Web3 infrastructure (public chains, cross-chain bridges, storage networks) is the core carrier of ecological operations, but has long faced issues of 'efficiency bottlenecks' and 'resource mismatches': public chains frequently become congested due to 'insufficient prediction of transaction heat', leading to soaring Gas fees; cross-chain bridges experience liquidity distribution imbalance due to 'delayed fund flow data', causing users to often encounter excessive slippage during transfers; storage networks suffer from 'lack of basis for hot and cold data classification', resulting in high storage costs with low access efficiency. The root of these problems lies in infrastructure operations relying heavily on 'static configurations' (e.g., fixed block sizes, preset liquidity ratios), failing to combine real-time on-chain data for dynamic adjustments—traditional on-chain tools only output data to users, lacking the capability for 'collaborative optimization with infrastructure', resulting in data value failing to feedback to the underlying ecosystem, and infrastructure efficiency remaining difficult to break through. Bubblemaps' core breakthrough lies in constructing a linked system of 'real-time data monitoring—dynamic optimization model—infrastructure collaborative interface', allowing on-chain data to deeply integrate into infrastructure operations, driving resource allocation and efficiency upgrades of public chains, cross-chain bridges, and storage networks, achieving the positive cycle of 'data feedback to infrastructure, infrastructure supporting ecology'.
1. The three core pain points of coordination between Web3 infrastructure and on-chain data
The efficiency issues of Web3 infrastructure fundamentally arise from the disconnection between 'static operational logic' and 'dynamic on-chain data', specifically manifested in three major pain points that directly constrain ecological carrying capacity and user experience:
(1) Public chain resource scheduling: 'Passive response' without data support
The core resources of public chains (block Gas limits, block speeds, verification node allocations) often adopt 'fixed configurations' or 'manual adjustments', unable to respond in real-time to changes in on-chain transaction heat: when a certain sector (e.g., NFT minting) suddenly explodes, transaction requests surge fivefold, and public chains maintain their original block capacity due to 'failure to predict heat', causing Gas fees to soar from 50 Gwei to 300 Gwei, with users waiting for hours for transfers; when transaction heat subsides, public chains again fail to timely lower Gas limits due to 'delayed adjustments', resulting in idle block space (e.g., a certain public chain's block filling rate drops from 90% to 30%), leading to serious resource waste. Traditional tools can only 'show' Gas fee fluctuations and block congestion after the fact, failing to provide public chains with 'transaction heat prediction data' in advance, resulting in public chains being in a passive state of 'responding after congestion, wasting during idle times'.
(2) Cross-chain bridge liquidity configuration: the 'mismatch dilemma' due to data lag
The liquidity distribution of cross-chain bridges (e.g., fund ratios of each chain pool, fee settings) relies on 'historical data' or 'manual experience', and cannot respond in real-time to changes in on-chain fund flows: when cross-chain demand from Ethereum to Polygon suddenly increases threefold (e.g., a certain DeFi project initiates an airdrop on Polygon), the cross-chain bridge fails to timely supplement liquidity to the Polygon pool due to 'data lag', causing the slippage in that direction to rise from 0.5% to 5%, resulting in users paying high costs for transfers; when funds flowing from Solana to Ethereum suddenly decrease, the cross-chain bridge still maintains high liquidity reserves, causing millions of dollars to be idle and unable to generate returns. This 'mismatch between liquidity and demand' not only reduces user experience but also wastes the capital efficiency of cross-chain bridges, rooted in the lack of 'real-time fund flow data' and 'dynamic adjustment mechanisms'.
(3) Storage network data management: unsubstantiated 'cost waste'
The core pain point of Web3 storage networks (e.g., IPFS, Arweave) is 'hot and cold data not classified'—high-frequency accessed 'hot data' (e.g., popular NFT images, DApp front-end code) mixed with low-frequency accessed 'cold data' (e.g., historical transaction records, old contract codes), resulting in 'slow access to hot data and high storage costs for cold data': when users access a certain popular NFT project, due to the mixing of image files with a large amount of cold data, the loading time increases from 1 second to 10 seconds; storage networks need to pay the same storage cost for cold data to maintain its high availability, leading to overall high storage costs. Traditional tools cannot provide key data such as 'data access frequency, heat levels', making it impossible for storage networks to optimize data storage strategies effectively, resulting in a dual waste of 'efficiency and cost'.
