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Atomic Commit Problem (ACP) is a single-shot agreement problem similar to consensus, meant to model the properties of transaction commit protocols in fault-prone distributed systems. We argue that ACP is too restrictive to capture the complexities of modern transactional data stores, where commit protocols are integrated with concurrency control, and their executions for different transactions are interdependent. As an alternative, we introduce Transaction Certification Service (TCS), a new formal problem that captures safety guarantees of multi-shot transaction commit protocols with integrated concurrency control. TCS is parameterized by a certification function that can be instantiated to support common isolation levels, such as serializability and snapshot isolation. We then derive a provably correct crash-resilient protocol for implementing TCS through successive refinement. Our protocol achieves a better time complexity than mainstream approaches that layer two-phase commit on top of Paxos-style replication.

The current technology has given arms, hands, and wings to the smart objects-internet of things, which create the centralized data collection and analysis nightmare. Even with the distributed big data-enabled computing, the relevant data filtering for the localized decisions take a long time. To make the IOT data communication smoother and make the devices talk to each other in a coherent way the device data transactions are made to communicate through the block chain, and the applications on the localized destination can take the decisions or complete transaction without the centralized hub communication. This chapter focuses on adding vendor-specific IOT devices to the public or private block chain and the emerging challenges and the possible solutions to make the devices talk to each other and have the decision enablement through the distributed transactions through the block chain technology.

In the present digital world, the day-to-day activities of human life is managed digitally, and often the scenario of “anything, anytime, and anywhere” holds. The three-dimensional buzz words which revolve globally are Internet of Things, Industry 4.0, and Smart Cities. Smart cities are feasible today because of the advancements in technologies such as ubiquitous sensing, heterogeneous network, intelligent sensing, and processing of information and control systems. Security is a major concern when all the daily chores are managed digitally. Therefore, more research has to be focused on security aspects to ensure authentication, privacy, and trust. Blockchain is providing a significant contribution to achieving a secured platform for distributed transaction management. There is a stringent need to discuss in detail the various standards and protocols of blockchain. This chapter explains the standards and categorization of blockchain and its protocols. It includes the protocols of network and data organization, distributed consensus protocols, the framework of autonomous organization that relies on smart contracts executed in distributed virtual machines, and the human–machine interface implementations. There are three main categories of consensus protocols namely, Proof of Work, Proof of Stake, and Byzantine Fault Tolerant which are categorized based on the applications.

Data replication has been one of the pathways for distributed database management as well as computational intelligence scheme as it continues to improve data access and reliability. The performance of data replication technique can be crucial when it involves failure interruption. In order to develop a more efficient data replication technique which can cope with failure, a fault tolerance approach needs to be applied in the data replication transaction. Fault tolerance approach is a core issue for a transaction management as it preserves an operation mode transaction prone to failure. In this study, a data replication technique known as Binary Vote Assignment on Grid (BVAG) has been combined with a fault tolerance approach named as Checkpoint and Rollback-Recovery (CR) to evaluate the effectiveness of applying fault tolerance approach in a data replication transaction. Binary Vote Assignment on Grid with Checkpoint and Rollback-Recovery Transaction Manager (BVAGCRTM) is used to run the BVAGCR proposed method. The performance of the proposed BVAGCR is compared to standard BVAG in terms of total execution time for a single data replication transaction. The experimental results reveal that BVAGCR improves the BVAG total execution time in failure environment of about 31.65% by using CR fault tolerance approach. Besides improving the total execution time of BVAG, BVAGCR also reduces the time taken to execute the most critical phase in BVAGCRTM which is Update (U) phase by 98.82%. Therefore, based on the benefits gained, BVAGCR is recommended as a new and efficient technique to obtain a reliable performance of data replication with failure condition in distributed databases.

Data management and distributed transactions are characteristics of the Blockchain. Although the Blockchain has initially been a technology used for bitcoin, the Blockchain has now spread to various sectors, including the sports sector. Taekwondo Indonesia has a plan for Level Up Inspections held in different Cities / Regencies by the Indonesian Taekwondo Branch Management and overseen by the Administrators of the Taekwondo Province of Indonesia in the province. Data management, data access, and limited data transactions between Indonesian Taekwondo Branch Management in an area become an obstacle that needs to be identified. Dapps using the Vexanium public Blockchain is a solution to solve this problem, wherewith the Vexanium Rate Increase data among Indonesian Taekwondo Branch Officers in this area can be seen transparently and becomes a preventative measure in data loss or data manipulation that often occurs. This study discusses to prove the use of Dapps using Vexanium is a Level Up Testing system to be more productive and far more efficient.

