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 14.6× to 68.19× over the state-of-the-art approach in a geo-replicated setting.

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. 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 operation timestamps. 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 14.6-68.19× over the state-of-the-art approach in a geo-replicated setting,

Summary 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 over the transaction dependencies. We propose the DiPETrans framework for distributed execution of transactions in a block. Here, peers in the blockchain network form a community of trusted nodes to execute the transactions and find the PoW in‐parallel, 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 concurrently. After execution, 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, the 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 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 a traditional sequential execution. We achieve a maximum speedup of 2.2 and 2.0 and an average speedup of 1.6 and 1.5 for the miner and the validator, respectively, with 100–500 transactions per block when using 6 machines in the community. Further, we achieve a peak of 5 end‐to‐end block creation speedup using a parallel miner over a serial miner.

Blockchain platforms such as Ethereum and several others execute complex transactions in blocks through user-defined scripts known as smart contracts. Normally, a block of the chain consists of multiple transactions of smart contracts which are added by a miner. To append a correct block into the blockchain, miners execute these transactions of smart contracts sequentially. Later the validators serially re-execute the smart contract transactions of the block. If the validators agree with the final state of the block as recorded by the miner, then the block is said to be validated. It is then added to the blockchain using a consensus protocol. In Ethereum and other blockchains that support cryptocurrencies, a miner gets an incentive every time such a valid block successfully added to the blockchain. In most of the current day blockchains the miners and validators execute the smart contract transactions serially. 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 adding concurrency to smart contracts execution, we can achieve better efficiency and higher throughput. In this paper, we develop an efficient framework to execute the smart contract transactions concurrently using optimistic Software Transactional Memory systems (STMs). Miners execute smart contract transactions concurrently using multi-threading to generate the final state of blockchain. STM is used to take care of synchronization issues among the transactions and ensure atomicity. Now when the validators also execute the transactions (as a part of validation) concurrently using multi-threading, then the validators may get a different final state depending on the order of execution of conflicting transactions. To avoid this, the miners also generate a block graph of the transactions during the concurrent execution and store it in the block. This graph captures the conflict relations among the transactions and is generated concurrently as the transactions are executed by different threads. The miner proposes a block which consists of set of transactions, block graph, hash of the previous block, and final state of each shared data-objects. Later, the validators re-execute the same smart contract transactions concurrently and deterministically with the help of block graph given by the miner to verify the final state. If the validation is successful then proposed block appended into the blockchain and miner gets incentive otherwise discard the proposed block. 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. Along with, BTO and MVTO validator outperform average 40.8x and 47.1x than serial validator respectively.