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Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform
Open accessAbstract
The rise in popularity of permissioned blockchain platforms in recent time is significant. Hyperledger Fabric is one such permissioned blockchain platform and one of the Hyperledger projects hosted by the Linux Foundation. The Fabric comprises various components such as smart-contracts, endorsers, committers, validators, and orderers. As the performance of blockchain platform is a major concern for enterprise applications, in this work, we perform a comprehensive empirical study to characterize the performance of Hyperledger Fabric and identify potential performance bottlenecks to gain a better understanding of the system. We follow a two-phased approach. In the first phase, our goal is to understand the impact of various configuration parameters such as block size, endorsement policy, channels, resource allocation, state database choice on the transaction throughput & latency to provide various guidelines on configuring these parameters. In addition, we also aim to identify performance bottlenecks and hotspots. We observed that (1) endorsement policy verification, (2) sequential policy validation of transactions in a block, and (3) state validation and commit (with CouchDB) were the three major bottlenecks. In the second phase, we focus on optimizing Hyperledger Fabric v1.0 based on our observations. We introduced and studied various simple optimizations such as aggressive caching for endorsement policy verification in the cryptography component (3x improvement in the performance) and parallelizing endorsement policy verification (7x improvement). Further, we enhanced and measured the effect of an existing bulk read/write optimization for CouchDB during state validation & commit phase (2.5x improvement). By combining all three optimizations1, we improved the overall throughput by 16x (i.e., from 140 tps to 2250 tps).
Overview
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Written from the paper’s full text.
TL;DR
This paper empirically benchmarks Hyperledger Fabric v1.0 across block size, endorsement policy, channels, resource allocation, and state database choices, identifying three major bottlenecks. It then introduces MSP caching, parallel VSCC validation, and CouchDB bulk read/write optimizations, improving single-channel throughput from 140 tps to 2250 tps (16×).
Key findings
- Fabric v1.0 saturated around 140 tps with GoLevelDB on a single channel; latency rose from hundreds of milliseconds to tens of seconds near saturation because VSCC validation ran serially on one vCPU.
- GoLevelDB throughput was 3× greater than CouchDB (140 tps vs. 50 tps); with CouchDB, increasing writes dropped throughput from 50 tps to 18 tps, while GoLevelDB showed no such impact.
- The three identified bottlenecks were endorsement policy verification, sequential policy validation of transactions in a block, and state validation/commit with CouchDB.
- Optimizations: MSP cache gave 3× improvement, parallel endorsement policy verification gave 7×, and CouchDB bulk read/write gave 2.5×; combined, they improved single-channel throughput from 140 tps to 2250 tps (16×).
Methods
- Empirical study of Hyperledger Fabric v1.0 using over 1000s of experiments, varying block size, endorsement policy, channels, resource allocation, and ledger database.
- Test network: 4 organizations, each with 2 endorsing peers (8 peer nodes total), 1 orderer with Kafka-Zookeeper, on IBM SoftLayer VMs with 32 vCPUs and 3 Gbps network; load generated by three client machines with 56 vCPUs and 128 GB memory.
- Custom benchmark modeled from 12 internal customer solutions, using write-only and read-write transactions; Fabric source was instrumented to collect fine-grained latencies such as VSCC, MVCC, and ledger update latency.
Limitations
- Stated: the ordering service performance was not studied; network was assumed not to be a bottleneck; arrival rates did not follow a distribution model; scalability and fault tolerance are left for future work.
- Stated: the study focuses on Fabric v1.0, though the authors note the optimizations were adopted in v1.1 and core functionality remains similar.
- Evident: results are tied to a specific datacenter setup, hardware allocation, single orderer, and benchmark workloads derived from 12 internal customer solutions, so generalization to other topologies, WAN deployments, or production arrival distributions is not established.
