Server Assignment via Deep Learning Network Metric Prediction
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Solution Overview
Problem
Existing distributed systems, such as Content Delivery Networks (CDNs) and Peer to Peer (P2P) networks, rely on simple metrics like geographic proximity for resource assignment, failing to consider other critical factors like bandwidth, delay, and jitter, which can lead to inefficient resource allocation and service quality issues.
Innovation Solution
A method using deep learning to predict network metrics between clients and candidate servers, allowing for intelligent resource assignment based on specific service requirements by measuring and modeling network performance, thereby selecting the best server for the requested service.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If simple metrics like geographic proximity are used for server assignment, then the system complexity is reduced and ease of operation is improved, but the service quality and network efficiency deteriorate
Solution Approach 1:
The system performs preliminary measurements of network metrics (bandwidth, delay, jitter) between clients and candidate servers before actual resource assignment. These pre-measured metrics are stored and used to predict connection quality, allowing the system to make informed server selection decisions without performing real-time measurements during service delivery.
Solution Approach 2:
The patent introduces an intermediary measurement and prediction system that acts as a mediator between the client-server assignment process and the actual resource delivery. This intermediary layer collects network metric data, trains machine learning models, and provides prediction recommendations, thereby improving service quality without directly modifying the core assignment mechanism.
2Measurement precision
If multiple network metrics are measured and predicted using deep learning, then the service quality and resource allocation accuracy are improved, but the system complexity and measurement requirements increase
Solution Approach 1:
The system segments the network metric prediction task into distinct components: measurement phase (collecting bandwidth, delay, jitter metrics), model training phase (training separate machine learning models for each metric type), and prediction phase (applying trained models to specific client-server pairs). This segmentation allows each component to be optimized independently and simplifies the overall system architecture.
Solution Approach 2:
The patent uses machine learning models to create virtual copies or predictions of network metric values without actually establishing physical connections. Instead of measuring real-time network conditions for every potential client-server pair, the system uses trained models to predict what the metrics would be, significantly reducing measurement overhead while maintaining prediction accuracy.
3Measurement precision
If network metrics are measured between all client-server pairs, then the prediction accuracy is improved, but the bandwidth consumption and time required for measurements increase
Solution Approach 1:
The system performs network metric measurements in advance during a training phase, building a dataset of actual measurements between various client-server pairs. These pre-collected measurements are used to train machine learning models, eliminating the need for extensive real-time measurements when actual service assignment occurs. The time-consuming measurement process is shifted to occur beforehand rather than during operational use.
Solution Approach 2:
Instead of measuring all possible client-server pairs exhaustively, the system performs measurements on a representative subset of pairs during the training phase. The machine learning models learn patterns from this partial dataset and generalize to predict metrics for unseen pairs, achieving sufficient accuracy without the excessive time and bandwidth cost of complete measurement coverage.
Data Source
AI summary
A method for assigning a server to provide a resource to a client in a distributed network includes receiving a request for the resource from the client. A network metric is measured at different points in the network. The network metric measurements are input to a deep learning model. Using the model, the network metric is predicted between the client and each of a plurality candidate servers which have the resource and have not had a prior connection with the client. One of the candidate servers is assigned to provide the resource to the client based on the predictions of the network metric.