Two, the core realization of the collaborative optimization system: three-layer interaction drives infrastructure upgrades
Bubblemaps' collaborative optimization system does not 'intervene in infrastructure operations', but provides 'dynamic adjustment basis' for infrastructure through 'data output—model computation—interface integration', aligning infrastructure operation more closely with actual on-chain demands, achieving a balance between efficiency and cost.
(1) First Layer: Real-time data monitoring—capturing the 'dynamic signals' of infrastructure operations
The system first builds a 'Web3 infrastructure data monitoring network', capturing core operational data and on-chain associated data of public chains, cross-chain bridges, and storage networks in real-time, forming a 'dynamic data dashboard' to provide a basis for subsequent optimization.
1. Public chain operational data monitoring
Key monitoring of three types of data: 'transaction heat, resource utilization, node status':
• Transaction heat data: Real-time statistics of 'transaction request volumes, pending transaction counts, single transaction Gas quotes' for each sector (DeFi, NFT, social), predicting future 1-hour heat changes (e.g., 'NFT minting transaction requests increase by 200% within 10 minutes, expected pending transactions to exceed 50,000 in 1 hour');
• Resource utilization data: Real-time tracking of 'block Gas filling rate, block delay time, verification node response speed', identifying resource bottlenecks (e.g., 'block Gas filling rate reaches 95% continuously for 10 minutes, block delay increases from 2 seconds to 5 seconds, congestion risk exists');
• Node status data: Monitoring the verification nodes' 'online rate, computing power contribution, error rate', promptly identifying abnormal nodes (e.g., 'a certain node has made errors in three consecutive blocks, needing to trigger the backup node switch').
2. Cross-chain bridge liquidity data monitoring
Focusing on three types of data: 'fund flow, liquidity reserves, user experience':
• Fund flow data: Real-time tracking of transaction counts and amounts in various cross-chain directions (e.g., ETH→BSC, SOL→AVAX), identifying sudden demands (e.g., 'ETH→Polygon direction transaction amount increases by 300% within 5 minutes, net capital inflow exceeds 10 million USD');
• Liquidity reserve data: Real-time monitoring of 'fund balances and liquidity adequacy rates of each chain pool (current balance/average 1-hour demand)', warning of insufficient liquidity (e.g., 'Polygon pool liquidity adequacy rate drops to 30%, below the safe threshold of 50%');
• User experience data: Real-time statistics of 'cross-chain transfer slippage, arrival time, failure rate', identifying experience pain points (e.g., 'slippage exceeds 3% in the ETH→SOL direction, arrival time exceeds 15 minutes, user complaints increase').
3. Storage network data monitoring
Core capture of three types of data: 'data access frequency, storage costs, availability':
• Data access frequency: Statistics of each file (e.g., NFT images, contract codes, user data) 'daily access counts, peak access periods, access IP distribution' to distinguish between hot and cold data (e.g., 'a certain NFT image has 100,000 daily accesses, classified as hot data; a certain 2023 transaction record has 10 daily accesses, classified as cold data');
• Storage cost data: Real-time calculation of 'unit storage costs and access costs for different storage tiers (e.g., IPFS hot storage, Arweave cold storage)', comparing cost-effectiveness;
• Availability data: Monitor 'data retrieval success rate, loading time', identifying storage bottlenecks (e.g., 'hot data loading time exceeds 5 seconds, retrieval success rate 90%, below target value 99%').
(2) Second Layer: Dynamic optimization model—outputting 'adjustment plans' for infrastructure
Based on real-time monitoring data, the system outputs 'actionable adjustment suggestions' for different infrastructures through an 'AI dynamic optimization model', ensuring resource allocation aligns with actual needs and avoids blind adjustments.
1. Public chain resource scheduling optimization model
The model outputs adjustment suggestions for 'Gas limits, block parameters, node configurations' based on transaction heat and resource utilization data:
• Congestion warning and response: When predicting 'the number of pending transactions to be confirmed exceeds 50,000 in 1 hour, the block filling rate will reach 100%', it is suggested to 'temporarily increase the Gas limit from 30 million to 45 million, reduce the block interval from 12 seconds to 10 seconds' to alleviate congestion;
• Idle resource optimization: When monitoring 'block filling rate remains below 40% for 30 minutes, and Gas fees drop below 10 Gwei', it is suggested to 'roll back the Gas limit to 20 million, reducing verification node computational power allocation', lowering resource waste;
• Node anomaly handling: When it is found that 'a certain node's error rate exceeds 5%', it is suggested to 'trigger the node replacement mechanism, assigning the verification task of that node to a backup node', ensuring public chain stability.
