Adaptive Encoding Network for Dynamic Bandwidth Reduction
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Solution Overview
Problem
Current data encoding solutions in computer systems are static and fail to adapt to dynamic aspects of data transmission, leading to excessive bandwidth consumption and network latency.
Innovation Solution
Implementing an adaptive encoding network with a consensus server to dynamically assign encodings based on usage frequency, using a consensus protocol to agree on encoding assignments and manage conflicts, while applying new encodings during non-peak network times to avoid write amplification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If static encoding schemes are used to reduce data size, then bandwidth consumption is reduced, but the system cannot adapt to dynamic transmission conditions and requires manual configuration
Solution Approach 1:
The patent implements dynamic encoding by continuously monitoring usage frequency of data values and automatically updating encoding assignments based on real-time patterns. The system transitions from static, manually-configured encoding to dynamic, self-adjusting encoding that adapts to changing transmission conditions and data patterns without manual intervention.
Solution Approach 2:
The system employs feedback mechanisms by tracking usage frequency of encoded values and using this information to continuously optimize encoding assignments. The monitoring component feeds usage statistics back to the encoding component, enabling the system to learn from transmission patterns and improve encoding efficiency over time.
2Productivity
If encoding assignments are updated frequently to optimize bandwidth usage, then transmission efficiency improves, but write amplification occurs during peak network times
Solution Approach 1:
The system implements periodic updates of encoding assignments based on monitored usage patterns rather than continuous updates. By updating encodings periodically when usage thresholds are met or at scheduled intervals during non-peak times, the system balances optimization needs with system load, avoiding excessive write operations during peak network usage.
Solution Approach 2:
The system performs encoding updates during non-peak network times in advance of when they are most needed. By proactively updating encoding assignments during low-traffic periods, the system prepares optimized encodings before peak transmission demands occur, avoiding the need for time-critical updates during high-load periods.
Data Source
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AI summary
Systems and methods of improving the functioning of a computer system by implementing an adaptive encoding network are disclosed. In some example embodiments, a computer system transmits a new encoding assignment representing an encoding of a value with a new code to a consensus server, receives an approval of the new encoding assignment from the consensus server, and, based on the receiving of the approval of the new encoding assignment from the consensus server, applies the new encoding assignment to the value in subsequent messages to one or more machines, with the applying of the new encoding assignment comprising including the new code of the new encoding assignment in the subsequent messages in association with the value.