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Major evolutions have took place starting with primary structure counting on initiated request via way of means of a customer to a processing facet known as the server. Such architectures had been now no longer sufficient to manage up with the quick ever-growing range of requests and want to make use of community bandwidth. Mobile sellers tried to triumph over such drawbacks however did cope up for see you later with the developing era platforms. Service Oriented Architecture (SOA) then developed to be one of the maximum a success representations of the customer-server structure with an introduced commercial enterprise price that offers reusable and loosely coupled services. SOA did now no longer meet clients and commercial enterprise expectancies because it changed into nevertheless counting on monolithic systems. Resilience, scalability, rapid software program shipping and the usage of fewer assets are incredibly applicable features.

With the advent of the Industry 4.0 era, the development of smart cities based on the Internet of Things (IoT) has reached a new level. As a key component of the Internet of Things (IoT), the security of wireless sensor networks (WSN) has received widespread attention. Among them, Energy Internet, as an important part to support the construction of smart cities, its security and reliability research is becoming more and more important. In the Energy Internet, distributed energy transaction model is a promising approach to replace the traditional centralized transaction model and has become the leading direction of development in energy trading. As the underlying support, blockchain technology is attracting more and more attention due to its advantages, i.e., integrity and non-repudiation. However, most blockchain-based trading models face the problem of privacy disclosure. In this paper, to solve this problem, Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is introduced as the core algorithm to reconstruct the transaction model. Specifically, we build a general model for distributed transactions called PP-BCETS (Privacy-preserving Blockchain Energy Trading Scheme). It can achieve fine-grained access control through transaction arbitration in the ciphertext form. This design can maximize the protection of privacy information, and considerably improve the security and reliability of the transaction model. Additionally, a credibility-based equity proof consensus mechanism is proposed in PP-BCETS, which can greatly increase the operation efficiency. The security analysis and experimental evaluations are conducted to prove the validity and practicability of our proposed scheme.

Providing ACID transactions under conflicts across globally distributed data is the Everest of transaction processing protocols. Transaction processing in this scenario is particularly costly due to the high latency of cross-continent network links, which inflates concurrency control and data replication overheads. To mitigate the problem, we introduce Ocean Vista—a novel distributed protocol that guarantees strict serializability. We observe that concurrency control and replication address different aspects of resolving the visibility of transactions, and we address both concerns using a multi-version protocol that tracks visibility using version watermarks and arrives at correct visibility decisions using efficient gossip. Gossiping the watermarks enables asynchronous transaction processing and acknowledging transaction visibility in batches in the concurrency control and replication protocols, which improves efficiency under high cross-data center network delays. In particular, Ocean Vista can access conflicting transactions in parallel and supports efficient write-quorum/read-one access using one round trip in the common case. We demonstrate experimentally in a multi-data center cloud environment that our design outperforms a leading distributed transaction processing engine (TAPIR) more than tenfold in terms of peak throughput, albeit at the cost of additional latency for gossip and a more restricted transaction model. The latency penalty is generally bounded by one wide area network (WAN) round trip time (RTT), and in the best case (i.e., under light load) our system nearly breaks even with TAPIR by committing transactions in around one WAN RTT.

Increasing demand for electricity necessitates the use of efficient mechanisms for demand response management (DRM) in the existing smart grid (SG) system. In the Industry 4.0 era, the usage of information and communication technologies in the energy industry revolutionized the existing grid called SG, which provides a bi-directional flow of energy and data. To handle the energy demand of the consumers, DRM is crucial. It provides the active participation of consumers in the energy trading system (ETS) between consumers and service providers. The traditional energy trading system (TETS) relies on the centralized system or trusted third parties, which may act as a single point of failure. So, it is essential to equip the SG system with a secure energy trading system (SETS) to provide privacy and security to the consumer's data. In this direction, one of the emerging technology, called blockchain, can handle the issue as mentioned above, which is a chain of decentralized and distributed transaction ledger that is retained and maintained by each user. It performs peerto- peer (P2P) energy transactions among different consumers, such as individual houses, using smart contracts, and without a central control body. In a decentralized system, each consumer has its energy storage locally generated using renewable energy resources (RES). In this article, SETS, a blockchain-based decentralized ETS framework, is proposed for storing and processing the data generated from smart meters (SMs). In SETS, miner node is designated to validate the requests of energy requirements, dynamic pricing, and time of stay. Then, an energy transaction execution approach is designed for SETS. The evaluation results obtained show that SETS outperforms the TETS in terms of computation time and communication costs.

