A Single-Node Performance Evaluation of Hash-Chain and Dependency-Graph Scheduling for Simulated Smart-Contract Workloads in Organic Certification
DOI:
https://doi.org/10.61255/decoding.v4i3.1777Keywords:
Directed acyclic graph, Parallel execution, Performance benchmarking, Smart contracts, Transaction schedulingAbstract
Purpose – This study evaluates when dependency-aware parallel scheduling improves a single-node simulated smart-contract workload for organic certification while separating scheduling effects from hash-chain and dependency-graph record representation.
Methods – A deterministic Python rule engine evaluates 18 certification tasks per submission. The same transactions were executed under four conditions: hash-chain sequential (HS), hash-chain dependency-aware parallel (HP), dependency-graph sequential (DS), and dependency-graph dependency-aware parallel (DP). Main experiments varied 18–900 transactions and 1–3,000 iterative SHA-256 rounds, with five warm-ups and 30 paired technical repetitions. Worker, graph-shape, conflict, functional, integrity, and focused reconciliation tests were also performed.
Findings – Representation-only ratios remained close to unity, whereas scheduling produced the dominant performance effect. At 3,000 rounds, HP/HS and DP/DS speedups were 1.680× and 1.620× at 450 transactions and 1.564× and 1.554× at 900 transactions. The focused reconciliation run showed that eight workers minimized makespan for both 180 and 900 transactions, although per-worker efficiency declined as worker count increased. All 12 functional and integrity tests passed.
Research Implications – Dependency information is useful primarily as a scheduling mechanism; the observed speedup should not be interpreted as evidence that a distributed DAG ledger is intrinsically faster than a hash chain.
Originality – The study provides a carefully documented 2 × 2 single-node benchmark that isolates record representation from execution policy for a simulated certification smart-contract workload.
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References
S. Sharma, ‘Organic Agriculture for Sustainable Food Systems: A Comprehensive Review of Benefits and Constraints’, Turkish J. Agric. - Food Sci. Technol., vol. 12, no. 8, pp. 1476–1481, 2024, doi: 10.24925/turjaf.v12i8.1476-1481.6482.
T. Hu, A. Al Mamun, M. N. H. Reza, M. Wu, and Q. Yang, ‘Examining Consumers’ Willingness to Pay Premium Price for Organic Food’, Humanit. Soc. Sci. Commun., vol. 11, no. 1, p. 1249, 2024, doi: 10.1057/s41599-024-03789-6.
B. Murphy, M. Martini, A. Fedi, B. L. Loera, C. T. Elliott, and M. Dean, ‘Consumer Trust in Organic Food and Organic Certifications in Four European Countries’, Food Control, vol. 133, p. 108484, 2022, doi: 10.1016/j.foodcont.2021.108484.
W. David, S. Alkausar, and B. Widyarti, Statistik Pertanian Organik Indonesia. Jakarta, Indonesia: Universitas Bakrie Press, 2023.
L. Zhang, D. Liu, Q. Yin, and J. Liu, ‘Organic Certification, Online Market Access, and Agricultural Product Prices: Evidence from Chinese Apple Farmers’, Agriculture, vol. 14, no. 5, p. 669, 2024, doi: 10.3390/agriculture14050669.
S. Tegeltija et al., ‘Blockchain Framework for Certification of Organic Agriculture Production’, Sustainability, vol. 14, no. 19, p. 11823, 2022, doi: 10.3390/su141911823.
Y. Huang, X. Li, L. Xu, and Y. Ma, ‘Digital Traceability in Horticulture: A Systematic Review of Edge-Cloud-Blockchain-Terminal (ECBT) Integration with IoT and AI Technologies’, Front. Blockchain, vol. 8, p. 1636627, 2025, doi: 10.3389/fbloc.2025.1636627.
T. H. Pranto, A. A. Noman, A. Mahmud, and A. K. M. B. Haque, ‘Blockchain and Smart Contract for IoT Enabled Smart Agriculture’, PeerJ Comput. Sci., vol. 7, p. e407, 2021, doi: 10.7717/peerj-cs.407.
S. A. Bhat, N.-F. Huang, I. B. Sofi, and M. Sultan, ‘Agriculture-Food Supply Chain Management Based on Blockchain and IoT: A Narrative on Enterprise Blockchain Interoperability’, Agriculture, vol. 12, no. 1, p. 40, 2022, doi: 10.3390/agriculture12010040.
T. T. A. Dinh, J. Wang, G. Chen, R. Liu, B. C. Ooi, and K.-L. Tan, ‘BLOCKBENCH: A Framework for Analyzing Private Blockchains’, in Proceedings of the 2017 ACM International Conference on Management of Data, in SIGMOD ’17. ACM, 2017, pp. 1085–1100. doi: 10.1145/3035918.3064033.
A. L. Bulgakov, A. V Aleshina, S. D. Smirnov, A. D. Demidov, M. A. Milyutin, and Y. Xin, ‘Scalability and Security in Blockchain Networks: Evaluation of Sharding Algorithms and Prospects for Decentralized Data Storage’, Mathematics, vol. 12, no. 23, p. 3860, 2024, doi: 10.3390/math12233860.
