Energy Credit Aggregation for Adaptive Compute Resource Allocation
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Solution Overview
Problem
The increasing complexity and energy intensity of distributed ledger systems and automated market transactions pose challenges in optimizing energy and compute resource management, particularly due to volatility in energy markets and the variability of computing resources, necessitating a flexible and intelligent system for resource allocation and transaction execution.
Innovation Solution
A transaction-enabling system that includes a resource requirement circuit for aggregating resource needs, a forward market circuit for accessing and trading energy resources, and a machine learning or AI component for adaptive resource distribution, enabling efficient energy and compute resource management through predictive pricing and transaction optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If distributed ledger systems and automated market transactions are implemented, then transaction execution and resource allocation are automated and decentralized, but energy consumption and system complexity increase significantly
Solution Approach 1:
The system performs preliminary actions by aggregating resource requirements before transactions are executed. The resource requirement circuit collects and aggregates compute, storage, networking, and energy requirements in advance, allowing the system to prepare and optimize resource allocation before actual transaction execution, thereby reducing the energy needed during the transaction process itself.
Solution Approach 2:
The patent introduces an intermediary resource distribution circuit that acts as a mediator between the automated transaction system and physical energy resources. This intermediary optimizes the matching of compute tasks with energy resources, and can leverage energy market transactions (such as forward markets) to allocate energy more efficiently, reducing overall system energy consumption while maintaining automation.
2Power
If energy-intensive computing operations are performed for blockchain mining and AI applications, then computational power and transaction security are enhanced, but energy costs and operational expenses increase
Solution Approach 1:
The system implements dynamic resource allocation where the resource distribution circuit continuously adjusts the matching of computational tasks with energy resources based on real-time conditions. The system can dynamically shift between different energy sources (renewable vs. non-renewable), adjust compute task scheduling, and respond to changing energy prices in forward markets, thereby maintaining high computational power while optimizing energy costs.
Solution Approach 2:
The patent changes key parameters by introducing forward market transactions for energy resources. Instead of simply consuming energy at spot prices, the system can purchase energy in advance at optimized rates, store energy credits, and adjust consumption patterns based on predicted energy prices and availability, thereby maintaining computational power while reducing energy costs.
3Adaptability or versatility
If forward markets for energy resources are accessed, then energy allocation flexibility and cost optimization are improved, but system complexity and transaction overhead increase
Solution Approach 1:
The resource distribution circuit is designed as a universal component that handles multiple functions: aggregating resource requirements, optimizing task-to-resource matching, executing forward market transactions, and managing energy credit storage. By consolidating these diverse functions into a single multi-functional circuit, the system achieves energy allocation flexibility without proportionally increasing overall system complexity.
Solution Approach 2:
The system implements self-service mechanisms where the resource distribution circuit autonomously monitors energy market conditions, automatically executes forward market transactions when beneficial, and dynamically adjusts resource allocation without requiring external intervention. This self-service capability provides adaptability while minimizing the operational complexity burden on users and external systems.
Data Source
AI summary
Systems and methods for aggregating transactions and optimization data related to energy and energy credits include a transaction-enabling system including a resource requirement circuit structured to aggregate a resource requirement for a fleet of machines to perform a task, wherein the resource requirement comprises an energy credit requirement; a forward resource market circuit structured to access a forward market for energy; and a machine resource acquisition circuit structured to execute a transaction on the forward market for energy in response to the aggregated resource requirement.