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“With an increase in the number of channels, the percentage of improvement decreased.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > B. Parallel VSCC Validation of a Block
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“With an increase in the number of signatures verification (specifically for NOutOf), the VSCC latency increased linearly from 68 ms to 137 ms.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > B. Impact of Endorsement Policy
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“The performance increased significantly from 50 tps to 115 tps (i.e., by 2.3×) for transactions with a single write.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > C. Bulk Read/Write During MVCC Validation & Commit
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“The maximum throughput achieved with GoLevelDB on a single channel was 140 tps (refer to Figure 4)”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > C. Impact of Channels and Resource Allocation
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“For an arrival rate lower than the saturation point, with an increase in the block size, the latency increased.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > A. Impact of Transaction Arrival Rate and Block Size
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“Thus, the endorsement and commit latency (of successful transactions) observed with 2 vCPUs were lesser than 4 vCPUs as shown in Figure 9(b).”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > C. Impact of Channels and Resource Allocation
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“Note that the block bytes increased with increase in the number of endorsements due to the number of x.509 certificates encoded in each transaction.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > B. Impact of Endorsement Policy
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“For e.g., When the number of vCPUs allocated were lesser than 16 for 16 channels, the throughput reduced significantly from 848 tps to 32 tps (by 26×) -refer to Figure 9(b).”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > C. Impact of Channels and Resource Allocation
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“In contrast, for instance, when the block size was 100 and arrival rate increased from 25 to 75 tps, the latency increased from 900 ms to 1250 ms.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > A. Impact of Transaction Arrival Rate and Block Size
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“As a result, throughput also increased by 3.5%.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > A. Impact of Transaction Arrival Rate and Block Size
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“Observation 9: At moderate loads, even when the number of vCPUs allocated for 2 peers out of 8 were lesser than the channel count, performance degraded.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > C. Impact of Channels and Resource Allocation
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“Even at the peak throughput, the resources utilization was very low.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > A. Impact of Transaction Arrival Rate and Block Size
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“When there were sub-policies (as with 2 nd and 3 rd AND/OR policies), the VSCC latency increased significantly (i.e., 172 ms and 203 ms, respectively).”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > B. Impact of Endorsement Policy
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“The ledger update latency of a block encompassing a higher number of writeonly transactions was higher.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > C. Bulk Read/Write During MVCC Validation & Commit
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“and to 307 tps (by 2.7×) for readwrite transactions”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > C. Impact of Channels and Resource Allocation
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“With an increase in the block size, when CouchDB was the state database, we observed a lower total latency due to the reduction in the number of bulk REST API call to CouchDB”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > D. Combinations of Optimizations
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“with an increase in the transaction complexity, i.e., for multiple writes, the throughput with CouchDB dropped from 50 tps to 18 tps”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > C. Impact of Channels and Resource Allocation
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“the reduction in the VSCC latency (from 300 ms to 30 ms, i.e., by 10× reduction for the block size of 30)”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > B. Parallel VSCC Validation of a Block
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“With higher loads, node rejoining after a failure and syncing up the ledger due to missing blocks was observed to have large delays.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > E. Scalability and Fault Tolerant
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“The average endorsement latency reduced from 800 ms to 40 ms, validating our hypothesis.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > D. Impact of Ledger Database
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“At moderate loads, when the number of vCPUs allocated were lesser than the channel count, performance degraded.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > C. Impact of Channels and Resource Allocation
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“Similarly, with CouchDB as the state database, the singe channel throughput increased to 700 tps from 50 tps (i.e., 14× improvement)”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > D. Combinations of Optimizations
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“A combination of a number of sub-policies and a number of crypto signatures verification impacted the performance as shown in Figure 6.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > B. Impact of Endorsement Policy
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“when only 2 vCPUs were allocated for 2 peers (others with 32 vCPUs), the throughput reduced from 848 tps to 417 tps (by 2×) for write-only transactions”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > C. Impact of Channels and Resource Allocation
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“With an increase in the number of less powerful peers, the endorsement requests timeout increased.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > C. Impact of Channels and Resource Allocation
