Atomic Transactions Research Articles

StarCross: Redactable blockchain-based secure and lightweight data sharing framework for satellite-based IoT

Due to limitations in network coverage and capacity, terrestrial networks cannot meet the growing demand for widespread intelligent applications in recent years. The increasingly popular Satellite-based Internet of Things (S-IoT), with its extensive and boundless coverage, has emerged as the optimal solution. However, the large-scale distributed communication and services have led to significant challenges in scalability and security. Blockchain can help mitigate security and privacy issues in data sharing of S-IoT. To address these challenges, we propose StarCross, a secure and lightweight data sharing framework for S-IoT based on redactable blockchain. Firstly, we develop a space-ground collaborative sharding blockchain network architecture. Low Earth Orbit (LEO) satellite network serves as the communication intermediary between shards. Secondly, StarCross employs an improved Proof of Work (PoW) consensus based on dynamic difficulty to mitigate the challenges of uneven computing power and centralization within each shard. To ensure the atomicity of cross-shard transactions, StarCross introduces a blockchain rewriting mechanism called RBCVC, which combines chameleon hash and voting-based consensus. Thirdly, we conducted theoretical analysis and experimental evaluations of StarCross. The results indicate that compared to existing solutions, StarCross achieves better scalability and security in S-IoT. StarCross’s TPS increased to 260.9% of the baseline, while the transaction confirmation latency decreased by 79.7%.

AChain: A SQL-Empowered Analytical Blockchain as a Database

In various multi-party cooperations data stored on blockchains (i.e., on-chain data) should be decentralized consistent, verifiable, traceable, and immutable. Online analytical processing (OLAP) services are critical requirements in these applications. However, OLAP performances of existing blockchain systems are much worse than those of relational databases due to the lack of SQL support. In this paper, we propose a novel SQL-empowered analytical blockchain framework, aChain. It fully provides SQL-based OLAP services, while keeping the secure characteristics. Specifically, aChain relationally reorganizes on-chain data to support full SQL executions. Then, a relational versioning scheme is designed to ensure the atomicity and consistency of transactions. Furthermore, SQL-based APIs are designed based on an execute-order-validate architecture. Finally, we demonstrate that the performance of aChain and MySQL (in both cluster and non-cluster models) is at the same level on a typical OLAP benchmark, TPC-H.

On the Effects of Transaction Data Access Patterns on Performance in Lock-Based Concurrency Control

Transaction Processing (TP) plays a primary role in the design and implementation of IT applications and services. Many TP systems exploit lock-based concurrency control to guarantee atomicity and isolation of transactions that access shared data. In this article, we show that transaction data access patterns, in particular the order of data accesses along the transaction execution, have a noticeable impact on how lock-based concurrency control affects performance. We show that the performance can remarkably change depending on whether transactions, or a percentage of them, access data items following some common ordering rule or not. We investigate on this aspect and its root causes through an analytical modeling approach, and with the evidence of data gathered through both simulation and the execution of real transactional workloads. Finally, we show how the findings of our study can be easily exploited for improving the performance of common transactional workloads.

Davida: A Decentralization Approach to Localizing Transaction Sequences for Debugging Transactional Atomicity Violations

Atomicity is a desirable property for multithreaded programs. In such programs, a transaction is an execution of an atomic code region that may contain memory accesses on an arbitrary number of shared variables. When transactions are not conflicting with one another in a trace, they greatly simplify the reasoning of the program correctness. If a transaction incurs an atomicity violation in a trace, developers have to debug the code, but this is challenging. To achieve practical runtime performances, existing dynamic techniques for detecting such atomicity violations face a challenge: They are designed for either detecting all such atomicity violations without the capability of localizing the corresponding cross-thread transaction sequences or deliberately missing some atomicity violations in the trade of localizing some of them to support their atomicity violation detection. In this article, we propose Davida, a novel technique to address this problem. Davida efficiently tracks selective transactions and cross-thread dependency sequences over transactions reachable from the currently active transactions of all the threads in a decentralized manner. We prove that Davida precisely accomplishes every atomicity violation in a trace with an actual sequence of transactions triggering the violation. The experimental results on 15 subjects showed that Davida outperformed Velodrome, the previous graph-based state-of-the-art technique, in both performance and completeness.

