Dynamic Network Connection Tuning for Cloud Throughput
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Solution Overview
Problem
Cloud computing networks face challenges in maximizing network throughput during heavy data transfer due to fluctuating network throughput and IOPS, leading to excessive retries and potential errors, which can overwhelm cloud providers and result in underutilized bandwidth.
Innovation Solution
A network tuning process that dynamically adjusts the number of connections based on error rates and bandwidth availability, using a greedy algorithm to maximize throughput without incurring exponential backoff penalties, thereby maintaining consistent network performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a static number of connections is used during data transfer, then system simplicity is maintained, but network throughput is underutilized and excessive retries occur during heavy traffic
Solution Approach 1:
The patent implements dynamic connection management by adjusting the number of connections based on real-time network conditions. The system monitors error rates and network throughput, then adaptively increases or decreases the connection count to optimize data transfer efficiency while avoiding overwhelming the cloud provider during peak traffic periods.
Solution Approach 2:
The system employs feedback mechanisms by continuously monitoring network error rates and throughput performance. Based on this feedback, the connection manager dynamically adjusts the number of active connections, reducing connections when error rates are high and increasing them when network conditions are favorable, thereby maximizing throughput while maintaining system stability.
2Productivity
If the number of connections is increased to maximize bandwidth utilization, then network throughput improves, but cloud provider load increases causing more errors and retries
Solution Approach 1:
The system dynamically adjusts connection counts based on real-time monitoring of cloud provider response quality. When error rates exceed thresholds or throughput degradation is detected, the system automatically reduces the number of connections to prevent overwhelming the provider, thereby maintaining reliable data transfer while optimizing bandwidth utilization.
Solution Approach 2:
The patent implements a feedback-driven connection management system that monitors data transfer success rates and network performance metrics. When feedback indicates poor performance or high error rates, the system reduces connection counts to improve reliability. When performance is good, it increases connections to maximize throughput, creating a self-regulating mechanism that balances both objectives.
3Reliability
If exponential backoff retry strategy is used during network errors, then individual retry attempts are spaced out, but overall throughput decreases and cloud provider remains overwhelmed
Solution Approach 1:
The system dynamically adjusts the retry strategy by modifying both the connection count and retry timing based on network conditions. Instead of fixed exponential backoff, the system adapts retry intervals and connection allocation in real-time, allowing more aggressive retries when network conditions permit and more conservative approaches when the cloud provider is overwhelmed, thereby improving both recovery reliability and overall throughput.
Solution Approach 2:
The patent changes key parameters of the retry mechanism dynamically, including retry timing intervals and active connection counts. By adjusting these parameters based on monitored network performance and error rates, the system optimizes the balance between reliable error recovery and maintaining high throughput, avoiding the throughput penalty of fixed exponential backoff while preserving reliability.
Data Source
AI summary
A system and method for maximizing network throughput by dynamically tuning the number of connections used during data transfer with various cloud providers. This adaptive tuning of the network connections is based on the accepted request rate for the cloud provider, the outstanding transfer capacity of the cloud network as derived from the network bandwidth, and errors and retry requests from the network and the cloud provider.