arXiv subjects

Parwat Singh Anjana

Publications and source records attributed to Parwat Singh Anjana.

10 recordsLinked to original sources

arXiv2026

Blockchain Transaction Conflicts: A Historical Perspective

This paper presents a comprehensive analysis of historical data across two popular blockchain networks: Ethereum and Solana. Our study focuses on two key aspects: transaction conflicts and the maximum theoretical parallelism within historical blocks. We aim to quantify the degree of transaction parallelism and assess how effectively it can be exploited by systematically examining block-level characteristics, both within individual blocks and across different historical periods. In particular, this study is the first of its kind to leverage historical transactional workloads to evaluate conflict patterns. By offering a structured approach to analyzing these conflicts, our research provides valuable insights and an empirical basis for developing more efficient parallel execution techniques for smart contracts in the Ethereum and Solana. Our empirical analysis reveals that historical Ethereum blocks frequently achieve high independence, with over 50\% independent transactions in more than 50\% of blocks, while, on average, Solana blocks contain longer conflict chains $\sim$58\%, compared to $\sim$18\% in Ethereum, reflecting fundamentally different parallel execution dynamics.

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arXiv2025

Efficient Parallel Execution of Blockchain Transactions Leveraging Conflict Specifications

Parallel execution of smart contract transactions in large multicore architectures is critical for higher efficiency and improved throughput. The main bottleneck for maximizing the throughput of a node through parallel execution is transaction conflict resolution: when two transactions interact with the same data, like an account balance, their order matters. Imagine one transaction sends tokens from account A to account B, and another tries to send tokens from account B to account C. If the second transaction happens before the first one, the token balance in account B might be wrong, causing the entire system to break. Conflicts like these must be managed carefully, or you end up with an inconsistent, unusable blockchain state. Traditional software transactional memory (STM) has been identified as a possible abstraction for the concurrent execution of transactions within a block, with Block-STM pioneering its application for efficient blockchain transaction processing on multicore validator nodes. This paper presents a parallel execution methodology that leverages conflict specification information of the transactions for block transactional memory (BTM) algorithms. Our experimental analysis, conducted over synthetic transactional workloads and real-world blocks, demonstrates that BTMs leveraging conflict specifications outperform their plain counterparts on both EVM and MoveVM. Our proposed BTM implementations achieve up to 1.75x speedup over sequential execution and outperform the state-of-the-art Parallel-EVM (PEVM) execution by up to 1.33x across synthetic workloads.

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arXiv2023

An Efficient Framework for Execution of Smart Contracts in Hyperledger Sawtooth

Blockchain technology is a distributed, decentralized, and immutable ledger system. It is the platform of choice for managing smart contract transactions (SCTs). Smart contracts are self-executing codes of agreement between interested parties commonly implemented using blockchains. A block contains a set of transactions representing changes to the system and a hash of the previous block. The SCTs are executed multiple times during the block production and validation phases across the network. The execution is sequential in most blockchain technologies. In this work, we incorporate a direct acyclic graph (DAG) based parallel scheduler framework for concurrent execution of SCTs. The dependencies among a block's transactions are represented through a concurrent DAG data structure that assists in throughput optimization. We have created a DAG scheduler module that can be incorporated into blockchain platforms for concurrent execution with ease. We have also formally established the safety and liveness properties of the DAG scheduler. For evaluation, our framework is implemented in Hyperledger Sawtooth V1.2.6. The performance across multiple smart contract applications is measured for various scheduler types. Experimental analysis shows that the proposed framework achieves notable performance improvements over the parallel SCT execution frameworks.