This protocol allows for transaction level authentication using shared secrets and one way hashing. It can be used to authenticate dynamic updates as coming from an approved client, or to authenticate responses as coming from an approved recursive name server.

Although the Microservices Architecture comes with a number of benefits, it is a challenge to design an efficient transaction management for multi-agent transactions that span across multiple services. Since microservices design require a loose coupling between service logic and resources, data used by a distributed transaction is spread across different nodes, there is need to an effective way to ensure data portability, speed consistency for transaction in microservices. Therefore this research addresses both the issue of improving transaction performance and maintaining data portability for distributed transactions in microservices. A novel approach to transaction management termed ‘buffered serialization’ is proposed in this research as a way to improve transaction management by the use serialization libraries. This work has a three-part objective which are (1) to highlight the challenges of distributed transaction management in microservices, (2) to propose an enhanced approach to transaction management through the use of buffered serialization between services (3) to provide a proof of concept for this novel approach. Obtained result indicate a 60% improvement in transaction failure recovery time, 10% higher success rate for load tests but an insignificantly higher resource utilization values.

The uncontrolled proportion of distributed transactions restricts the scalability of distributed databases. In this paper, we presented a novel scalable distributed database system, named CBase, which has been applied in a large commercial bank for several years. In CBase, for elasticity scalability, transaction processing, and data storage are thoroughly decoupled. CBase uses incremental data partition and repartitioning scheme to efficiently reduce the proportion of distributed transactions to a low boundary and support online repartition. The experimental results highlight the scalability of CBase.

Blockchain technologies have been very effective in processing distributed transactions securely. They have many applications including in handling bitcoin cryptocurrencies and smart contracts. More recently the use of blockchain has been explored for data science applications. This paper examines blockchain technologies and discusses their applications in data science and cyber security.

Distributed storage systems run transactions across machines to ensure serializability in single-service architecture. Traditional protocols for distributed transactions are based on two-phase commit (2PC) or multi-version concurrency control (MVCC). 2PC serializes transactions as soon as they conflict and MVCC resorts to aborts, leaving many opportunities for con-currency on the distributed storage system. In the micro-service architecture, service nodes are deployed in heterogeneous distributed systems. 2PC and MVCC are struggling to break through the performance limitations of their existence. This paper presents 2PC+, a novel concurrency control protocol for distributed transactions that outperforms 2PC allowing more concurrency in micro-service architecture. 2PC+ is based on the traditional 2PC and MVCC, combined with transaction thread synchronization blocking optimization algorithm SAOLA. Then, we verify the algorithm SAOLA using the temporal logic of action TLA+ language. Finally, we compare 2PC+ to 2PC applying a case of Ctrip MSECP. When concurrent threads of the service call reach a certain threshold, 2PC+’s RT performance is improved by 2.87 times to 3.77 times compared with 2PC, and 2PC+’s TPS performance is 323.7% to 514.4% higher than 2PC.

The global ecological environment continues to deteriorate and traditional energy sources are facing exhaustion. The development and use of clean and sustainable renewable energy has become an important development trend in the energy industry. Distributed energy has the characteristics of high energy efficiency, flexible operation and good economy, and has gradually become an important source of power generation. At the same time, the characteristics of blockchain technology such as decentralization, distribution, detrust, non-tamperable data, and traceability are consistent with the needs of distributed energy transactions. Therefore, exploring distributed energy transactions based on blockchain can help to improve economic efficiency and renewable energy consumption rate and reduce credit costs. This paper introduces the overall situation of china's distributed energy development, explains the necessity of developing distributed energy market transactions, and analyzes the fit between blockchain technology and distributed transactions. Combing the principles of blockchain technology in detail, popularizing the application scenarios of blockchain technology in the energy industry. This paper proposes the application mechanism of distributed energy market-based transaction architecture based on blockchain, which has reference significance for the application of blockchain technology in distributed energy transactions in microgrid.

Aiming at the problems that game relationships between current distributed trading market entities merit further exploration and the impact of distributed transactions on distribution network operation is not yet fully understood. This paper proposes a multi-microgrid distributed Peer-to-Peer (P2P) day-ahead trade method considering congestion management. A model of the autonomous economic scheduling of the market entity was constructed. The non-cooperative game model and Stackelberg game model were then used to analyze the game relationship in the P2P transaction process under normal distribution network operation conditions. A congestion scheduling technique was then applied in the case of congested distribution network operation based on market capacity. Finally, simulation results were used to analyze the P2P transaction results in different scenarios as well as the impact of the proposed trade method on distribution network operation.