Hybrid Transactional and Analytical Processing (HTAP) databases require processing transactional and analytical queries in isolation to remove the interference between them. To achieve this, it is necessary to maintain different replicas of data specified for the two types of queries. However, it is challenging to provide a consistent view for distributed replicas within a storage system, where analytical requests can efficiently read consistent and fresh data from transactional workloads at scale and with high availability. To meet this challenge, we propose extending replicated state machine-based consensus algorithms to provide consistent replicas for HTAP workloads. Based on this novel idea, we present a Raft-based HTAP database: TiDB. In the database, we design a multi-Raft storage system which consists of a row store and a column store. The row store is built based on the Raft algorithm. It is scalable to materialize updates from transactional requests with high availability. In particular, it asynchronously replicates Raft logs to learners which transform row format to column format for tuples, forming a real-time updatable column store. This column store allows analytical queries to efficiently read fresh and consistent data with strong isolation from transactions on the row store. Based on this storage system, we build an SQL engine to process large-scale distributed transactions and expensive analytical queries. The SQL engine optimally accesses row-format and column-format replicas of data. We also include a powerful analysis engine, TiSpark, to help TiDB connect to the Hadoop ecosystem. Comprehensive experiments show that TiDB achieves isolated high performance under CH-benCHmark, a benchmark focusing on HTAP workloads.

The two-phase commit (2PC) protocol is a key technique for achieving distributed transactions in storage systems such as relational databases and distributed databases. 2PC is a strongly consistent and centralized atomic commit protocol that ensures the serialization of the transaction execution order. However, it does not scale well to large and high-throughput systems, especially for applications with many transactional conflicts, such as microservices and cloud computing. Therefore, 2PC has a performance bottleneck for distributed transaction control across multiple microservices. In this paper, we propose 2PC*, a novel concurrency control protocol for distributed transactions that outperforms 2PC, allowing greater concurrency across multiple microservices. 2PC* can greatly reduce overhead because locks are held throughout the transaction process. Moreover, we improve the fault-tolerance mechanism of 2PC* using transaction compensation. We also implement a middleware solution for transactions in microservice support using 2PC*. We compare 2PC* to 2PC by applying both to Ctrip MSECP, and 2PC* outperforms 2PC in workloads with varying degrees of contention. When the contention becomes high, the experimental results show that 2PC* achieves at most a 3.3x improvement in throughput and a 67% reduction in latency, which proves that our scheme can easily support distributed transactions with multi-microservice modules. Finally, we embed our middleware scheme in the PaaS cloud platform and demonstrate its strong applicability to cloud computing through long-term analysis of the monitoring results in the cloud platform.

As bitcoin has received considerable attentions, a large number of transactions are increasingly published through the Internet. The potential threat to online transactions is not only limited to the anonymity of identities such as a d d r e s s and p s e u d o n y m s , but also other sensitive information shown on scripts like transaction amounts. We propose a framework to hide the amounts by employing the Paillier cryptosystem for encryption and decryption. Due to its homomorphic properties, we create a d u m b a c c o u n t which can receive the same amounts of bitcoins in each transaction but cannot spend them. This ensures that the hidden amounts are always positive, and keeps the equality between the Input-sum and the Output-sum via the commitment proofs. Our framework achieves delicate anonymity and prevents active and passive attacks, which effectively improves the transaction privacy. Analyses show that our proposal is secure and practical.

In view of the problems of large-grid-level centralized transactions and dispatch centers with information asymmetry and high processing costs, a completely decentralized transaction architecture and a weak centralized scheduling strategy based on block-chain are proposed. Firstly, the concepts of transaction decentralization and scheduling decentralization are defined, and the reliability of distributed transaction communication is studied. Built a blockchain transaction risk control model based on the communication credit consensus mechanism. Secondly, under the weakly centralized scheduling architecture based on the autonomous chain of substations, security checks are performed, and temporary central nodes are set up to perform scheduling tasks. Finally, an improved evolutionary game algorithm is used to solve the above model, and the optimal solution is obtained by dynamically updating the credibility.