E. Androulaki et al., ‘Hyperledger Fabric: A Distributed Operating System for Permissioned Blockchains’, in Proceedings of the Thirteenth EuroSys Conference, in EuroSys ’18. ACM, 2018, pp. 1–15. doi: 10.1145/3190508.3190538.
B. Cao et al., ‘Performance Analysis and Comparison of PoW, PoS and DAG Based Blockchains’, Digit. Commun. Networks, vol. 6, no. 4, pp. 480–485, 2020, doi: 10.1016/j.dcan.2019.12.001.
F. Kahmann, F. Honecker, J. Dreyer, M. Fischer, and R. Tönjes, ‘Performance Comparison of Directed Acyclic Graph-Based Distributed Ledgers and Blockchain Platforms’, Computers, vol. 12, no. 12, p. 257, 2023, doi: 10.3390/computers12120257.
Y. Bai, S. Lee, and S.-H. Seo, ‘A Survey on Directed Acyclic Graph-Based Blockchain in Smart Mobility’, Sensors, vol. 25, no. 4, p. 1108, 2025, doi: 10.3390/s25041108.
J. Huang, C. Liu, and J. Harding, ‘Research on Blockchain Architecture and Operating Principles Based on H-DAG’, Symmetry (Basel)., vol. 15, no. 7, p. 1361, 2023, doi: 10.3390/sym15071361.
T. Noreen, Q. Xia, and M. Z. Haider, ‘Advanced DAG-Based Ranking (ADR) Protocol for Blockchain Scalability’, Comput. Mater. & Contin., vol. 75, no. 2, pp. 2593–2613, 2023, doi: 10.32604/cmc.2023.036139.
X. Lu and C. Jiang, ‘TEEDAG: A High-Throughput Distributed Ledger Based on TEE and Directed Acyclic Graph’, Electronics, vol. 12, no. 11, p. 2393, 2023, doi: 10.3390/electronics12112393.
X. Fu, H. Wang, P. Shi, and X. Zhang, ‘Teegraph: A Blockchain Consensus Algorithm Based on TEE and DAG for Data Sharing in IoT’, J. Syst. Archit., vol. 122, p. 102344, 2022, doi: 10.1016/j.sysarc.2021.102344.
W. Zheng et al., ‘Data Management Method for Building Internet of Things Based on Blockchain Sharding and DAG’, Internet Things Cyber-Physical Syst., vol. 4, pp. 217–234, 2024, doi: 10.1016/j.iotcps.2024.01.001.
R. Xiao, Y. Cao, and B. Xia, ‘Adaptive Tip Selection for DAG-Shard-Based Federated Learning with High Concurrency and Fairness’, Sensors, vol. 25, no. 1, p. 19, 2025, doi: 10.3390/s25010019.
A. Spiegelman, N. Giridharan, A. Sonnino, and L. Kokoris-Kogias, ‘Bullshark: DAG BFT Protocols Made Practical’, in Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security, Association for Computing Machinery, 2022, pp. 2705–2718. doi: 10.1145/3548606.3559361.
A. Villafranca et al., ‘Comparing Blockchain and DAG Technologies for Smart Agriculture Traceability in Terms of Efficiency and Latency’, Simul. Model. Pract. Theory, vol. 142, p. 103131, 2025, doi: 10.1016/j.simpat.2025.103131.
J. Wang, Y. Li, Y. Wu, W. Zheng, S. Zhou, and X. Xiong, ‘Blockchain Sharding Scheme Based on Generative AI and DRL: Applied to Building Internet of Things’, Internet Things Cyber-Physical Syst., vol. 4, pp. 333–349, 2024, doi: 10.1016/j.iotcps.2024.11.001.
T. Xu and Y. Zhang, ‘A Parallel Execution Model for Permissioned Blockchain Systems’, in Proceedings of the 2022 5th International Conference on Blockchain Technology and Applications, Association for Computing Machinery, 2022, pp. 83–87. doi: 10.1145/3581971.3581983.
P. S. Anjana, S. Kumari, S. Peri, S. Rathor, and A. Somani, ‘An Efficient Framework for Optimistic Concurrent Execution of Smart Contracts’, in 2019 27th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP), IEEE, 2019, pp. 83–92. doi: 10.1109/EMPDP.2019.8671637.
R. Gelashvili et al., ‘Block-STM: Scaling Blockchain Execution by Turning Ordering Curse to a Performance Blessing’, in Proceedings of the 28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming, in PPoPP ’23. Association for Computing Machinery, 2023, pp. 232–244. doi: 10.1145/3572848.3577524.
M. Piduguralla, S. Chakraborty, P. S. Anjana, and S. Peri, ‘DAG-Based Efficient Parallel Scheduler for Blockchains: Hyperledger Sawtooth as a Case Study’, in Euro-Par 2023: Parallel Processing, in Lecture Notes in Computer Science, vol. 14100. Springer, 2023, pp. 184–198. doi: 10.1007/978-3-031-39698-4_13.
M. Bartoletti, L. Galletta, and M. Murgia, ‘A Theory of Transaction Parallelism in Blockchains’, Log. Methods Comput. Sci., vol. 17, no. 4, 2021, doi: 10.46298/lmcs-17(4:10)2021.
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