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“When the arrival rate was close to or above the saturation point, the latency increased significantly (i.e., from an order of 100s of ms to 10s of seconds).”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > A. Impact of Transaction Arrival Rate and Block Size
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“On average, the throughput increased by 3× due to MSP cache as compared to a vanilla peer.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > A. MSP Cache
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“Observation 12: Only with an increase in the number of reads per transaction, the MVCC latency increased as shown in Figure 12(c).”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > D. Impact of Ledger Database
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“Note that the block creation rate at orderer node was always greater than the processing rate at validator irrespective of block size and arrival rate.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > A. Impact of Transaction Arrival Rate and Block Size
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“With GoLevelDB as the state database, the single channel throughput increased to 2250 tps from 140 tps (i.e., 16× improvement) due to all three optimizations”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > D. Combinations of Optimizations
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“various simple optimizations such as aggressive caching for endorsement policy verification in the cryptography component (3× improvement in the performance)”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > Abstract
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“Observation 10: The Fabric transaction throughput with GoLevelDB as state database was 3× greater than CouchDB.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > C. Impact of Channels and Resource Allocation
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“while with CouchDB, it was only 50 tps (refer to Figure 11(b))”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > C. Impact of Channels and Resource Allocation
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“For e.g., when the arrival rate was 50 tps, with an increase in the block size from 10 to 100, the transaction latency increased 5-fold, from 242 ms to 1250 ms.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > A. Impact of Transaction Arrival Rate and Block Size
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“while no such impact with GoLevelDB (refer to Figure 11(a))”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > C. Impact of Channels and Resource Allocation
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“For e.g., when the arrival rate was 150 tps, with an increase in block size from 10 to 200, the transaction latency decreased from 14 secs to 10 secs.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > A. Impact of Transaction Arrival Rate and Block Size
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“Observation 11: With CouchDB, the endorsement latency and ledger update latency increased with an increase in the number of writes per transaction, i.e., from 40 ms and 240 ms with one write to 800 ms and 1200 ms with three writes, respectively”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > D. Impact of Ledger Database
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“By combining all three optimizations 1 , we improved the overall throughput by 16× (i.e., from 140 tps to 2250 tps).”
- Also stated in: § I. INTRODUCTION · plain
“Introduced and studied three simple optimizations 2 to improve the overall performance by 16× (i.e., from 140 tps to 2250 tps) for a single channel environment.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > Abstract
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“In our initial and early study, we observed that node failures do not affect the performance (during non-overloaded case) as client can collect endorsement from other available nodes.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > E. Scalability and Fault Tolerant
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“Further, a higher proportion of total submitted transactions became invalid due to MVCC conflicts.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > C. Impact of Channels and Resource Allocation
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“With an allocation of 2 vCPUs, a higher number of endorsement requests got a timeout as compared to 4 vCPUs.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > C. Impact of Channels and Resource Allocation
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“and parallelizing endorsement policy verification (7× improvement).”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > Abstract
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“With an increase in the number of writes, MVCC latency did not increase as shown in Figure 12(b)”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > D. Impact of Ledger Database
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“As it can be observed, the number of crypto operations and call stack depth reduced significantly with the MSP cache.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > A. MSP Cache
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“For an arrival rate greater than the saturation point, with an increase in the block size, the latency decreased.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > A. Impact of Transaction Arrival Rate and Block Size
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“Hence, with an increase in the sub-policies (i.e., search space), the number of identities & signatures to be validated, both CPU utilization and VSCC latency increased.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > B. Impact of Endorsement Policy
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“The resource utilization such as CPU also increased as shown in Figure 8.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > C. Impact of Channels and Resource Allocation
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“With an increase in transaction arrival rate, the throughput increased linearly as expected till it flattened out at around 140 tps, the saturation point as shown in Figure 4.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > A. Impact of Transaction Arrival Rate and Block Size
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“Further, we enhanced and measured the effect of an existing bulk read/write optimization for CouchDB during state validation & commit phase (2.5× improvement).”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > Abstract
