Distributed Resource Exchange System for Cloud Data Centers
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Solution Overview
Problem
Current distributed computing systems face challenges in effectively sharing and exchanging computational resources among multiple organizations, leading to inefficiencies in resource utilization and management, particularly due to the difficulty in seamlessly matching resource providers and consumers and addressing spare capacity within private data centers.
Innovation Solution
A distributed resource-exchange system employing a distributed-search-based auction mechanism that automatically generates hosting fees for computational resources, facilitating the aggregation of multiple data centers to create a multi-organization cloud-computing and resource-sharing facility, allowing for the leasing of unused resources and optimizing resource allocation through automated brokerage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If resource sharing and exchange among computing facilities of multiple organizations is implemented, then computational efficiency is improved, but the difficulty of matching resource providers and consumers increases
Solution Approach 1:
The patent introduces an automated resource-exchange system that acts as an intermediary between resource providers and consumers. The system includes a distributed-search-implemented auction mechanism that automatically matches computing resources with demands, eliminating the need for manual matching processes. The system generates hosting fees automatically and manages the complex matching logic through centralized auction procedures, thereby improving computational efficiency while reducing the operational complexity of resource matching.
Solution Approach 2:
The system performs preliminary actions by pre-configuring resource listings, demand profiles, and auction parameters before actual resource exchange occurs. Resource providers and consumers register their capabilities and requirements in advance, allowing the automated system to perform efficient matching without real-time complexity. The auction mechanism is pre-programmed with matching criteria and fee structures, enabling automatic resource allocation when demands arise.
2Productivity
If automated resource-exchange system is implemented, then resource allocation is optimized, but the complexity of the system increases
Solution Approach 1:
The automated resource-exchange system operates autonomously without requiring continuous human intervention. The distributed-search-implemented auction mechanism automatically generates hosting fees, matches resources with demands, and executes transactions. The system self-regulates through programmed auction rules and fee structures, optimizing resource allocation while containing operational complexity through automation rather than manual management procedures.
Solution Approach 2:
The system optimizes resource allocation by dynamically adjusting parameters such as hosting fees, auction timing, and matching criteria based on supply and demand conditions. The automated mechanism modifies these parameters through the auction process, allowing flexible resource allocation without requiring complex manual reconfiguration. Parameter changes are driven by algorithmic decision-making rather than human judgment, simplifying system operation while maintaining optimization capabilities.
3Measurement precision
If distributed-search-implemented auction is used, then resource matching accuracy is improved, but the time required for resource exchange increases
Solution Approach 1:
The distributed-search-implemented auction operates through periodic cycles rather than continuous processing. The system conducts structured auction rounds at defined intervals, allowing thorough resource matching during each cycle while providing predictable timing for resource exchange. This periodic structure enables accurate matching through multiple evaluation phases while constraining the total time required through scheduled completion points, balancing precision with efficiency.
Data Source
AI summary
The current document is directed a resource-exchange system that facilitates resource exchange and sharing among computing facilities. The currently disclosed methods and systems employ efficient, distributed-search-based auction methods and subsystems within distributed computer systems that include large numbers of geographically distributed data centers to locate resource-provider computing facilities that match the resource needs of resource-consumer computing facilities. In one implementation, resource-provider computing facilities automatically generate hosting fees for hosting computational-resources-consuming entities on behalf of resource-consumer computing facilities that are included in bid-response messages returned by the resource-provider computing facilities in response to receiving bid-request messages. In another implementation, a cloud-exchange system automatically generates hosting fees on behalf of resource-provider computing facilities.