Latency Data Collection for Automated Resource Exchange Matching
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Solution Overview
Problem
Current distributed computer systems face challenges in effectively sharing and exchanging computational resources among multiple organizations due to complexity and geographical distribution, leading to inefficiencies in resource utilization and management.
Innovation Solution
A resource-exchange system employing distributed-search-based auction methods and latency data collection to facilitate efficient matching of resource consumers and providers across geographically distributed data centers, enabling automated selection and optimization of resource exchanges.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated resource-exchange systems are implemented across geographically distributed data centers, then resource allocation efficiency improves, but network latency variability increases
Solution Approach 1:
The system performs preliminary actions by continuously measuring and storing latency data between data centers before resource exchange decisions are made. This pre-collected latency information enables the automated brokerage system to predict and account for network delays when selecting resource providers, allowing it to choose candidates that meet latency constraints without real-time measurement delays.
Solution Approach 2:
The system cushions against latency variability by establishing latency constraints and selecting resource providers that satisfy these constraints in advance. By pre-identifying candidates within acceptable latency bounds, the system protects against timing issues that would otherwise disrupt resource exchange operations across distributed data centers.
2Measurement precision
If continuous latency data collection is implemented for all participant pairs, then candidate selection accuracy improves, but system complexity increases
Solution Approach 1:
Latency measurements are performed preliminarily and stored for later use, rather than being measured in real-time when needed. This approach allows the system to maintain high measurement accuracy while avoiding the complexity of real-time measurement coordination across all participant pairs during resource exchange operations.
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, the resource-exchange system continuously collects communications-latency data for pairs of resource-exchange participants, in order to support latency constraints associated with potential resource exchanges. The collected data facilitates efficient, rapid, automated candidate-resource-provider selection during auction-based matching of resource consumers to resource providers.