Developing a reasonable, efficient distributed market transaction mechanism is an important issue in distribution systems. The gaming relation between distributed transaction market entities has yet to be fully elucidated in various trading links, and the impact of distributed transactions on distribution network operations has yet to be comprehensively analyzed. This paper proposes a novel distributed Peer-to-Peer (P2P) day-ahead trading method under multi-microgrid congestion management in active distribution networks. First, a flexible load model for price-based demand response load and an autonomous microgrid economic scheduling model are constructed. Second, under normal operation of the distribution network, a non-cooperative game model and Stackelberg game model are employed to separately and comprehensively analyze gaming relationship among sellers, and between sellers and buyers. Thereafter, a congestion management method based on market capacity is established from the perspective of distribution network control centers. Finally, the impact of end energy consumption characteristics on microgrid economic scheduling and P2P trading is analyzed through a modified IEEE 33-node power distribution system. The economic and technical benefits such as congestion mitigation and network loss reduction that produced by P2P trading to the operation of microgrid systems are analysed with specific indicators.

Designers of distributed database systems face the choice between stronger consistency guarantees and better performance. A number of applications only require read atomicity (RA) (either all or none of a transaction’s updates are visible to other transactions) and prevention of lost updates (PLU). Existing distributed transaction systems that meet these requirements also provide additional stronger consistency guarantees (such as causal consistency ), but this comes at the price of lower performance. In this paper we propose a new distributed transaction protocol, ROLA, that targets application scenarios where only RA and PLU are needed. We formally specify ROLA in Maude. We then perform model checking to analyze both the correctness and the performance of ROLA. For correctness, we use standard model checking to analyze ROLA’s satisfaction of RA and PLU. To analyze performance we: (a) perform statistical model checking to analyze key performance properties; and (b) compare these performance results with those obtained by also modeling and analyzing in Maude the well-known protocols Walter and Jessy that also guarantee RA and PLU. Our statistical model checking results show that ROLA outperforms both Walter and Jessy.

Efficient building energy management is essential for energy saving and green society. This paper investigates sustainable energy management for an energy building cluster with distributed transaction. The building cluster consists of several types of energy buildings, e.g., office, industrial, and commercial buildings. We first formulate utility functions for the buildings of consuming energy based on the characteristics of their controllable loads. Then a two-stage energy sharing strategy is presented. In the first stage, the total social energy cost is minimized through finding the optimal energy sharing profiles in a distributed way. In the second stage, the clearing for mutual energy sharing is modeled as a non-cooperative game, and the existence of the equilibrium of the game is illustrated and a relaxation-based algorithm is introduced to search for the equilibrium. Moreover, a real-time model for each building to overcome real-time uncertainties, such as renewable energy generation and base loads is provided. The simulation results show that the proposed energy sharing strategy is economically beneficial for the energy buildings, computationally efficient, and is promising to facilitate a sustainable regional building cluster.

High-performance distributed transactions require efficient remote operations on database memory and protocol metadata. The high communication cost of this workload calls for hardware acceleration. Recent research has applied RDMA to this end, leveraging the network controller to manipulate host memory without consuming CPU cycles on the target server. However, the basic read/write RDMA primitives demand trade-offs in data structure and protocol design, limiting their benefits. SmartNICs are a flexible alternative for fast distributed transactions, adding programmable compute cores and on-board memory to the network interface. Applying measured performance characteristics, we design Xenic, a SmartNIC-optimized transaction processing system. Xenic applies an asynchronous, aggregated execution model to maximize network and core efficiency. Xenic's co-designed data store achieves low-overhead remote object accesses. Additionally, Xenic uses flexible, point-to-point communication patterns between SmartNICs to minimize transaction commit latency. We compare Xenic against prior RDMA- and RPC-based transaction systems with the TPC-C, Retwis, and Smallbank benchmarks. Our results for the three benchmarks show 2.42x, 2.07x, and 2.21x throughput improvement, 59%, 42%, and 22% latency reduction, while saving 2.3, 8.1, and 10.1 threads per server.

Remote Direct Memory Access (RDMA) has been used to accelerate a variety of distributed systems, by providing low-latency, CPU-bypassing access to a remote host's memory. However, most of the distributed protocols used in these systems cannot easily be expressed in terms of the simple memory READs and WRITEs provided by RDMA. As a result, designers face a choice between introducing additional protocol complexity (e.g., additional round trips) or forgoing the benefits of RDMA entirely. This paper argues that an extension to the RDMA interface can resolve this dilemma. We introduce the PRISM interface, which adds four new primitives: indirection, allocation, enhanced compare-and-swap, and operation chaining. These increase the expressivity of the RDMA interface, while still being implementable using the same underlying hardware features. We show their utility by designing three new applications using PRISM primitives, that require little to no server-side CPU involvement: (1) PRISM-KV, a key-value store; (2) PRISM-RS, a replicated block store; and (3) PRISM-TX, a distributed transaction protocol. Using a software-based implementation of the PRISM primitives, we show that these systems outperform prior RDMA-based equivalents.