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“Note that the MVCC latency increased with the increase in the number of keys read in a bulk read.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > C. Bulk Read/Write During MVCC Validation & Commit
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“Similarly, the throughput and resource utilization during the overloaded case increased to 1.5× for 16 channels to as much as 10× for 1 channel”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > B. Parallel VSCC Validation of a Block
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“This is because the MSP cache reduced certain repetitive CPU intensive operations.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > A. MSP Cache
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“The MVCC latency for read-write transactions reduced (by at least 6×) due to a bulk reading of all keys in the read set of all transactions in a block.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > C. Bulk Read/Write During MVCC Validation & Commit
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“The usage of database such as GoLevelDB and CouchDB, without the snapshot isolation level, results in whole database lock during the endorsement and the ledger update phase.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > Action Item 5:
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“This is because the sum of MVCC latency and ledger update latency (less CPU intensive tasks) was almost same or higher than the VSCC latency”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > D. Combinations of Optimizations
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“In the lack of standard benchmarks for Blockchain, we built our own benchmarks by surveying around 12 internal customer solutions built on Fabric for diverse use cases.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > A. Setup and Workloads
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“Due to these multiple REST API calls, performance degraded significantly as demonstrated in §V-D.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > C. Bulk Read/Write During MVCC Validation & Commit
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“This is because the number of ordered transactions waiting in the VSCC queue during validation phase grew rapidly (refer to Figure 5) which affected the commit latency.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > A. Impact of Transaction Arrival Rate and Block Size
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“Hence, both the endorsement phase and final ledger update contended for this resource.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > D. Impact of Ledger Database
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“The reason for significant performance differences between CouchDB and GoLevelDB is that the latter is an embedded database to peer process while former is accessed using REST APIs over a secure HTTP.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > C. Impact of Channels and Resource Allocation
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“This is because the time taken to validate and commit a block of size n was always lesser than the time taken to validate and commit m blocks each of size n m .”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > A. Impact of Transaction Arrival Rate and Block Size
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“The reason is that with the increase in rate, the number of transactions in a block increased and so did the time taken by validation and commit phase.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > A. Impact of Transaction Arrival Rate and Block Size
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“This is because with an increase in the number of items in the read set, the number of GET REST API calls to CouchDB increased during MVCC validation phase.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > D. Impact of Ledger Database
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In the paper
“CouchDB supports bulk read/write operations [3] without additional transactional semantics.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > D. Impact of Ledger Database
What the sources say
- Not matchedcites [3]
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In the paper
“This is because, in read-write transactions, the MVCC validation phase itself loaded the required revision numbers into the cache (as the transaction read those keys before modification) which was not the case with write-only transactions.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > C. Bulk Read/Write During MVCC Validation & Commit
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In the paper
“Beyond this, additionally allocated vCPUs were idle due to the single-threaded sequential VSCC validation -refer to Figure 9(c).”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > C. Impact of Channels and Resource Allocation
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In the paper
“Due to these sequential phases, vCPUs were underutilized.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > D. Combinations of Optimizations
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“This is because though the block processing rate at the rejoined node was at the peak, other nodes continues to add new blocks at the same peak processing rate.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > E. Scalability and Fault Tolerant
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“Similarly, the final ledger update phase acquired an exclusive write lock on the whole database.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > D. Impact of Ledger Database
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“Especially, the final ledger update with CouchDB was costlier as it had to perform the following three tasks for each key-value write in a transaction's write-set.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > D. Impact of Ledger Database
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In the paper
“With CouchDB, the VSCC latency increased compared to the GoLevelDB as the peer accessed state database using a REST API call for every transaction to retrieve the endorsement policy of the”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > IX. ACKNOWLEDGEMENTS
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“This is because the endorsement phase acquired a shared read lock on the whole database to provide a consistent view of data (i.e., repeatable read isolation level [17]) to the chaincode.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > D. Impact of Ledger Database
What the sources say