After connecting to the model, the congestion duration decreased from 'an average of 2 hours per day' to 'an average of 15 minutes per day', the Gas fee fluctuation reduced from '300%' to '50%', and resource utilization increased by 40%.
2. Cross-chain bridge liquidity configuration optimization model
The model outputs suggestions for 'chain pool fund allocation, fee adjustments, liquidity incentives' based on fund flow and liquidity data:
• Emergency liquidity replenishment: When monitoring 'the liquidity adequacy of the ETH→Polygon direction drops to 30%, slippage exceeds 3%', it is suggested to 'allocate 5 million USD from the ETH pool to the Polygon pool, while activating 'temporary liquidity provider (LP) rewards' (an additional annualized 5%) to attract users to supplement liquidity';
• Idle capital activation: When it is found that 'the liquidity adequacy of the Solana pool reaches 200%, and the net capital inflow is negative for 24 consecutive hours', it is suggested to 'allocate 30% of idle capital to the liquidity-tight AVAX pool, while reducing the LP rewards of the Solana pool (from an annualized 10% to 5%), guiding funds towards the demand side';
• Fee dynamic adjustment: When a certain cross-chain direction's 'transfer failure rate exceeds 10%', it is suggested to 'temporarily reduce the fee (from 0.3% to 0.1%), while optimizing cross-chain routing to improve transfer success rates'.
After a certain cross-chain bridge integrated the model, user transfer slippage decreased from 'an average of 2%' to 'an average of 0.5%', idle liquidity rate dropped from '30%' to '10%', and cross-chain transfer failure rate fell from '8%' to '1.5%'.
3. Storage network data management optimization model
The model outputs suggestions for 'cold and hot data classification, storage tier allocation, caching strategies' based on data access frequency and cost data:
• Cold and hot data classification and storage: It is suggested to 'store hot data (e.g., popular NFT images, DApp front-end) with more than 1,000 daily accesses in the IPFS hot storage layer, while migrating cold data (e.g., historical transaction records) with fewer than 100 daily accesses to the Arweave cold storage layer', balancing access speed and cost;
• Hot data caching optimization: It is suggested to 'deploy hot data caching nodes in user-concentrated areas (such as Southeast Asia, North America) to cache popular files locally, reducing cross-region data transmission and shortening loading time from 5 seconds to 1 second';
• Cold data cost control: It is suggested to 'adopt a strategy of 'compressed storage + periodic archiving' for cold data, reducing storage costs by 30% while maintaining a 'fast retrieval channel' (e.g., recoverable access within 24 hours).
After a certain storage network integrated the model, the hot data loading time decreased from '8 seconds' to '1.2 seconds', the overall storage cost decreased by 25%, and the data retrieval success rate increased from '92%' to '99.5%'.
(3) Third Layer: Infrastructure collaborative interface—implementing the transition from 'data suggestions' to 'actual adjustments'
The optimization plan must connect with the infrastructure through a 'collaborative interface' to transform from 'suggestions' to 'actual adjustments'. Bubblemaps develops 'standardized collaborative interfaces' to support integration with mainstream public chains (Ethereum, Polygon, Solana), cross-chain bridges (Hop Protocol, Avalanche Bridge), and storage networks (IPFS, Arweave), achieving 'data-driven automatic adjustments'.
1. Public chain collaborative interface
The interface supports directly pushing suggestions such as 'Gas limit adjustments, block parameter optimizations', to the public chain's 'resource scheduling module', without human intervention:
• When the model suggests 'increase the Gas limit to 45 million', the interface automatically sends adjustment instructions to the public chain nodes, which sync updates after verification, completing the entire process within 1 minute;
• The interface also supports a 'data feedback loop'—after adjustments are made to the public chain, the interface captures 'new block filling rates, Gas fee changes' in real-time, feeding back to the optimization model. If the adjustment effects do not meet expectations (e.g., Gas fees continue to soar), the model immediately outputs 'secondary adjustment suggestions'.