Algorand is a recent, open-source public or permissionless blockchain system that employs a novel proof-of-stake byzantine consensus protocol to efficiently scale the distributed transaction agreement problem to billions of users. In addition to being more democratic and energy-efficient, compared to popular protocols such as Bitcoin, Algorand also touts a much high transaction throughput. This paper is the first attempt in the literature to study and address this problem. By carefully modeling the participation costs and rewards received within a strategic interaction scenario, we first empirically show that even a small number of nodes defecting to participate in the protocol tasks due to insufficiency of the available incentives can result in the Algorand network failing to compute and add new blocks of transactions. We further show that this effect can be formalized by means of a mathematical model of interaction in Algorand given its participation costs and the current (or planned) reward distribution/sharing approach envisioned by the Algorand Foundation. Specifically, on analyzing this game model we observed that mutual cooperation under the currently proposed reward sharing approach is not a Nash equilibrium. This is a significant result which could threaten the success of an otherwise robust distributed consensus mechanism. We propose a novel reward sharing approach for Algorand and formally show that it is incentive-compatible, i.e., it can guarantee cooperation within a group of selfish Algorand users. Extensive numerical and Algorand simulation results further confirm our analytical findings. Moreover, these results show that for a given distribution of stakes in the network, our reward sharing approach can guarantee cooperation with a significantly smaller reward per round.

Distributed transactions on high-overhead TCP/IP-based networks were conventionally considered to be prohibitively expensive and thus were avoided at all costs. To that end, the primary goal of almost any existing partitioning scheme is to minimize the number of cross-partition transactions. However, with the new generation of fast RDMA-enabled networks, this assumption is no longer valid. In fact, recent work has shown that distributed databases can scale even when the majority of transactions are cross-partition. In this paper, we first make the case that the new bottleneck which hinders truly scalable transaction processing in modern RDMA-enabled databases is data contention, and that optimizing for data contention leads to different partitioning layouts than optimizing for the number of distributed transactions. We then present Chiller, a new approach to data partitioning and transaction execution, which aims to minimize data contention for both local and distributed transactions. Finally, we evaluate Chiller using various workloads, and show that our partitioning and execution strategy outperforms traditional partitioning techniques which try to avoid distributed transactions, by up to a factor of 2.

Linear two-phase commit (L-2PC) protocol is used in the commit processing of distributed transactions via the transaction managers of the various nodes when transaction runs serially. However, it suffers from the poor recovery process and some other associated problems also. In past, almost no effort has been done to overcome the aforementioned problems of L-2PC protocol. This paper is an attempt to propose a Serial Parallel Linear and Fast-paced recovery-centered Transaction (SP-LIFT) commit protocol by modifying the L-2PC. It gives autonomy to the participating cohorts by informing the cohort-in-doubt about the distributed transaction’s state instead of always looking for the coordinator’s stand. The SP-LIFT protocol is also different from LIFT protocol by allowing the second phase of commit processing to go in parallel while keeping the first phase of the commit processing the same as it was in the LIFT protocol. In this way, the SP-LIFT protocol allows the distributed transaction to complete its commit processing earlier as compared to the L-2PC and LIFT protocols. The performance results also confirm that the proposed protocol will be a better alternative.

Based on the “physical distribution and logical integration” architecture of new generation power grid dispatching and control system, with requirements of the crossterminal display, cross-center transparent access and multicenter business collaboration, it is necessary to develop a technical framework of data synchronization based on global analysis and decision. Through unified data synchronization, distributed transactions and data recovery and other functions to achieve multi-center data consistency, improve the centralized management of data in the control system. Finally, through the test and live demonstration, data synchronization function of multi-center was verified.

Intersections are the bottlenecks of road networks. Coordination mechanisms for intersection crossing greatly affect the efficiency of road utilization. Typically, coordination is done by implanting local infrastructure, whether signs, traffic lights, or through common, well known-rules shared by all users. In this paper, we introduce STARC, a decentralized intersection management protocol for future connected vehicles and other traffic participants. With STARC, all participants coordinate their movement using reservations to guarantee safe crossings. To enable cost-efficient deployment, STARC does neither rely on any centralized infrastructure, such as traffic lights, nor centralized wireless intersection coordinators, like virtual traffic lights. STARC targets small, cheap, and energy-efficient platforms and the open low-power wireless standard 802.15.4 so that all participants in road traffic could take advantage of it, including vehicles, bikes, electric scooters, and even pedestrians. STARC builds on low-power wireless communication with A2-Synchrotron and multi-hop routing as a communication substrate and provides distributed transaction, election, and handover mechanisms to manage the intersection cooperatively. We show that STARC reduces average waiting times by up to 50% compared to a fixed traffic light schedule in traffic volumes with less than 1000 vehicles per hour. Moreover, we illustrate a platoon extension that allows STARC to outperform traffic lights even at traffic loads beyond 1000 vehicles per hour.

