wireless Grid Computing Resource Allocation via Base Station Feedback
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Solution Overview
Problem
Current grid computing methods face limitations in efficiently allocating and managing computing resources and channel quality information across wireless terminals and base stations, leading to reduced performance and potential system damage due to overhead and interference in wireless networks.
Innovation Solution
A method and apparatus for wireless grid computing that involves a base station receiving feedback on computing resource state and channel quality information from candidate terminals, analyzing execution patterns, and determining optimal work execution terminals to assign parallel processing tasks, considering spatial properties and resource availability to minimize interference and maximize resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If wireless grid computing is implemented without centralized resource management, then system flexibility and ease of deployment are improved, but resource allocation efficiency and system performance deteriorate due to lack of coordination
Solution Approach 1:
The base station serves as an intermediary between multiple wireless terminals, centralizing the management of computing resource states and channel quality information. It collects feedback from terminals, analyzes execution patterns, and makes centralized scheduling decisions, thereby coordinating resource allocation across the wireless grid computing system while maintaining wireless connectivity flexibility
Solution Approach 2:
The system implements continuous feedback mechanisms where terminals report their computing resource states and channel quality information to the base station. The base station uses this feedback to dynamically adjust task allocation and scheduling decisions, optimizing resource utilization based on real-time system conditions
2Power
If more wireless terminals are allocated to execute parallel tasks, then computing power and processing capacity are improved, but channel interference and system overhead increase
Solution Approach 1:
The base station evaluates individual terminal characteristics including channel quality information and computing resource states, assigning tasks based on local conditions of each terminal. This localized optimization ensures that terminals with better channel conditions handle communication-intensive tasks while minimizing overall system interference
Solution Approach 2:
The system dynamically changes allocation parameters based on real-time feedback, adjusting which terminals execute which tasks based on their current computing resource availability and channel conditions. This dynamic parameter adjustment optimizes the balance between utilizing available computing power and minimizing channel interference
3Productivity
If computing tasks are frequently reallocated based on real-time conditions, then resource utilization efficiency is improved, but system overhead and complexity increase
Solution Approach 1:
The base station collects and stores computing resource state and channel quality information from terminals in advance, maintaining updated feedback information for future scheduling decisions. This preliminary data collection reduces the complexity of real-time allocation by having pre-processed information ready when task assignment is needed
Solution Approach 2:
The system implements selective reallocation rather than complete reassignment - only adjusting task allocations when necessary based on changing conditions, rather than continuously reallocating all tasks. This partial action approach maintains resource utilization efficiency while limiting the overhead associated with frequent reallocation operations
Data Source
AI summary
A method for wireless grid computing includes: receiving feedback of a computing resource state (“CRS”) and channel quality information (“CQI”) from at least one candidate terminal; determining at least one parallel work execution terminal to execute the wireless grid computing among at least one terminal based on the feedback of the CRS and the feedback of the CQI; and assigning parallel processing work of the wireless grid computing to the at least one parallel work execution terminal. Such wireless grid computing method may be performed using a base station or a terminal.