- Not matchedcites [17]
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In the paper
“The reason is that with an increase in the block size, the block creation time at the orderer increased and hence, on average, a transaction had to wait at the orderer for a little longer.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > A. Impact of Transaction Arrival Rate and Block Size
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“This shows that parallel VSCC validation of a block significantly increases the performance of a single channel.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > B. Parallel VSCC Validation of a Block
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“The is because of the lower block commit rate at less powerful peers as compared to powerful peers.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > C. Impact of Channels and Resource Allocation
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“We conducted a comprehensive empirical study of Fabric platform by varying values assigned to the five major parameters by conducting over 1000s of experiments.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > I. INTRODUCTION
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In the paper
“This is because multiple channels by default result in the parallel validation of blocks (instead of transactions) and hence a few number of free vCPUs were available for parallel VSCC.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > B. Parallel VSCC Validation of a Block
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In the paper
“1) Deserialization of identity (i.e., x.509 certificate). 2) Validation of identity with organization MSP [9]. 3) Verification of signature on the transaction data.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > B. Impact of Endorsement Policy
What the sources say
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In the paper
“when the endorsement policy required signature from two endorsers (defined using AND/OR syntax), the maximum throughput achieved without MSP cache was 160 tps while with cache, it increased to 540 tps.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > A. MSP Cache
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“With the increase in the number of channels, the throughput increased and latency decreased.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > C. Impact of Channels and Resource Allocation
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In the paper
“For e.g., when the transaction arrival rate was 100 tps, for the block size of 50 and 100, the block creation rate was 2 and 1 block(s) per second, respectively, causing latency to double.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > A. Impact of Transaction Arrival Rate and Block Size
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In the paper
“However, with further increase in the arrival rate, we observed no impact on the endorsement and broadcast latency but commit latency.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > A. Impact of Transaction Arrival Rate and Block Size
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In the paper
“For a 5 minutes run at an arrival rate of 150 tps, we observed 220K such validation out of which 96K validation failed that resulted in wastage of CPU and time.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > B. Impact of Endorsement Policy
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In the paper
“For multiple writes (3-w & 5-w), the throughput increased from 26 tps to 100 tps (i.e., 3.8× for 3-w), and 18 tps to 90 tps (i.e., 5× for 5-w).”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > C. Bulk Read/Write During MVCC Validation & Commit
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In the paper
“For e.g., with the increase in the number of channels from 1 to 16, the throughput increased from 140 tps to 832 tps (i.e., by 6× in non-overloaded case) and to 1320 tps (i.e., 9.5× in overloaded case).”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > C. Impact of Channels and Resource Allocation
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In the paper
“Further, both the average endorsement and commit latency exploded (from 37 ms to 21 s, and 640 ms to 49 s, respectively) due to a lot of contention on the CPU.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > C. Impact of Channels and Resource Allocation
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In the paper
“The throughput and resource utilization during the non-overloaded case for one channel exploded from 130 tps to 800 tps (improved by 6.3×) for the block size of 30 and to 980 tps (7.5×) for the block size of 300.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > B. Parallel VSCC Validation of a Block
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In the paper
“We observed that (1) endorsement policy verification, (2) sequential policy validation of transactions in a block, and (3) state validation and commit (with CouchDB) were the three major bottlenecks.”
- Also stated in: § I. INTRODUCTION · plain
“We identified three major performance bottlenecks: (i) crypto operations, (ii) serial validation of transactions in a block, and (iii) multiple REST API calls to CouchDB.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > Abstract
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- Also stated in: § I. INTRODUCTION · plain
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In the paper
“For e.g., the throughput achieved with 2 nd & 3 rd AND/OR policies was 7% & 20% lesser than other policies, respectively.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > B. Impact of Endorsement Policy
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Further, these three optimizations have been successfully adopted in Fabric v1. 1”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > VIII. CONCLUSION & FUTURE WORK
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Due to the bulk read/write optimization, the MVCC latency, ledger update latency and endorsement latency decreased as shown in Figure 17(c) and (d) as compared to Figure 12.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > C. Bulk Read/Write During MVCC Validation & Commit
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Further, for 8 and 16 channels, the throughput increased to 2700 tps from 1025 tps and 1321 tps, respectively”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > D. Combinations of Optimizations
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- The authors state this as an assumption: The network is not a bottleneck in the study (a datacenter or high-bandwidth, very-low-latency network is assumed).