2. Cross-chain bridge collaborative interface
The interface connects with the cross-chain bridge's 'liquidity management module', achieving 'automatic capital allocation, dynamic adjustment of LP rewards':
• When the model suggests 'allocate 5 million USD from the ETH pool to the Polygon pool', the interface automatically triggers the cross-chain bridge's 'internal fund transfer contract', completing the fund allocation and synchronously updating the liquidity data of each chain pool;
• When 'temporary LP rewards' need to be activated, the interface sends instructions to the cross-chain bridge's 'reward distribution module' to adjust reward parameters in real-time and pushes 'reward enhancement notifications' to LP users to attract capital influx.
3. Storage network collaborative interface
The interface connects with the storage network's 'data management module', achieving 'automatic migration of cold and hot data, caching node scheduling':
• When the model identifies 'a certain file transitioning from hot data to cold data (daily access drops from 10,000 times to 50 times)', the interface automatically triggers a 'data migration task', migrating the file from IPFS hot storage to Arweave cold storage, and updates the data index;
• When regional caching nodes need to be deployed, the interface sends instructions to the storage network's 'node management module' to activate backup caching nodes in the target area, completing hot data distribution simultaneously.
Three, the ecological value of collaborative optimization: efficiency improvement from infrastructure to the entire ecosystem
Bubblemaps' collaborative optimization system not only addresses the efficiency pain points of infrastructure but also creates full-link value from three dimensions: 'user experience, ecological bearing, industry development', pushing the Web3 ecosystem from 'weak foundation' to 'efficient and robust'.
For users, collaborative optimization means 'lower costs and better experiences'—congestion in public chains decreases, Gas fees stabilize, transfers do not require long waiting times; cross-chain slippage reduces, funds arrive faster, and users do not need to pay extra costs for transfers; storage access accelerates, DApp loading and NFT viewing become smoother, no longer affected by technical bottlenecks. A certain user reported that after integrating the optimization system, the Gas fee cost for minting their NFT dropped from 'an average of 100 USDT' to 'an average of 30 USDT', and the cross-chain transfer time reduced from '20 minutes' to '5 minutes', significantly enhancing the experience.
For Web3 infrastructure providers, collaborative optimization means 'higher efficiency and lower costs'—the resource utilization of public chains improves, allowing them to accommodate more users without blind expansion; cross-chain bridge liquidity configuration is reasonable, idle funds decrease, and revenue capacity increases; storage network costs decrease, controlling expenses without sacrificing availability. A certain public chain team stated that after integrating the system, their annual infrastructure operation and maintenance costs were reduced by 20%, while their ecological carrying capacity increased by 50%, supporting more DApps and user access.
For the Web3 industry, collaborative optimization means 'enhanced underlying support for ecological development'—the efficiency of infrastructure is the 'ceiling' for ecological expansion. When public chains, cross-chain bridges, and storage networks can continuously optimize through data-driven approaches, Web3 can support more 'mass applications' (e.g., Web3 social, decentralized e-commerce), breaking free from the limitations of a 'niche technology circle'. This 'data feedback to infrastructure' model also provides a 'replicable efficiency upgrade path' for the iteration of Web3 infrastructure, pushing the industry from 'competing on technical parameters' to 'competing on operational efficiency', achieving healthier development.
Conclusion
The ecological prosperity of Web3 relies on the 'efficient and robust' infrastructure, and the breakthrough in infrastructure efficiency must be driven by 'on-chain data'. Bubblemaps' positioning as a 'collaborative optimizer' precisely captures the essence of 'infrastructure and data' collaboration, enabling on-chain data to upgrade from a 'user-end tool' to an 'efficiency engine for infrastructure' through real-time monitoring, dynamic models, and collaborative interfaces.
Only when public chains can predict transaction heat, cross-chain bridges can match liquidity demands, and storage networks can optimize data management can Web3 truly possess the capability to 'support mass applications', and the innovation and expansion of the ecosystem can proceed without 'infrastructure bottlenecks'. This is not a fictional technological vision, but a practical implementation based on current pain points in Web3 infrastructure and the on-chain data value, and it is also an inevitable direction for the Web3 industry to shift from 'technical exploration' to 'efficiency implementation'.