Monolith applications tend to be difficult to deploy, upgrade, maintain, and understand. Microservices, on the other hand, have the advantages of being independently developed, tested, deployed, scaled and, more importantly, easier to change and maintain. This paper addresses the problem of migrating a monolith to a microservices architecture. Therefore, we address two research questions: (1) Can we define the cost of decomposition in terms of the effort to redesign a functionality, which is implemented in the monolith as an ACID transaction, into several distributed transactions? (2) Considering several similarity measures between domain entities, which provide a better decomposition when they are compared using the proposed complexity metric? To answer the first research question, we propose a complexity metric, for each functionality of the monolith application, that measures the impact of relaxing the functionality consistency on the architecture redesign and implementation. Regarding the second research question, we experiment with four similarity measures, each based on a different type of information collected from monolith functionality implementation. We evaluated our approach with three monolith systems and compared our complexity metric against industry metrics of cohesion and coupling. We also evaluated the different similarity measures in terms of the complexity of the decomposition they produce. We were able to correctly correlate the complexity metric with other metrics of cohesion and coupling defined in other research and we conclude that no single combination of similarity measures outperforms the other, which is confirmed by the existing research. Additionally, we conclude that the approach can help on an incremental migration to microservices, which, actually, is the strategy proposed by the industry experts.

Дослідження ставить на меті підвищення рівня загальної доступності даних в окремому вузлі розподіленої бази даних та ефективності використання програмних систем по роботі з даними за рахунок зменшення кількості розподілених запитів. Мета досягається шляхом оптимізації структури вузла розподіленої бази даних та мінімізації обсягів даних, що зберігаються у ньому. Для досягнення мети було створено підсистему обліку користувацьких запитів, граматику мови T-SQL та виконано парсинг коду SQL-запитів. В результаті запити класифікуються за списком таблиць бази даних, які трапляються у запиті, а також, після виконання більш детального аналізу, за списком атрибутів та кортежів відношення. Останнє досягається за рахунок виконання набору запитів зі зверненням до первинного ключа кожного відношення, що входить до складу запиту. Виконання повного аналізу оцінки корисності атрибутів та кортежів таблиць бази даних є досить ресурсоємною операцією, тому не може виконуватися при кожній зміні даних. У рамках дослідження запропоновано реалізувати вирішення задачі класифікації нових даних із використанням нейронної мережі прямого поширення, що навчається на базі оцінених попередньо даних на базі парсингу SQL-запитів. Враховуючи необхідність виконання аналізу накопичених даних з точки зору множинності вимірів, а також, ймовірно, великі їх обсяги, було виконано представлення даних, необхідних для аналізу, у вигляді багатовимірної моделі., The paper deals with the tendency to move from "universal" accounting systems to specialized solutions usage. This requires the synchronization of distributed database data. It is noted that among the strategies of data distribution between distributed database nodes, the combined one is the most justified, but the main disadvantage consists in the existence of distributed transactions when handling data. The research aims to improve the general availability of data in the separate node of the distributed database and the efficiency of using software systems to work with database data by reducing the number of distributed requests. The goal is achieved by optimizing the structure of the distributed database node and minimizing the amount of data stored in it. To achieve the goal, users' query accounting subsystem and T-SQL grammar have been created, and SQL query code has been parsed. As a result, the queries are classified by the list of database tables that are found in the query, and, after performing more deyailed analysis, by the list of attributes and relation tuples. The last one is achieved by executing a set of queries with getting the primary key of each relation included in the query. Performing the complete analysis of the database tables attributes and tuples estimation is a very resource-intensive operation, so it cannot be performed every time the database data is changed. The research proposes to solve the problem of classification of new data by using the perceptron, which learns on the basis of pre-evaluated data based on SQL query parsing. Also, according to the need of performing the analysis of received data from the point of view of multiple dimensions, as well as probably their large amount, the data required for the analysis has been presented in the form of a multidimensional model.

Universities and public research centers have been looking for ways to transfer technologies created in their research activities to companies that promote business and innovation. One of the strategies adopted is to provide technology licensing opportunities via the web. There are several studies in the literature that indicate the potential of the web for technology transfer and others that point out limitations, given the complexity of the technology market. This article investigates websites with technologies available for business at universities and public research centers in order to propose a classification of these sites based on design attributes and to identify aspects relevant to the users’ experience. Twenty public institutions of reference that occupy the first places in the international ranking of quality of presence on the web were considered for analysis. Based on this analysis, we propose a classification of websites composed of three types: proactive transactions, interactive search tools, and low interaction. The findings indicate widespread use of the web for the dissemination of technologies - all institutions have at least one website with patents or business inventions. The findings also suggest that proactive functionalities, quality of search engines, transactional resources and technology aggregators are relevant aspects for the users’ experience.

