Geographic Consensus Protocol for Mobile Distributed Data Management
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Solution Overview
Problem
Data management in highly distributed systems with mobile computing resources is challenging due to frequent node availability changes, leading to issues like timeouts, re-elections, multi-phase commits, sporadic network connectivity, and data protection risks.
Innovation Solution
Implementing a geographic location-based consensus protocol that predicts available consensus nodes by using geographic coordinates, route information, and speed to optimize data movement and management, and dynamically adjusts data protection policies based on the number of available nodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If mobile compute platforms are used in highly distributed systems, then system flexibility and mobility are improved, but node availability stability deteriorates due to frequent movement and connectivity changes
Solution Approach 1:
The system performs preliminary actions by predicting future node availability based on current geographic location, movement direction, and speed. Consensus nodes are pre-identified and pre-contacted before they actually become unavailable, allowing the system to prepare alternative validation paths in advance. This is implemented through the availability prediction module that calculates future positions and determines which nodes will be available at future time points.
Solution Approach 2:
The system dynamically adapts the consensus validation process based on real-time node availability predictions. Instead of using a static consensus mechanism, the system adjusts which nodes participate in validation based on their predicted availability, creating a dynamic consensus protocol that responds to the mobile nature of the compute platforms. This allows the system to maintain reliability despite node movement.
2Extent of automation
If traditional consensus protocols are used with mobile nodes, then decentralization is maintained, but transaction validation time increases due to node unavailability and re-elections
Solution Approach 1:
The system contacts and secures node availability predictions in advance before transaction validation is needed. By predicting which nodes will be available at future time points and preparing the validation set beforehand, the system eliminates delays caused by waiting for node responses during the validation process. This preliminary action significantly reduces transaction validation time while maintaining decentralization.
Solution Approach 2:
The system skips unnecessary re-election processes and redundant validation steps by using predicted availability information. When nodes are predicted to be unavailable, the system skips contacting them and directly selects alternative nodes from the predicted available set, rushing through the validation process efficiently without the delays of traditional consensus re-elections.
3Speed
If data is transferred to continuously moving compute platforms, then system mobility is improved, but data management complexity increases due to sporadic network connectivity
Solution Approach 1:
The system performs preliminary data preparation and node selection based on predicted availability before data transfer begins. By identifying which nodes will be available and preparing the data distribution plan in advance, the system simplifies the actual data transfer process despite mobile connectivity challenges. This preliminary planning reduces the complexity of managing data transfers to moving platforms.
Solution Approach 2:
The availability prediction module acts as an intermediary between the data management system and mobile compute platforms. It translates the complexity of mobile node availability into simplified predictions about future node states, allowing the data management system to make decisions based on these predictions rather than dealing directly with the complexity of sporadic connectivity. This intermediary layer abstracts and simplifies data management operations.
Data Source
AI summary
Techniques are disclosed for decentralized data management using a geographic location-based consensus protocol in a network of computing resources such as, by way of example, a highly distributed system. For example, at a given consensus node of a consensus network comprising a plurality of consensus nodes configured to participate in a consensus protocol wherein at least a portion of the consensus nodes are mobile, a list is obtained of at least a subset of the plurality of consensus nodes that are predicted to be currently available to participate in the consensus protocol based on geographic location information. A message comprising a transaction to be validated is sent from the given consensus node to the subset of the plurality of consensus nodes in the obtained list. Techniques are also disclosed for adjusting a data protection policy based on the number of computing nodes, some of which are mobile, available to participate.