Monitoring Framework
The Data Marketplace monitors on-chain activities like dataset access and Proof Pod performance via smart contract event logs and the native event system.
These events log crucial data such as dataset IDs and timestamps.
Off-chain indexing tools coordinate through off-chain workers to aggregate this into KPIs including throughput (aligned with the base layer's 100–500 TPS capacity), weight usage, and token volume.
This monitoring framework ensures the marketplace operates efficiently within the constraints and capabilities of the underlying blockchain.
For example, an AccessExtended event might log a dataset ID and timestamp, tracking 1,000 accesses/hour for a popular dataset.
Testnet will assess scalability with 1,000 users, targeting 1-second updates.
Off-Chain Monitoring for Infrastructure Health
Off-chain monitoring focuses on IPFS availability (target: 99.9% uptime, consistent with the base layer specifications) and Proof Pod performance metrics that align with the ZKP Blockchain’s parameters.
These are coordinated through off-chain workers, where IPFS probes can measure retrieval latency (<1 second for 1 GB, illustrative) across five nodes per dataset, flagging downtime.
Off-chain monitoring focuses on IPFS availability (target: 99.9% uptime, consistent with the base layer specifications) and Proof Pod performance metrics that align with the ZKP Blockchain’s parameters.
These are coordinated through off-chain workers, where IPFS probes can measure retrieval latency (<1 second for 1 GB, illustrative) across five nodes per dataset, flagging downtime.
Off-chain monitoring focuses on IPFS availability (target: 99.9% uptime, consistent with the base layer specifications) and Proof Pod performance metrics that align with the ZKP Blockchain’s parameters.
These are coordinated through off-chain workers, where IPFS probes can measure retrieval latency (<1 second for 1 GB, illustrative) across five nodes per dataset, flagging downtime.
Off-chain monitoring focuses on IPFS availability (target: 99.9% uptime, consistent with the base layer specifications) and Proof Pod performance metrics that align with the ZKP Blockchain’s parameters.
These are coordinated through off-chain workers, where IPFS probes can measure retrieval latency (<1 second for 1 GB, illustrative) across five nodes per dataset, flagging downtime.
APIs (e.g., REST) might track network health, with testnet validating 100 nodes for >99% coverage.
Privacy-Preserving Monitoring
Monitoring aims to potentially ensure confidentiality by logging anonymized data (e.g., dataset IDs), avoiding user identities.
Future zero-knowledge proofs verified through the verification infrastructure might enhance privacy, with testnet targeting <1% linkage risk (illustrative) for 100 users.
Monitoring aims to potentially ensure confidentiality by logging anonymized data (e.g., dataset IDs), avoiding user identities.
Future zero-knowledge proofs verified through the verification infrastructure might enhance privacy, with testnet targeting <1% linkage risk (illustrative) for 100 users.
Monitoring aims to potentially ensure confidentiality by logging anonymized data (e.g., dataset IDs), avoiding user identities.
Future zero-knowledge proofs verified through the verification infrastructure might enhance privacy, with testnet targeting <1% linkage risk (illustrative) for 100 users.
Monitoring aims to potentially ensure confidentiality by logging anonymized data (e.g., dataset IDs), avoiding user identities.
Future zero-knowledge proofs verified through the verification infrastructure might enhance privacy, with testnet targeting <1% linkage risk (illustrative) for 100 users.
Practical Implications
The system seeks to potentially offer insights, possibly informing tier adjustments (e.g., 1,000 accesses/hour) and workload optimization (>90% Proof Pod completion, illustrative), with testnet refining privacy and scalability.
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