On Atomicity and Confidentiality Across Blockchains Under Failures

Distributed applications that utilize <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">heterogeneous</i> blockchain systems have the potential to be widely deployed. In such applications, users from different blockchains can transact with one another through <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cross-chain transactions</i> . There are two essential features of particular relevance for those applications during cross-chain transactions: the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">atomicity</i> in that either all or none of the blockchains involved confirm a cross-chain transaction, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">confidentiality</i> in that a blockchain involved in a cross-chain transaction is only accessible for designated users. Existing cross-chain proposals have largely relied on permissioned blockchains to ensure confidentiality. However, we found that failures could occur when reading or writing information during transaction confirmations across permissioned blockchains, namely read/write (r/w) failures, which can lead to the violation of atomicity. In this paper, we propose a novel mechanism, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Unity</i> , to ensure both atomicity and confidentiality of cross-chain transactions under r/w failures by leveraging permissioned blockchains. When failures occur in reading or writing data, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Unity</i> classifies the data into two categories based on its status - whether data is the latest version or not, and presents different solutions for atomicity. Specifically, when data is not the latest, we design a four-phase-commit protocol4pc, in which consensus on confirming or aborting a cross-chain transaction can be achieved. If data is the latest when r/w failures occur, we propose a smart contract based solution SSC. We examine the effectiveness of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Unity</i> theoretically and through experiments. With a failure probability of 0.7, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Unity</i> achieves <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$98\%$</tex-math></inline-formula> more atomic cross-chain transactions when compared with the state-of-the-art cross-chain platform, Hyperservice.

Meepo: Multiple Execution Environments per Organization in Sharded Consortium Blockchain

Blockchain performance cannot meet the requirement nowadays. One of the crucial ways to improve performance is sharding. However, most blockchain sharding research focuses on the public blockchain. As for consortium blockchain, previous studies cannot support high cross-shard efficiency, multiple-shard contract calling, strict transaction atomicity, and shard availability, which are essential requirements but also challenges in consortium blockchain systems. Facing these challenges, we propose Meepo, a systematic study on sharded consortium blockchain. Meepo enhances cross-shard efficiency via the cross-epoch and cross-call. Moreover, a partial cross-call merging strategy is designed to handle the multi-state dependency in contract calls, achieving flexible multiple-shard contract calling. Meepo employs a replay-epoch to ensure strict transaction atomicity, and it also uses a backup algorithm called shadow shard based recovery to improve the shard robustness. On a test-bed of 128 AliCloud servers, setting 32 shards and 4 consortium members, Meepo-OpenEtheruem can achieve more than 140,000 cross-shard TPS under the workload of 100,000,000 asset transactions. It also shows more than 50,000 TPS under the transactions of real-world shopping behaviors.

A critical evaluation of handling uncertainty in Big Data processing

Big Data is a modern economic and social transformation driver all over the world. The selection has reached a tipping point in terms of big technological advancements that could usher in new ones. It's about making decisions, taking charge of our health, our cities, our wallets, and our education. While the amount, diversity, speed, and reliability of data has increased, so has the complexity of the data (Amzal et al., 2006). Our ability to identify the "value of information through Big Data" will have the most impact. As a result of the resolution of large data concerns, Big Data is revolutionising the IT sector Software vendors in many industries are unable to move forward with dependability and scalability, Information on the project. With the help of relational databases, the explanation for why business requires adequate atomic transactions in the business sector. As a result, to benefit from both technology, advantages, and solutions. Big Data, which refers to databases that are too huge to be processed with current database administration tools, is gaining traction in a variety of significant applications, including internet search, business, social networking, genomics, and meteorology [2]. This concept highlights key research challenges and the promise of data-driven optimization that organically integrates fuzzy, machine learning, and deep learning for decision-making under uncertainty, and identifies potential research opportunities in the business field of Bayesian optimization under uncertainty through a modern data lens. Big Data is a big issue for science and database data mining. Here, we look at the fascinating activities that my community has been doing at this conference to talk about the Big Data problem. As a result, the central focus is linking Big Data to individuals in numerous ways.