As distributed transactions become more and more mature, such as peer-to-peer (P2P), the widespread application of distributed transactions not only requires fair and open transaction process, but also has as little impact on the main network as possible. This paper considers an energy matching model, which includes two parts. The first part is a kind of new auction, in which participants can clearly aware of competitive pressures because of current or historical data in blockchain. The second part is an interaction between charging station operator and unmatched participants in the first part. Cases have been studied to demonstrate the effectiveness of this distributed transaction. Department of Education of Guangdong Province: New and Integrated Energy System TheoryResearch Group under Grant 2016KCXTD022; Brunel Research Initiative and Enterprise Fund (BRIEF); National Natural Science Foundation of China under Grant 51907031; Operation Fund of Guangdong Key Laboratory of Clean Energy Technology under Grant 2017B030314127.

Lecture Notes in Computer Science, 12229, ISSN:0302-9743, ISSN:1611-3349, NASA Formal Methods. 12th International Symposium, NFM 2020, Moffett Field, CA, USA, May 11–15, 2020, Proceedings, ISBN:978-3-030-55753-9, ISBN:978-3-030-55754-6

The Distributed Asynchronous Object Storage (DAOS) is an open source scale-out storage system designed from the ground up to support Storage Class Memory (SCM) and NVMe storage in user space. DAOS uses an optimized two-phase commit protocol to guarantee atomicity of distributed I/O. This protocol is tightly coupled with the self-healing system of DAOS, in contrast with traditional two-phase commit protocol that is blocking when coordinator fails, this protocol can proceed in presence of failure, and it also has shorter transaction response time than the traditional protocol, these characteristics are important for massively distributed and low latency storage system like DAOS. This paper introduces the distributed transaction and self-healing system of DAOS, and presents the performance benefits of the transaction protocol.

Programming loosely connected distributed applications is a challenging endeavour. Loosely connected distributed applications such as geo-distributed stores and intermittently reachable IoT devices cannot afford to coordinate among all of the replicas in order to ensure data consistency due to prohibitive latency costs and the impossibility of coordination if availability is to be ensured. Thus, the state of the replicas evolves independently, making it difficult to develop correct applications. Existing solutions to this problem limit the data types that can be used in these applications, which neither offer the ability to compose them to construct more complex data types nor offer transactions.

Along with the increasing prosperity of electric vehicles (EVs), the large-scale penetration of EVs in V2G (Vehicle to Grid) trading market brings difficulties and security issues in the operation of the energy market. The blockchain has the characteristics of security, transparency, decentralization and traceability, which provides an alternative for complex energy market operation in a secure environment. In this paper, a novel hierarchical blockchain architecture is constructed for the V2G trading system including Data Blockchain layer, Operation Blockchain layer and Transaction Blockchain layer. The intelligent contracts of the V2G trading, two-level auction and optimization strategies are designed for scheduling EVs orderly charge and discharge to participate in the grid load regulation so as to minimize the total load variance of the grid and realize reasonable benefit distribution among the involved market entities, i.e., EVs, aggregators and grid. Based on the V2G trading characteristics, a transaction manager with four optimization strategies are designed to further optimize the transaction generation process. The CA (cellular automata) based simulation experiments are performed to verify the effectiveness of the proposed V2G trading system.

As more and more software moves off the desktop and into data centers, and more and more cell phones use server requests as the other half of apps, observation tools for large-scale distributed transaction systems are not keeping up. This makes it tempting to look under the lamppost using simpler tools. You will waste a lot of high-pressure time following that path when you have a sudden complex performance crisis. Instead, know what each tool you use is blind to, know what information you need to understand a performance problem, and then look for tools that can actually observe that information directly.

Distributed transactions suffer from poor performance due to two major limiting factors. First, distributed transactions suffer from high latency because each of their accesses to remote data incurs a long network delay. Second, this high latency increases the likelihood of contention among distributed transactions, leading to high abort rates and low performance. We present Sundial , an in-memory distributed optimistic concurrency control protocol that addresses these two limitations. First, to reduce the transaction abort rate, Sundial dynamically determines the logical order among transactions at runtime, based on their data access patterns. Sundial achieves this by applying logical leases to each data element, which allows the database to dynamically calculate a transaction's logical commit timestamp. Second, to reduce the overhead of remote data accesses, Sundial allows the database to cache remote data in a server's local main memory and maintains cache coherence. With logical leases, Sundial integrates concurrency control and cache coherence into a simple unified protocol. We evaluate Sundial against state-of-the-art distributed concurrency control protocols. Sundial outperforms the next-best protocol by up to 57% under high contention. Sundial's caching scheme improves performance by up to 4.6× in workloads with high access skew.