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Even with a throughput of 2700 tps, the average CPU utilization of a peer was only 60% and the network utilization of a peer was 1680 Mbps (send) and 240 Mbps (receive).”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > D. Combinations of Optimizations
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“As a result, the endorsement latency, VSCC latency, MVCC latency and the ledger update latency was higher with CouchDB as compared to the GoLevelDB”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > C. Impact of Channels and Resource Allocation
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“With sub-policies, both the number of sub-policies (i.e., search space) and the number of signatures dictated the performance.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > B. Impact of Endorsement Policy
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In this experiment, the endorsement and broadcast latency was around 12 ms and 9 ms, respectively.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > A. Impact of Transaction Arrival Rate and Block Size
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We noticed similar improvements for read-write transactions.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > C. Bulk Read/Write During MVCC Validation & Commit
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The quote only partly states this claim.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The throughput saturated at 950 tps (for the block size of 30) & 1075 tps (for the block size of 300)”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > B. Parallel VSCC Validation of a Block
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“Fabric supports two alternatives for key-value store, CouchDB [2] and GoLevelDB [7] to maintain the current state.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > 4) Resource Allocation.
What the sources say
- Not matchedcites [2]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [7]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, being a minor version update much of the core functionality remains the same and our observations hold true for v1.1 and future versions based on the new architecture of Fabric.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > VII. RELATED WORK
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For lower block sizes and at higher arrival rates, blocks were created faster (rather than waiting for a block timeout) which reduced the transaction waiting time at the orderer node.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > A. Impact of Transaction Arrival Rate and Block Size
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Further, we instrumented the Fabric source code to collect fine-grained latency such as MVCC latency and others latencies.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > IV. EXPERIMENTAL METHODOLOGY
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“because it only checks whether any read keys has been modified.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > D. Impact of Ledger Database
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Due to the CPU contention, the VSCC latency also increased that affected the commit latency as shown in Figure 9(c).”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > C. Impact of Channels and Resource Allocation
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The throughput and transaction latency presented in this section are averaged over multiple runs.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > V. EXPERIMENTAL RESULTS
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“Both are key-value stores, while GoLevelDB is an embedded database, CouchDB uses a client-server model (accessed using REST API over a secure HTTP) and supports document/JSON data-model.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > 4) Resource Allocation.
What the sources say
- Not matchedcites [2]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [7]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The reasons for the reduction are twofold, endorsement requests timeout from less powerful peers and MVCC conflicts specifically for read/write transactions.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > C. Impact of Channels and Resource Allocation
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“The VSCC validation of a transaction' endorsements require evaluation of endorsement policy expression against the collected endorsements and checking for satisfiability [24], which is NP-Complete.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > 1) Block Size.
What the sources say
- Not matchedcites [24]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
In the paper
“Hyperledger Fabric is one such permissioned blockchain platform and one of the Hyperledger projects hosted by the Linux Foundation [13].”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > Abstract
What the sources say
- Not matchedcites [13]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
In the paper
“Hyperledger Fabric [16] is an enterprise-grade open-source permissioned blockchain platform which has a modular design and a high degree of specifiability through trust models and pluggable components.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > I. INTRODUCTION
What the sources say
- Not matchedcites [16]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
The authors state this as an assumption.
The study assumes a datacenter or high bandwidth network with very low latency.
“While network characteristics are important, we assume a datacenter or high bandwidth network with very low latency for this study.”