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arXiv2022

An Efficient Approach to Move Elements in a Distributed Geo-Replicated Tree

Replicated tree data structures are extensively used in collaborative applications and distributed file systems, where clients often perform move operations. Local move operations at different replicas may be safe. However, remote move operations may not be safe. When clients perform arbitrary move operations concurrently on different replicas, it could result in various bugs, making this operation challenging to implement. Previous work has revealed bugs such as data duplication and cycling in replicated trees. In this paper, we present an efficient algorithm to perform move operations on the distributed replicated tree while ensuring eventual consistency. The proposed technique is primarily concerned with resolving conflicts efficiently, requires no interaction between replicas, and works well with network partitions. We use the last write win semantics for conflict resolution based on globally unique timestamps of operations. The proposed solution requires only one compensation operation to avoid cycles being formed when move operations are applied. The proposed approach achieves an effective speedup of 68.19$\times$ over the state-of-the-art approach in a geo-replicated setting on Microsoft Azure standard instances at three different continents.

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arXiv2021

DiPETrans: A Framework for Distributed Parallel Execution of Transactions of Blocks in Blockchain

Contemporary blockchain such as Bitcoin and Ethereum execute transactions serially by miners and validators and determine the Proof-of-Work (PoW). Such serial execution is unable to exploit modern multi-core resources efficiently, hence limiting the system throughput and increasing the transaction acceptance latency. The objective of this work is to increase the transaction throughput by introducing parallel transaction execution using a static analysis technique. We propose a framework DiPETrans for the distributed execution of the transactions in a block. Here, peers in the blockchain network form a community to execute the transactions and find the PoW parallelly, using a leader-follower approach. During mining, the leader statically analyzes the transactions, creates different groups (shards) of independent transactions, and distributes them to followers to execute them in parallel. After the transaction executes, the community's compute power is utilized to solve the PoW concurrently. When a block is successfully created, the leader broadcasts the proposed block to other peers in the network for validation. On receiving a block, validators re-execute the block transactions and accept the block if they reach the same state as shared by the miner. Validation can also be done as a community, in parallel, following the same leader-follower approach as mining. We report experiments using over 5 Million real transactions from the Ethereum blockchain and execute them using our DiPETrans framework to empirically validate the benefits of our techniques over traditional sequential execution. We achieve a maximum speedup of 2.2x for the miner and 2.0x for the validator, with 100 to 500 transactions per block. Further, we achieve a peak of 5x end-to-end block creation speedup using a parallel miner over a serial miner when using 6 machines in the community.

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arXiv2021

OptSmart: A Space Efficient Optimistic Concurrent Execution of Smart Contracts

Popular blockchains such as Ethereum and several others execute complex transactions in blocks through user-defined scripts known as smart contracts. Serial execution of smart contract transactions/atomic-units (AUs) fails to harness the multiprocessing power offered by the prevalence of multi-core processors. By adding concurrency to the execution of AUs, we can achieve better efficiency and higher throughput. In this paper, we develop a concurrent miner that proposes a block by executing the AUs concurrently using optimistic Software Transactional Memory systems (STMs). It captures the independent AUs in a concurrent bin and dependent AUs in the block graph (BG) efficiently. Later, we propose a concurrent validator that re-executes the same AUs concurrently and deterministically using a concurrent bin followed by a BG given by the miner to verify the proposed block. We rigorously prove the correctness of concurrent execution of AUs and achieve significant performance gain over the state-of-the-art.

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arXiv2020

DeHiDe: Deep Learning-based Hybrid Model to Detect Fake News using Blockchain

The surge in the spread of misleading information, lies, propaganda, and false facts, frequently known as fake news, raised questions concerning social media's influence in today's fast-moving democratic society. The widespread and rapid dissemination of fake news cost us in many ways. For example, individual or societal costs by hampering elections integrity, significant economic losses by impacting stock markets, or increases the risk to national security. It is challenging to overcome the spreading of fake news problems in traditional centralized systems. However, Blockchain-- a distributed decentralized technology that ensures data provenance, authenticity, and traceability by providing a transparent, immutable, and verifiable transaction records can help in detecting and contending fake news. This paper proposes a novel hybrid model DeHiDe: Deep Learning-based Hybrid Model to Detect Fake News using Blockchain. The DeHiDe is a blockchain-based framework for legitimate news sharing by filtering out the fake news. It combines the benefit of blockchain with an intelligent deep learning model to reinforce robustness and accuracy in combating fake news's hurdle. It also compares the proposed method to existing state-of-the-art methods. The DeHiDe is expected to outperform state-of-the-art approaches in terms of services, features, and performance.