Cloud-based data-intensive applications have to process high volumes of transactional and analytical requests on large-scale data. Businesses base their decisions on the results of analytical requests, creating a need for real-time analytical processing. We propose Janus, a hybrid scalable cloud datastore, which enables the efficient execution of diverse workloads by storing data in different representations. Janus manages big datasets in the context of datacenters, thus supporting scaling out by partitioning the data across multiple servers. This requires Janus to efficiently support distributed transactions. In order to support the different datacenter requirements, Janus also allows diverse partitioning strategies for the different representations. Janus proposes a novel data movement pipeline to continuously ensure up to date data between the different representations. Unlike existing multi-representation storage systems and Change Data Capture (CDC) pipelines, the data movement pipeline in Janus supports partitioning and handles both distributed transactions and diverse partitioning strategies. In this paper, we focus on supporting Online Transaction Processing (OLTP) and Online Analytical Processing (OLAP) workloads, and hence use row and column-oriented representations, which are the most efficient representations for these workloads. Our evaluations over Amazon AWS illustrate that Janus can provide real-time analytical results, in addition to processing high-throughput transactional workloads.

DGCC protocol has been shown to achieve good performance on multi-core in-memory system. However, distributed transactions complicate the dependency resolution, and therefore, an effective transaction partitioning strategy is essential to reduce expensive multi-node distributed transactions. During failure recovery, log must be examined from the last checkpoint onwards and the affected transactions are re-executed based on the way they are partitioned and executed. Existing approaches treat both transaction management and recovery as two separate problems, even though recovery is dependent on the sequence in which transactions are executed. In this paper, we propose to treat the transaction management and recovery problems as one. We first propose an efficient Distributed Dependency Graph based Concurrency Control (DistDGCC) protocol for handling transactions spanning multiple nodes, and propose a new novel and efficient logging protocol called Dependency Logging that also makes use of dependency graphs for efficient logging and recovery. DistDGCC optimizes the average cost for each distributed transaction by processing transactions in batch. Moreover, it also reduces the effects of thread blocking caused by distributed transactions and consequently improves the runtime performance. Further, dependency logging exploits the same data structure that is used by DistDGCC to reduce the logging overhead, as well as the logical dependency information to improve the recovery parallelism. Extensive experiments are conducted to evaluate the performance of our proposed technique against state-of-the-art techniques. Experimental results show that DistDGCC is efficient and scalable, and dependency logging supports fast recovery with marginal runtime overhead. Hence, the overall system performance is significantly improved as a result., 13 pages

With rapidly increasing interest in distributed real-time database systems (DRTDBS), data conflicts amongst distributed transactions have become a crucial factor which negatively influences the system performance. To address above issue, an I mproved Data Lending based D istributed R eal-Time C ommit (IDRC) protocol has been developed. IDRC reduce the negative impact of data conflicts on the DRTDBSperformance by increasing the data accessibility as compared to the earlier protocols. It allows the commit dependent borrower participant node (who has already lent the data item to the abort dependent borrower participant node) to lend the same data item in read mode to more than one borrower participant nodes. The simulation results show that the proposed commit protocol outperforms.

Mobile edge computing (MEC) is being introduced and leveraged in many domains, but few studies have addressed MEC for secure in-home therapy management. To this end, this paper presents an in-home therapy management framework, which leverages the IoT nodes and the blockchain-based decentralized MEC paradigm to support low-latency, secure, anonymous, and always-available spatiotemporal multimedia therapeutic data communication within an on-demand data-sharing scenario. To the best of our knowledge, this non-invasive, MEC-based IoT therapy platform is first done by our group. This platform can provide a full-body joint range of motion data for physically challenged individuals in a decentralized manner. With MEC, the framework can provide therapy diagnostic and analytical data on demand to a large portion of humanity who are either born with disabilities or became disabled due to accidents, war-time injuries, or old age. For security, the framework uses blockchain–Tor-based distributed transactions to preserve the therapeutic data privacy, ownership, generation, storage, and sharing. Our initial test results from a complete implementation of the framework show that it can support a sufficiently large number of users without considerable increase in mean processing time.