The authors state this as an assumption.
The orderer, gossip, and physical network were kept static and did not affect the experiments and observations.
“we keep the Orderer, Gossip (physical network) etc. static so that it does not affect our experiments and observations.”
The paper relies on this without support.
There is a lack of standard benchmarks for blockchain.
“In the lack of standard benchmarks for Blockchain, we built our own benchmarks by surveying around 12 internal customer solutions built on Fabric for diverse use cases.”
The authors state this as an assumption.
The network is not a bottleneck in the study (a datacenter or high-bandwidth, very-low-latency network is assumed).
“In our study, we assumed that the network is not a bottleneck.”
“While network characteristics are important, we assume a datacenter or high bandwidth network with very low latency for this study.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“In 2016, blockchain platform Hyperledger Fabric [50,51] introduced multi-ledger based architecture for private network, as shown in Figure 7.”
From A Review of Blockchain Architecture and Consensus Protocols: Use Cases, Challenges, and Solutions (2019) · cites this paper as [51]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > C. Impact of Channels and Resource Allocation
“#Peers with (2, 32) vCPUs (0, 8), (2, 4), (4, 4), (8, 0) peers”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > C. Impact of Channels and Resource Allocation
“Observation 9: At moderate loads, even when the number of vCPUs allocated for 2 peers out of 8 were lesser than the channel count, performance degraded.For e.g., when only 2 vCPUs were allocated for 2 peers (others with 32 vCPUs), the throughput reduced from 848 tps to 417 tps (by 2×)”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > A. Impact of Transaction Arrival Rate and Block Size
“For e.g., when the arrival rate was 150 tps, with an increase in block size from 10 to 200, the transaction latency decreased from 14 secs to 10 secs.”
§ Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform > A. Setup and Workloads
“TABLE II CONFIGURATION TO IDENTIFY THE IMPACT OF BLOCK SIZE AND TRANSACTION ARRIVAL RATE.Parameters Values Tx.Arrival Rate 25, 50, 75, 100, 125, 150, 175 (tps) Block Size 10, 30, 50, 100 (#tx)”
Citations
Cites
Practical Byzantine fault tolerance
Castro, M., & Liskov, B. (1999) · USENIX Symposium on Operating Systems Design and Implementation · doi:10.1145/296806.296824 · Cited by 5,499
Practical Byzantine fault tolerance
Castro, M., & Liskov, B. H. (1999) · Operating Systems Design and Implementation · doi:10.5555/296806.296824 · Cited by 4,026
Hyperledger fabric: a distributed operating system for permissioned blockchains
Androulaki, E., Barger, A., Bortnikov, V., Cachin, C., Christidis, K., De Caro, A., Enyeart, D., Ferris, C., Laventman, G., Manevich, Y., et al. (2018) · Proceedings of the Thirteenth EuroSys Conference · doi:10.1145/3190508.3190538 · Cited by 3,654
On the Security and Performance of Proof of Work Blockchains
Gervais, A., Karame, G., Wüst, K., Glykantzis, V., Ritzdorf, H., & Čapkun, S. (2016) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/2976749.2978341 · Cited by 1,683
On Scaling Decentralized Blockchains
Croman, K., Decker, C., Eyal, I., Gencer, A. E., Juels, A., Kosba, A. E., Miller, A. E., Saxena, P., Shi, E., Sirer, E. G., et al. (2016) · Lecture notes in computer science · doi:10.1007/978-3-662-53357-4_8 · Cited by 1,199
The Quest for Scalable Blockchain Fabric: Proof-of-Work vs. BFT Replication
Vukolić, M. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-39028-4_9 · Cited by 1,051
A critique of ANSI SQL isolation levels
