Currently, the Layer2 ecosystem faces key pain points of 'computing power resource mismatch' and 'rigid cost allocation'—some Rollups (such as financial chains) experience computing overload during peak times leading to transaction congestion, while other Rollups (such as gaming chains) have idle computing power during off-peak times. At the same time, costs are allocated based on a fixed ratio, leading to unequal burdens among participants as high computing power consumption scenarios and low consumption scenarios bear the same costs. Caldera's breakthrough lies in constructing a 'cross-chain computing power collaboration network' to achieve dynamic scheduling of computing power and creating a 'dynamic cost allocation system' that matches consumption with costs, transforming Layer2 from 'computing power islands' into efficient infrastructures where 'computing power is fluid and costs can be fairly allocated.'
1. Cross-Chain Computing Power Collaboration Network: From 'Computing Power Islands' to 'Universal Computing Power Scheduling'
To address computing power mismatch, Caldera innovates with a 'distributed computing pool + real-time scheduling algorithm + elastic computing interface': it aggregates the idle computing power of various Rollups (for example, 60% idle nodes during off-peak times for gaming chains) into a 'distributed computing pool' available for computing power-short Rollups to call upon through standardized interfaces. The real-time scheduling algorithm allocates resources based on 'urgency of computing power demand + scenario value,' prioritizing computing power for large settlements in financial chains (high urgency, high value) while allowing gaming chains' daily interactions (low urgency) to utilize idle resources at staggered times. A certain test showed that this network reduced the computing overload rate of the financial chain from 35% to 5% and increased the computing utilization rate of the gaming chain from 40% to 85%, with cross-chain computing power call response times controlled within 200ms.
2. Dynamic Cost Allocation System: From 'Fixed Ratio' to 'Consumption-Matched Costs'
To resolve cost rigidity, the system employs 'computing power consumption measurement + scenario value weight': basic costs are measured based on the actual computing power utilized by participants (for example, financial chains calling 100 core-hours and gaming chains calling 20 core-hours), and then the scenario value weight is added (financial scenario 1.5, gaming scenario 0.8). The final cost = computing power consumption × value weight. A certain cross-border payment project (high value scenario) called 100 core-hours of computing power, resulting in costs of 100 × 1.5 = 150 ERA; whereas a gaming project (low value scenario) calling the same computing power incurred costs of 100 × 0.8 = 80 ERA. This system makes the cost burden for high consumption high value scenarios more reasonable, reducing costs for low consumption scenarios by 40%, and increasing participant satisfaction by 70%.
3. Commercial Implementation: The Synergistic Value of Computing Power and Costs
In the smart healthcare scenario, a medical data on-chain project (high computing power demand, high value) utilized the computing power collaboration network to call upon the computing power of three idle Rollups, reducing data processing time from 2 hours to 15 minutes. Costs were allocated based on 'computing power consumption × 1.8 (weight for healthcare scenarios),' saving 60% compared to building a dedicated computing cluster. In the e-commerce live streaming scenario, idle computing power was called upon to process order data during peak live streaming times, with costs allocated based on 'computing power consumption × 0.9,' reducing merchant operating costs by 35% and tripling order processing efficiency.
In summary, Caldera's innovation directly addresses the core contradiction between computing power and costs. Through cross-chain computing power collaboration and dynamic cost allocation, it enables Layer2 to support high computing power demand scenarios while ensuring fair costs, providing new pathways for large-scale implementation across multiple scenarios.