This paper addresses multi-directional energy trading problems (ETPs) for multiple microgrids (MMGs) in grid-connected and islanded modes considering the unit commitment (UC) scheme. Firstly, a mixed integer quadratic programming (MIQP) model is formulated to describe unit commitment problems (UCPs) for each microgrid (MG) and energy transactions among MMGs. Then binary variables from UCPs are relaxed to convert the original non-convex model into a tractable convex one. Furthermore, an alternating optimization method (AOM) composed of alternating direction method of multipliers (ADMM) and MIQP block is proposed to solve continuous and binary variables alternately. Especially, a rounding-off procedure with thresholds is designed to treat out-of-limit problems of generators and smooth the parameter passing between ADMM and MIQP block with corresponding convergence proof. Finally, comprehensive comparisons are conducted to demonstrate the effectiveness of proposed method. Simulation results indicate that AOM can be directly applied to solve ETPs for MMGs in a distributed manner, and thresholds are effective for operators to adjust the operating range of generators flexibly under the condition that ETPs are completely solvable.

Electric power system reform and innovative energy transaction policies have become increasingly common. In view of the fact that research on today’s multi-energy coupled market have not yet incorporated multi-energy substitution characteristics on the supply and demand sides, nor the coordination between regional and user level markets. A joint energy market was developed in this study for the procurement of local integrated energy system services considering demand flexibility. The transaction mechanisms of the regional level energy coupled market and user level Peer-to-Peer (P2P) distributed transaction market were determined accordingly. The multi-energy and complementary regional energy market was coupled with the user-level distributed transaction market that considering flexible demand response characteristics to establish a regional integrated energy system joint market. The electricity, gas, and heat markets in the regional market were modeled separately, then a joint clearing mechanism of the energy coupled market was established. An autonomous scheduling model for distributed market participants at the user level and a P2P transaction mechanism considering multi-agent non-cooperative game was proposed. The bidding behaviors of different energy operators in the regional level market were simulated to assess the influencing factors. The economic and technical benefits of P2P transactions at the user level as remitted to market participants were quantitatively determined with specific indexes. The correlations and interactions between the region level and user level markets were also analyzed.

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.

High-frequency trading (HFT) has always been welcomed because it benefits not only personal benefits but also the whole social welfare. While the recent advance of portfolio selection in HFT market enables to bring about more profit, it yields much contended OLTP workloads. Featuring exploiting the abundant parallelism, transaction pipeline, the state-of-the-art concurrency control (CC) mechanism, however, suffers from limited concurrency confronted with HFT workloads. Its variants that enable more parallel execution by leveraging fine-grained contention information also take little effect. To solve this problem, we for the first time observe and formulate the source of restricted concurrency as harmful ordering of transaction statements. To resolve harmful ordering, we propose PARE, a pipeline-aware reordered execution, to improve application performance by rearranging statements in order of their degrees of contention. In concrete, two mechanisms are devised to ensure the correctness of statement rearrangement and identify the degrees of contention of statements, respectively. We also study the off-line reordering problem. We prove that this problem is NP-hard and present an off-line reordering approach to approximate the optimal reordering strategy. Experiment results show that PARE can improve transaction throughput and reduce transaction latency on HFT applications by up to an order of magnitude than the state-of-the-art CC mechanism.

This paper tells about electronic transaction on ecommerce website using Secure Electronic Transaction (SET) protocol. SET is a very comprehensive security protocol, which utilizes cryptography to provide confidentiality of information, ensure payment integrity, and enable identity authentication. It relies on cryptography, digital certificate and authentication by SMS to ensure message confidentiality and security. First the report introduces about ecommerce websites and how to build it. It then explains how SET works and the components involved in it. Then the report gives out a design and implementation of this protocol.

Wide-column NoSQL databases are an important class of NoSQL (Not only SQL) databases which scale horizontally and feature high access performance on sparse tables. With current trends towards big Data Warehouses (DWs), it is attractive to run existing business intelligence/data warehousing applications on higher volumes of data in wide-column NoSQL databases for low latency by mapping multidimensional models to wide-column NoSQL models or using additional SQL add-ons. For examples, applications like retail management can run over integrated data sets stored in big DWs or in the cloud to capture current item-selling trends. Many of these systems also employ Snapshot Isolation (SI) as a concurrency control mechanism to achieve high throughput for read-heavy workloads. SI works well in a DW environment, as analytical queries can now work on (consistent) snapshots and are not impacted by concurrent update jobs performed by online incremental Extract-Transform-Load (ETL) flows that refresh fact/dimension tables. However, the snapshot made available in the DW is often stale, since at the moment when an analytical query is issued, the source updates (e.g. in a remote retail store) may not have been extracted and processed by the ETL process in time due to high input data volume or slow processing speed. This staleness may cause incorrect results for time-critical decision support queries. To address this problem, snapshots which are supposed to be accessed by analytical queries need to be first maintained by corresponding ETL flows to reflect source updates based on given freshness needs. Snapshot maintenance in this work means maintaining the distributed data partitions that are required by a query. Since most NoSQL databases are not ACID compliant and do not provide full-fledged distributed transaction support, snapshot may be inconsistently derived when its data partitions are updated by different ETL maintenance jobs. This paper describes an extended version of HBelt system [1] which tightly integrates the wide-column NoSQL database HBase with a clustered & pipelined ETL engine. Our objective is to efficiently refresh HBase tables with remote source updates while a consistent snapshot is guaranteed across distributed partitions for each scan request in analytical queries. A consistency model is defined and implemented to address so-called distributed snapshot maintenance . To achieve this, ETL jobs and analytical queries are scheduled in a distributed processing environment. In addition, a partitioned, incremental ETL pipeline is introduced to increase the performance of ETL (update) jobs. We validate the efficiency gain in terms of data pipelining and data partitioning using the TPC-DS benchmark, which simulates a modern decision support system for a retail product supplier. Experimental results show that high query throughput can be achieved in HBelt when distributed, refreshed snapshots are demanded.