Berenson, H., Bernstein, P., Gray, J., Melton, J., O'Neil, E., & O'Neil, P. (1995) · Proceedings of the 1995 ACM SIGMOD international conference on Management of data - SIGMOD '95 · doi:10.1145/223784.223785 · Cited by 856
Dinh, T. T. A., Wang, J., Chen, G., Liu, R. Z. Z., Ooi, B. C., & Tan, K. (2017) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/3035918.3064033 · Cited by 824
Introducing Ethereum and Solidity
Dannen, C. (2017) · Apress eBooks · doi:10.1007/978-1-4842-2535-6 · Cited by 785
ARC: a self-tuning, low overhead replacement cache
Megiddo, N., & Modha, D. S. (2003) · Cited by 784
Research works printed but not linked: 20
- white, 0, Digitizing Global Trade with Maersk and IBM
- 0, e SDK for Fabric Client/Application
- 0, PostgreSQL as the State Database for Hyperledger Fabric
- 0, PostgreSQL Database Management System
- 0, The Linux Foundation Helps Hyperledger Build the Most Vibrant Open Source Ecosystem for Blockchain
- 0, Quorum an Ethereum-forked variant Blockchain
- 0, SecureKey Building Trusted Identity Networks
- nakamoto, 0, Bitcoin A Peer-to-Peer Electronic Cash System
- 0, CouchDB Document Revision Number
- 0, CouchDB Bulk API
- 0, Go SDK for Fabric Client/Application
- 0, Everledger - A Digital Global Ledger
- 0, Java SDK for Fabric Client/Application
- 0, GoLevelDB
- 0, CouchDB
- 0, Membership Service Providers (MSP)
- 0, Chaincodes
- detwiler, 0, One nations move to increase food safety with blockchain
- keller, 0, New collaboration on trade finance platform built on blockchain
- hearn, 0, Corda A distributed ledger
Cited by
Sorted by citations
Blockchain-based electronic healthcare record system for healthcare 4.0 applications
Tanwar, S., Parekh, K., & Evans, R. D. (2019) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2019.102407 · Cited by 977
A survey on the adoption of blockchain in IoT: challenges and solutions
Uddin, M. A., Stranieri, A., Gondal, I., & Balasubramanian, V. (2021) · Blockchain: Research and Applications · doi:10.1016/j.bcra.2021.100006 · Cited by 365
Systematic Literature Review of Challenges in Blockchain Scalability
Khan, D., Jung, L. T., & Hashmani, M. A. (2021) · Applied Sciences · doi:10.3390/app11209372 · Cited by 385
TrustChain: Trust Management in Blockchain and IoT Supported Supply Chains
Malik, S., Dedeoglu, V., Kanhere, S. S., & Jurdak, R. (2019) · IEEE International Conference on Blockchain (Blockchain) · doi:10.1109/blockchain.2019.00032 · Cited by 329
Performance Evaluation of Blockchain Systems: A Systematic Survey
Fan, C., Ghaemi, S., Khazaei, H., & Musı́lek, P. (2020) · IEEE Access · doi:10.1109/access.2020.3006078 · Cited by 325
Metadata
- Authors
- Thakkar, P., Nathan, S., & Viswanathan, B.
- Affiliations
- National Institute of Technology Tiruchirappalli (IN)
- IBM Research - India (IN)
- Venue
- 2018 IEEE 26th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS)
- Publisher
- IEEE
- Published
- 2018-09
- Pages
- 264-276
- ISSN
- 1526-7539 (ISSN-L)
- OpenAlex
- W2806281946
- Cited by
- 632
- References
- 15
- Citations by year
- 2026: 45 · 2025: 49 · 2024: 49 · 2023: 79 · 2022: 97 · 2021: 118 · 2020: 107 · 2019: 84 · 2018: 4
- FWCI
- 61.41
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Proceedings - International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology in Education and Learning
- Physical Sciences › Computer Science › Computer Networks and Communications › Distributed systems and fault tolerance
- Keywords
- Hyperledger Fabric, permissioned blockchain, blockchain performance, performance bottlenecks, transaction throughput, latency, CouchDB, caching
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-11-10, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-11-10 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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