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<p>The blockchain trilemma, involving the balance of scalability, security, and decentralization, remains a critical challenge in blockchain networks. Layer 2 (L2) solutions, especially rollups, have emerged as promising approaches to enhancing scalability. They execute transactions on an auxiliary L2 chain and submit batched results to the Layer 1 (L1) blockchain, preserving L1 security and decentralization while significantly increasing throughput and reducing transaction costs. However, current rollup mechanisms primarily focus on batching and compression techniques without dynamically optimizing transaction execution based on resource utilization. This paper extends our previous work on AI-driven opcode smart contract classification, presented at the ISIVC2024 conference [<CitationRef CitationID="CR1">1</CitationRef>], by introducing a new layer of optimization through an opcode-based adaptive rollup execution strategy. By analyzing opcode sequences of smart contract transactions, we categorize transactions based on computational complexity and resource requirements. Our adaptive batching algorithm prioritizes transactions using an opcode-based score, forming batches that optimize gas consumption, enhance throughput, and improve processing efficiency within rollup mechanisms. Additionally, we incorporate dynamic scheduling algorithms within the sequencer, utilizing machine learning models to predict optimal execution orders and adjust strategies based on real-time network conditions. Our analysis evaluates the performance of our adaptive batching algorithm against traditional methods and assesses the dynamic scheduling approach as an enhancement to our model. The results indicate improvements in sequencer efficiency and resource utilization during rollup transaction execution. This research contributes to addressing the blockchain scalability trilemma by offering an adaptive approach that responds to evolving blockchain network demands.</p>

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Adaptive rollup execution: dynamic opcode-based sequencing for smart transaction ordering in Layer 2 rollups

  • 期刊论文

  • 发表日期：

    2025年2月20日

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  • Inas Hasnaoui,
  • Maria Zrikem,
  • Raja Elassali

摘要

The blockchain trilemma, involving the balance of scalability, security, and decentralization, remains a critical challenge in blockchain networks. Layer 2 (L2) solutions, especially rollups, have emerged as promising approaches to enhancing scalability. They execute transactions on an auxiliary L2 chain and submit batched results to the Layer 1 (L1) blockchain, preserving L1 security and decentralization while significantly increasing throughput and reducing transaction costs. However, current rollup mechanisms primarily focus on batching and compression techniques without dynamically optimizing transaction execution based on resource utilization. This paper extends our previous work on AI-driven opcode smart contract classification, presented at the ISIVC2024 conference [1], by introducing a new layer of optimization through an opcode-based adaptive rollup execution strategy. By analyzing opcode sequences of smart contract transactions, we categorize transactions based on computational complexity and resource requirements. Our adaptive batching algorithm prioritizes transactions using an opcode-based score, forming batches that optimize gas consumption, enhance throughput, and improve processing efficiency within rollup mechanisms. Additionally, we incorporate dynamic scheduling algorithms within the sequencer, utilizing machine learning models to predict optimal execution orders and adjust strategies based on real-time network conditions. Our analysis evaluates the performance of our adaptive batching algorithm against traditional methods and assesses the dynamic scheduling approach as an enhancement to our model. The results indicate improvements in sequencer efficiency and resource utilization during rollup transaction execution. This research contributes to addressing the blockchain scalability trilemma by offering an adaptive approach that responds to evolving blockchain network demands.