Fleet Resource Transactions for Volatile Energy and Compute Markets
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Solution Overview
Problem
The increasing demand for efficient and flexible management of energy and compute resources in distributed markets, particularly in the context of blockchain and AI applications, is hindered by volatility in resource costs and availability, as well as the need for intelligent systems that can adapt to uncertainty and optimize operations.
Innovation Solution
A transaction-enabling system comprising a fleet of machines with compute, networking, and energy consumption requirements, controlled by a system that determines resource needs, accesses resource markets, and executes aggregated transactions using machine learning or AI to optimize resource allocation and cost management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If distributed ledger and peer-to-peer interaction models are used to replace centralized authorities, then system reliability and adaptability are improved, but device complexity and difficulty of detecting and measuring increase
Solution Approach 1:
The patent introduces automated agents as intermediary entities that mediate transactions in distributed markets. These agents simplify complex peer-to-peer interactions by autonomously negotiating and executing transactions, thereby maintaining system reliability while reducing the operational complexity for human users. The agents act as intelligent intermediaries that handle the complexity of distributed ledger operations, smart contract execution, and resource allocation.
2Power
If energy-intensive computing operations are performed for blockchain mining and AI applications, then processing power and transaction validation are improved, but energy consumption increases
Solution Approach 1:
The patent implements dynamic resource allocation where automated agents continuously monitor market conditions, energy prices, and computational demands to adjust computing operations in real-time. This allows the system to optimize the balance between processing power and energy consumption by dynamically scaling operations based on current needs, performing intensive computations only when necessary and at optimal times when energy costs are lower.
Solution Approach 2:
The system changes operational parameters such as computing intensity, transaction batch sizes, and mining difficulty adjustments based on market conditions and energy availability. By dynamically modifying these parameters, the system can maintain adequate processing power for blockchain validation and AI operations while adapting energy consumption levels to match supply conditions and cost variations.
3Productivity
If automated agents execute transactions in distributed markets, then transaction speed and efficiency are improved, but system complexity and uncertainty management increase
Solution Approach 1:
The patent empowers automated agents with self-service capabilities to independently execute transactions, negotiate terms, and manage their own resource allocations in distributed markets. These agents autonomously interpret smart contracts, validate transactions against consensus rules, and adapt to market fluctuations without requiring centralized coordination, thereby increasing transaction speed while distributing system complexity across multiple independent agents rather than concentrating it in a single control point.
Data Source
AI summary
The present disclosure describes transaction-enabling systems and methods. A system can include a controller and a fleet of machines each having at least one of a compute task requirement, a networking task requirement, and an energy consumption task requirement. The controller may include a resource requirement circuit structured to determine an amount of a resource for each of the machines to service at least one of the task requirements, a resource market circuit to access a resource market, and a resource distribution circuit to execute an aggregated transaction of the resource on the resource market in response to the determined amount of the resource for each of the machines.