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arXiv2020

Efficient Concurrent Execution of Smart Contracts in Blockchains using Object-based Transactional Memory

This paper proposes an efficient framework to execute Smart Contract Transactions (SCTs) concurrently based on object semantics, using optimistic Single-Version Object-based Software Transactional Memory Systems (SVOSTMs) and Multi-Version OSTMs (MVOSTMs). In our framework, a multi-threaded miner constructs a Block Graph (BG), capturing the object-conflicts relations between SCTs, and stores it in the block. Later, validators re-execute the same SCTs concurrently and deterministically relying on this BG. A malicious miner can modify the BG to harm the blockchain, e.g., to cause double-spending. To identify malicious miners, we propose Smart Multi-threaded Validator (SMV). Experimental analysis shows that the proposed multi-threaded miner and validator achieve significant performance gains over state-of-the-art SCT execution framework.

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arXiv2019

An Efficient Framework for Optimistic Concurrent Execution of Smart Contracts

Blockchain platforms such as Ethereum and several others execute complex transactions in blocks through user-defined scripts known as smart contracts. To append a correct block into blockchain, miners execute these transactions of smart contracts sequentially. Later the validators serially re-execute the smart contract transactions of the block to validate it. If validation is successful then the block is added to the blockchain using a consensus protocol and miner gets the incentive. In the current era of multi-core processors, by employing the serial execution of the transactions, the miners and validators fail to utilize the cores properly and as a result, have poor throughput. By entitling concurrency to smart contracts execution, we can achieve better efficiency and higher throughput. In this proposal, we develop a novel and efficient technique to execute the smart contract transactions concurrently by miner using optimistic Software Transactional Memory systems (STMs). The miner proposes a block which consists of the set of transactions, conflict graph, hash of previous block and final state of each shared data-objects. Later, we propose concurrent validator which re-executes the same smart contracts concurrently and deterministically with the help of conflict graph given by miner and verifies the final states. On successful validation, proposed block appended into the blockchain and miner gets the incentive. We consider a benchmarks from solidity documentation. We execute the smart contract transactions concurrently using Basic Time stamp Ordering (BTO) and Multi-Version Time stamp Ordering (MVTO) protocols as optimistic STMs. BTO and MVTO miner achieves 3.6x and 3.7x average speedups over serial miner respectively. BTO and MVTO validator outperform average 40.8x and 47.1x than serial validator respectively.

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arXiv2018

Cloud Service Provider Evaluation System using Fuzzy Rough Set Technique

Cloud Service Providers (CSPs) offer a wide variety of scalable, flexible, and cost-efficient services to cloud users on demand and pay-per-utilization basis. However, vast diversity in available cloud service providers leads to numerous challenges for users to determine and select the best suitable service. Also, sometimes users need to hire the required services from multiple CSPs which introduce difficulties in managing interfaces, accounts, security, supports, and Service Level Agreements (SLAs). To circumvent such problems having a Cloud Service Broker (CSB) be aware of service offerings and users Quality of Service (QoS) requirements will benefit both the CSPs as well as users. In this work, we proposed a Fuzzy Rough Set based Cloud Service Brokerage Architecture, which is responsible for ranking and selecting services based on users QoS requirements, and finally monitor the service execution. We have used the fuzzy rough set technique for dimension reduction. Used weighted Euclidean distance to rank the CSPs. To prioritize user QoS request, we intended to use user assign weights, also incorporated system assigned weights to give the relative importance to QoS attributes. We compared the proposed ranking technique with an existing method based on the system response time. The case study experiment results show that the proposed approach is scalable, resilience, and produce better results with less searching time.

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