Understanding the Benefit of Being Patient in Payment Channel Networks

Scaling blockchain efficiency is crucial to its widespread usage in which the payment channel is one of the most prominent approaches. With payment channels and the network they construct, two users can move some transactions off-chain to avoid expensive and time-consuming on-chain settlements. Existing works are devoted to designing high-throughput payment channel networks (PCNs) or efficient PCN routing policies to reduce the fee charged by intermediate nodes. In this paper, we investigate the PCN routing from a different perspective by answering whether the routing fee of transactions can be saved through being a bit more patient. The key idea is to reorder the processing sequence of atomic transactions, other than to handle each of them separately and immediately. We present two mechanisms, one is periodic transaction processing and the other is purely strategic waiting. In the former, the incoming transactions in a short time interval are processed collectively. We formulate an optimization model to minimize their total routing fee and derive the optimal permutation of processing transactions as well as the routing policy for each of them. A <i>Shapley value</i> based scheme is presented to redistribute the benefit of reordering among the transactions efficiently and fairly. In the latter, we model the waiting time of a strategic transaction on a single payment channel as the <i>first passage time</i> problem in queuing theory when the transaction value is higher than the channel balance upon its arrival. By capturing the balance dynamics, we are able to calculate the recursive expression of waiting time distribution that is useful to gauge a user&#x0027;s cost of patience. Experimental results manifest that our cost redistribution mechanism can effectively save routing fees for all the transactions, and the waiting time distribution coincides with the model well.

Efficient Cross-Chain Transaction Processing on Blockchains

Blockchain has received great attention in academia and industry due to its decentralization and immutability. From the perspective of transaction processing, blockchain is a distributed shared ledger and database with the characteristics of decentralization, traceability, and transparency. These features ensure the security of blockchain’s reliability. However, because a blockchain network requires complex consensus verification between users, it causes problems such as a high cost of data exchange and a low system throughput. Such problems are aggravated when executing a cross-chain transaction, as it is particularly important to ensure the atomicity and isolation of transactions across the blockchain. Considering this, in this paper, we propose the cross-chain transaction processing flow of EOVPC and efficient transaction processing based on version control. Different from the existing cross-chain transaction approaches based on locking, we propose optimistic approaches in which the updated data can be used immediately, with a rolling back procedure that guarantees atomicity. We conducted extensive experiments, which show that our approaches can improve the throughput and success rate significantly.

All in One: Design, Verification, and Implementation of SNOW-optimal Read Atomic Transactions

Distributed read atomic transactions are important building blocks of modern cloud databases that magnificently bridge the gap between data availability and strong data consistency. The performance of their transactional reads is particularly critical to the overall system performance, as many real-world database workloads are dominated by reads. Following the SNOW design principle for optimal reads, we develop LORA, a novel SNOW-optimal algorithm for distributed read atomic transactions. LORA completes its reads in exactly one round trip, even in the presence of conflicting writes, without imposing additional overhead to the communication, and it outperforms the state-of-the-art read atomic algorithms. To guide LORA’s development, we present a rewriting-logic-based framework and toolkit for design, verification, implementation, and evaluation of distributed databases. Within the framework, we formalize LORA and mathematically prove its data consistency guarantees. We also apply automatic model checking and statistical verification to validate our proofs and to estimate LORA’s performance. We additionally generate from the formal model a correct-by-construction distributed implementation for testing and performance evaluation under realistic deployments. Our design-level and implementation-based experimental results are consistent, which together demonstrate LORA’s promising data consistency and performance achievement.

