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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
AI snapshot
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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In the paper
“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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In the paper
“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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In the paper
“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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In the paper
“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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In the paper
“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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In the paper
“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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In the paper
“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 the paper
“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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In the paper
“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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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
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- Not matchedcites [9]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
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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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In the paper
“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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“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
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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
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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
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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
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- 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).
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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
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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
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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
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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
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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
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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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
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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.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
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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
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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
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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
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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
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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
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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.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
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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.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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?
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]
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.
Heterogeneous-peer configuration accounts for only six peers
MinorTable VI includes a configuration with two peers at 2 vCPUs and four at 32 vCPUs, totaling six. Observation 9 describes the corresponding case as two less-powerful peers out of eight, with the other peers at 32 vCPUs.
“#Peers with (2, 32) vCPUs (0, 8), (2, 4), (4, 4), (8, 0) peers”
“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×)”
Block size of 200 is reported but not listed in the experiment configuration
MinorTable II lists block sizes of 10, 30, 50, and 100 transactions, but Observation 3 describes a result for a block size of 200.
“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.”
“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)”
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Jahn, R. G., Kraft, O., Klaes, M., Schmid, D., Rehtanz, C., & Häger, U. (2022) · IEEE Mediterranean Electrotechnical Conference (MELECON) · doi:10.1109/melecon53508.2022.9843109 · Cited by 4
Embedded Systems and Applications
(2022) · doi:10.5121/csit.2022.1206 · Cited by 4
A Blockchain-based Edge Computing Architecture for the Internet of ThingsPreprint
Tošić, A., Vičič, J., Burnard, M., & Mrissa, M. (2022) · Preprints.org · doi:10.20944/preprints202111.0489.v2 · Cited by 4
Zhou, Z., Feng, Y., & Sakurai, K. (2026) · Computers · doi:10.3390/computers15020108 · Cited by 3
A Blockchain-Based Approach to Streamlining Land Registration using Hyperledger Fabric
Dcunha, L., Dsouza, L., Ramanan, G., & Chaudhari, S. (2023) · International Conference on Emerging Smart Computing and Informatics (ESCI) · doi:10.1109/esci56872.2023.10099592 · Cited by 3
Intelligent Transaction Generation Control for Permissioned Blockchain-Based Services
Kim, D.-S., Yun, S., Lee, S., Lee, J., & Niyato, D. T. (2025) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2025.3528318 · Cited by 4
Blockchain-enabled road vehicle emissions monitoring: A secure, scalable and private framework
O’Brien, D., Christaras, V., Kounelis, I., Fovino, I. N., & Fontaras, G. (2025) · Internet of Things · doi:10.1016/j.iot.2025.101628 · Cited by 3
Secure and Transparent Space Exploration Data Management Using a Hybrid Blockchain Model
Kim, J., Cartagena, M., & Kim, S. (2025) · Applied Sciences · doi:10.3390/app15116060 · Cited by 3
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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