Opportunistic networks (ONs) allow wireless devices to communicate and collaborate in the absence of dedicated networking infrastructure. Recent research work exploits mobility of nodes to create delayed paths in opportunistic networks. Execution of distributed transactions that comprise a sequence of atomic operations across multiple nodes continues to be a challenge for opportunistic networks. Implementing distributed transactions poses new challenges as participation of multiple processes, both local and remote, is required. In this paper, we propose and implement a novel transaction system called DiTON, specifically tailored to operate in opportunistic networks for delay-tolerant applications. To the best of our knowledge, our work is the first attempt to implement a transaction paradigm specifically intended for opportunistic networks. DiTON is a novel transaction scheme that allows multiple nodes to collaborate on shared sets of data while providing global coherency despite network interruptions. Through experimental results, we demonstrate that DiTON can be successfully implemented in opportunistic networks.

The specialized production of e-commerce transaction systems is an important research topic, which is of great significance for evaluating the development level and forecasting the development direction of e-commerce. However, there is a certain disparity between the current mainstream research model and reality, which leads to a deviated result. This paper puts forward a definition of “transaction efficiency” based on essential transaction services and establishes a model of the middleman's specialized production decision of transaction services. The research result shows that (1) transaction efficiency plays an important role in improving the middleman's specialized production level, (2) only when the transaction efficiency is higher than a certain threshold will the distribution middleman appear, and (3) the degree of economic specialization, price of commodities and transaction services, and other associated factors also affect the evolution of e-commerce transaction systems.

Debugging transactions and understanding their execution are of immense importance for developing OLAP applications, to trace causes of errors in production systems, and to audit the operations of a database. However, debugging transactions is hard for several reasons: 1) after the execution of a transaction, its input is no longer available for debugging, 2) internal states of a transaction are typically not accessible, and 3) the execution of a transaction may be affected by concurrently running transactions. We present a debugger for transactions that enables non-invasive, post-mortem debugging of transactions with provenance tracking and supports what-if scenarios (changes to transaction code or data). Using reenactment, a declarative replay technique we have developed, a transaction is replayed over the state of the DB seen by its original execution including all its interactions with concurrently executed transactions from the history. Importantly, our approach uses the temporal database and audit logging capabilities available in many DBMS and does not require any modifications to the underlying database system nor transactional workload., Comment: to appear as "Debugging Transactions and Tracking their Provenance with Reenactment" in PVDLB 2017, vol 10., nr. 12

In information systems, illegal information flow among objects has to be prevented. A transaction illegally reads data in an object if the object includes data in other objects which are not allowed to be read. In our previous studies, the FRWA-R (flexible read-write-abortion with role sensitivity) and FRWA-O (object sensitivity) protocols are proposed to prevent illegal information flow. Here, a transaction aborts with some probability once illegally reading data in an object. The abortion probability depends on the sensitivity of roles which the transaction holds and objects in which the transaction illegally reads data. The role sensitivity and object sensitivity show how many transactions which hold the role and illegally read data in the object abort after illegally reading data in the object, respectively. Here, the sensitivity just monotonically increases each time a transaction aborts. In this paper, we propose a new safety concept of a role and an FRWA-RS (FRWA with role safety) protocol. Here, the safety of a role increases and decreases each time a transaction holding the role commits and aborts by issuing an illegal read operation, respectively. A transaction with safer roles aborts with smaller probability. In the evaluation, we show fewer and more numbers of transactions abort in the FRWA-RS protocol than the RWA protocol and than the WA protocol, respectively, and transactions are more efficiently performed than the WA protocol.