Blockchain Interoperability: Towards a Sustainable Payment System

The highly fragmented blockchain and cryptocurrency ecosystem necessitates interoperability mechanisms as a requirement for blockchain-technology acceptance. The immediate implication of interchain interoperability is automatic swapping between cryptocurrencies. We performed a systematic review of the existing literature on Blockchain interoperability and atomic cross-chain transactions. We investigated different blockchain interoperability approaches, including industrial solutions, categorized them and identified the key mechanisms used, and list several example projects for each category. We focused on the atomic transactions between blockchain, a process also known as atomic swap. Furthermore, we studied recent implementations along with architectural approaches for atomic swap and deduced research issues and challenges in cross-chain interoperability and atomic swap. Atomic swap can instantly transfer tokens and significantly reduce the associated costs without using any centralized authority, and thus facilitates the development of a sustainable payment system for wider financial inclusion.

Which Event Happened First? Deferred Choice on Blockchain Using Oracles

First come, first served: Critical choices between alternative actions are often made based on events external to an organization, and reacting promptly to their occurrence can be a major advantage over the competition. In Business Process Management (BPM), such deferred choices can be expressed in process models, and they are an important aspect of process engines. Blockchain-based process execution approaches are no exception to this, but are severely limited by the inherent properties of the platform: The isolated environment prevents direct access to external entities and data, and the non-continual runtime based entirely on atomic transactions impedes the monitoring and detection of events. In this paper we provide an in-depth examination of the semantics of deferred choice, and transfer them to environments such as the blockchain. We introduce and compare several oracle architectures able to satisfy certain requirements, and show that they can be implemented using state-of-the-art blockchain technology.

Cross-chain deals and adversarial commerce

Modern distributed data management systems face a new challenge: how can autonomous, mutually distrusting parties cooperate safely and effectively? Addressing this challenge brings up familiar questions from classical distributed systems: how to combine multiple steps into a single atomic action, how to recover from failures, and how to synchronize concurrent access to data. Nevertheless, each of these issues requires rethinking when participants are autonomous and potentially adversarial. We propose the notion of a cross-chain deal, a new way to structure complex distributed computations that manage assets in an adversarial setting. Deals are inspired by classical atomic transactions, but are necessarily different, in important ways, to accommodate the decentralized and untrusting nature of the exchange. We describe novel safety and liveness properties, along with two alternative protocols for implementing cross-chain deals in a system of independent blockchain ledgers. One protocol, based on synchronous communication, is fully decentralized, while the other, based on semi-synchronous communication, requires a globally shared ledger. We also prove that some degree of centralization is required in the semi-synchronous communication model.

Medical Record Aggregation Using Blockchain Technology

Healthcare domain and a reliable method to store every patient’s data record securely by keeping protection of privacy in mind are considered to be one of the major concerns in the field of study and implementation. An efficient decentralized system for control accessed interoperability of transactions or data between the owner (patient) and the healthcare practitioner is required. This would also ensure a complete and accurate medical history of the patient and allow the full use of it by the health organization as well. The rise in blockchain technology has enabled to build the needs to bridge the gap between the patient and practitioner to securely store and utilize personal data digitally. This paper is a study on proposed implementations and a scope of those medical health aggregation methods have been discussed along with an idea to improve the existing system. A two-level access-controlled mechanism of the patient’s private medical record and granting the requested access or transfer of ownership properties to the practitioner in any chain of a hospital is proposed. Hence, a decentralized, automatically verifiable, transparent and immutable smart e-healthcare card medical records aggregation system could be designed using the Hyperledger Sawtooth framework built on the blockchain technology platform. Three algorithms are being proposed to ensure an atomic transaction - Proof of Work (PoW), Byzantine Fault Tolerance (BFT) and Asymmetric Cryptography (like RSA) algorithm. The aim is to implement the proposed model and strive towards the scope of betterment in the field of healthcare. The health data is planned to be stored in a format like Electronic Health Records (EHR).

Next generation stock exchange: Recurrent neural learning model for distributed ledger transactions

A distributed stock exchange system encompasses multiple network hosts that participate in the sharing and exchange of resources. In such exchanges, the mediator or stock exchange must manage and delineate all operations in a cohesive manner. Stock exchange (SE) also acts as the transaction manager to provide consistent, isolated, durable, and atomic transactions for participating entities. However, the work for the stock exchange is not so straightforward as it may sound. With multiple transactions happening per second, the global serializability and concurrency control becomes an issue resulting in multiple threats and vulnerabilities. We propose a novel stock exchange that integrates time series prediction to distributed transactions and understanding the rapid global transactions and limitations of resources at the stock exchange. We use distributed acyclic graph (DAG) based distributed ledger technology IOTA to provide security and consensus for independent users. The paper proposes a time-variant model that adjusts its predictions based on transactions, moments of observations, participating entities, and history. We show that our model outcasts other state-of-art schemes in terms of prediction accuracy. Also, the model is fair, fast, and scalable to handle millions of transactions per second.

On the continuous contract verification using blockchain and real-time data

Supply chains play today a crucial role in the success of a company’s logistics. In the last years, multiple investigations focus on incorporating new technologies to the supply chains, being Internet of Things (IoT) and blockchain two of the most recent and popular technologies applied. However, their usage has currently considerable challenges, such as transactions performance, scalability, and near real-time contract verification. In this paper we propose a model for continuous verification of contracts in supply chains using the benefits of blockchain technology and real-time data acquisition from IoT devices for early decision-making. We propose two platform independent optimization techniques (atomic transactions and grouped validation) that enhances data transactions protocol and the data storage procedure and a method for continuous verification of contracts, which allows to take corrective actions to reduce operational costs and increase benefits in current supply chains. An automatic deployment of a large scale distributed business logic system using virtualized appliances is also proposed. Evaluation results show the feasibility or the solution proposed.

RegionTrack

Atomicity is a correctness criterion to reason about isolated code regions in a multithreaded program when they are executed concurrently. However, dynamic instances of these code regions, called transactions , may fail to behave atomically, resulting in transactional atomicity violations. Existing dynamic online atomicity checkers incur either false positives or false negatives in detecting transactions experiencing transactional atomicity violations. This article proposes &lt;monospace&gt;RegionTrack&lt;/monospace&gt;. &lt;monospace&gt;RegionTrack&lt;/monospace&gt; tracks cross-thread dependences at the event, dynamic subregion, and transaction levels. It maintains both dynamic subregions within selected transactions and transactional happens-before relations through its novel timestamp propagation approach. We prove that &lt;monospace&gt;RegionTrack&lt;/monospace&gt; is sound and complete in detecting both transactional atomicity violations and non-serializable traces. To the best of our knowledge, it is the first online technique that precisely captures the transitively closed set of happens-before relations over all conflicting events with respect to every running transaction for the above two kinds of issues. We have evaluated &lt;monospace&gt;RegionTrack&lt;/monospace&gt; on 19 subjects of the DaCapo and the Java Grande Forum benchmarks. The empirical results confirm that &lt;monospace&gt;RegionTrack&lt;/monospace&gt; precisely detected all those transactions which experienced transactional atomicity violations and identified all non-serializable traces. The overall results also show that &lt;monospace&gt;RegionTrack&lt;/monospace&gt; incurred 1.10x and 1.08x lower memory and runtime overheads than &lt;monospace&gt;Velodrome&lt;/monospace&gt; and 2.10x and 1.21x lower than &lt;monospace&gt;Aerodrome&lt;/monospace&gt;, respectively. Moreover, it incurred 2.89x lower memory overhead than &lt;monospace&gt;DoubleChecker&lt;/monospace&gt;. On average, &lt;monospace&gt;Velodrome&lt;/monospace&gt; detected about 55% fewer violations than &lt;monospace&gt;RegionTrack&lt;/monospace&gt;, which in turn reported about 3%–70% fewer violations than &lt;monospace&gt;DoubleChecker&lt;/monospace&gt;.

SPDS: A Secure and Auditable Private Data Sharing Scheme for Smart Grid Based on Blockchain

The exponential growth of data generated from increasing smart meters and smart appliances brings about huge potentials for more efficient energy production, pricing, and personalized energy services in smart grids. However, it also causes severe concerns due to improper use of individuals' private data, as well as the lack of transparency and auditability for data usage. To bridge this gap, in this article, we propose a secure and auditable private data sharing (SPDS) scheme under data processing-as-a-service mode in smart grid. Specifically, we first present a novel blockchain-based framework for trust-free private data computation and data usage tracking, where smart contracts are employed to specify fine-grained data usage policies (i.e., who can access what kinds of data, for what purposes, at what price) while the distributed ledgers keep an immutable and transparent record of data usage. A trusted execution environment based off-chain smart contract execution mechanism is exploited as well to process confidential user datasets and relieve the computation overhead in blockchain systems. A two-phase atomic delivery protocol is designed to ensure the atomicity of data transactions in computing result release and payment. Furthermore, based on contract theory, the optimal contracts are designed under information asymmetry to stimulate user's participation and high-quality data sharing while optimizing the payoff of the energy service provider. Extensive simulation results demonstrate that the proposed SPDS can effectively improve the payoffs of participants, compared with conventional schemes.

Specification and automated verification of atomic concurrent real-time transactions

Many database management systems (DBMS) need to ensure atomicity and isolation of transactions for logical data consistency, as well as to guarantee temporal correctness of the executed transactions. Since the mechanisms for atomicity and isolation may lead to breaching temporal correctness, trade-offs between these properties are often required during the DBMS design. To be able to address this concern, we have previously proposed the pattern-based UPPCART framework, which models the transactions and the DBMS mechanisms as timed automata, and verifies the trade-offs with provable guarantee. However, the manual construction of UPPCART models can require considerable effort and is prone to errors. In this paper, we advance the formal analysis of atomic concurrent real-time transactions with tool-automated construction of UPPCART models. The latter are generated automatically from our previously proposed UTRAN specifications, which are high-level UML-based specifications familiar to designers. To achieve this, we first propose formal definitions for the modeling patterns in UPPCART, as well as for the pattern-based construction of DBMS models, respectively. Based on this, we establish a translational semantics from UTRAN specifications to UPPCART models, to provide the former with a formal semantics relying on timed automata, and develop a tool that implements the automated transformation. We also extend the expressiveness of UTRAN and UPPCART, to incorporate transaction sequences and their timing properties. We demonstrate the specification in UTRAN, automated transformation to UPPCART, and verification of the traded-off properties, via an industrial use case.

PfTouch: Concurrent page-fault handling for Intel restricted transactional memory

Page faults occurring within transactions jeopardize concurrency in Intel Restricted Transactional Memory (RTM). To make progress in spite of page-fault-induced aborts, the program must resort to the non-speculative fallback path and re-execute the affected transaction. Since the atomicity of a non-speculative transaction is guaranteed by impeding the execution of any other speculative transactions until the former completes, taking the fallback path is particularly harmful for performance. Therefore, such page-fault-induced aborts currently lead to thread serialization during the potentially long period of time taken to resolve them. In this work we propose PfTouch, a simple extension to RTM that allows page-fault handling to be moved out of non-speculative transactional execution in mutual exclusion. Our proposal sidesteps taking the fallback path in these cases and thus avoids its associated performance loss, by triggering page faults in the abort handler while other speculative transactions can run concurrently. PfTouch requires minimal modifications in the Intel RTM specification and keeps the OS unaltered. Through full-system simulation, we show that PfTouch achieves average reductions in execution time of 7.7% (up to 24.4%) for the STAMP benchmarks, closely matching the performance of the more complex suspended transactional mode in the IBM Power ISA.