A key challenge of designing distributed software systems is maintaining data consistency. We can define data consistency and data isolation guarantees --e.g. serializability-- in terms of schedules of atomic reads and writes, but this excludes schedules that would be semantically consistent. Others use manually provided information on "non-conflicting operations" to define guarantees that work for more applications allowing more parallel schedules. To be safe, an engineer might avoid marking operations as non-conflicting, with detrimental effects to efficiency. To be fast, they might mark more non-conflicting operations than is strictly safe. Our goal is to help engineers by automatically deriving commutative operations (using their respective contracts) such that more parallel schedules with global consistency are possible. We define a new general consistency and isolation guarantee named "Return-Value Serializability" to check consistency claims automatically, and we present distributed event processing algorithms that make use of the same "Contract-based Commutativity" information. We validated both the definitions and the algorithms using model-checking with TLA+. Previous work provided evidence that local coordination avoidance such as applied here has a significant positive effect on the performance of distributed transaction systems. Client-centric return-value commutativity promises to hit a sweet spot in design trade-offs for business applications, such as payment systems, that must scale-out while their operations are not embarrassingly parallel and consistency guarantees are of the highest priority. It can also provide design feedback, indicating that some operations will simply not scale together even before a line of code has been written.

Multi-phase atomic commitment protocols require long-lived resource locks on the participants and introduce blocking behaviour at the coordinator. They are also pessimistic in nature, preventing reads from executing concurrently with writes. Despite their known shortfalls, multi-phase protocols are the mainstay of transactional integration between autonomous, federated systems. This paper presents a novel atomic commitment protocol, STRIDE (Serializable Transactions in Decentralised Environments), that offers strict serializable certification of distributed transactions across autonomous, replicated sites. The protocol follows the principles of optimistic concurrency control, operating on the premise that conflicting transactions are infrequent. When they do occur, conflicting transactions are identified through antidependency testing on the certifier, which may be replicated for performance and availability. The majority of transactions can be certified entirely in memory. Unlike its multi-phase counterparts, STRIDE is nonblocking, decentralised and does not mandate the use of long-lived resource locks on the participants. It also offers a flexible isolation model for read-only transactions, which can be served directly from the participant sites without undergoing certification. Also, update transactions are Φ-serializable, making the certifier immune to the recently disclosed logical timestamp skew anomaly.

The Bitcoin cryptocurrency is a worldwide prevalent virtualized digital currency conceptualized in 2008 as a distributed transactions system. Bitcoin transactions make use of peer-to-peer network nodes without a third-party intermediary, and the transactions can be verified by the node. Although Bitcoin networks have exhibited high efficiency in the financial transaction systems, their payment transactions are vulnerable to several ransomware attacks. For that reason, investigators have been working on developing ransomware payment identification techniques for bitcoin transactions’ networks to prevent such harmful cyberattacks. In this paper, we propose a high performance Bitcoin transaction predictive system that investigates the Bitcoin payment transactions to learn data patterns that can recognize and classify ransomware payments for heterogeneous bitcoin networks. Specifically, our system makes use of two supervised machine learning methods to learn the distinguishing patterns in Bitcoin payment transactions, namely, shallow neural networks (SNN) and optimizable decision trees (ODT). To validate the effectiveness of our solution approach, we evaluate our machine learning based predictive models on a recent Bitcoin transactions dataset in terms of classification accuracy as a key performance indicator and other key evaluation metrics such as the confusion matrix, positive predictive value, true positive rate, and the corresponding prediction errors. As a result, our superlative experimental result was registered to the model-based decision trees scoring 99.9% and 99.4% classification detection (two-class classifier) and accuracy (multiclass classifier), respectively. Hence, the obtained model accuracy results are superior as they surpassed many state-of-the-art models developed to identify ransomware payments in bitcoin transactions.

Serverless computing is currently the fastest-growing cloud services segment. The most prominent serverless offering is Function-as-a-Service (FaaS), where users write functions and the cloud automates deployment, maintenance, and scalability. Although FaaS is a good fit for executing stateless functions, it does not adequately support stateful constructs like microservices and scalable, low-latency cloud applications, mainly because it lacks proper state management support and the ability to perform function-to-function calls. Most importantly, executing transactions across stateful functions remains an open problem. In this paper, we introduce a programming model and implementation for transaction orchestration of stateful serverless functions. Our programming model supports serializable distributed transactions with two-phase commit, as well as relaxed transactional guarantees with Sagas. We design and implement our programming model on Apache Flink StateFun. We choose to build our solution on top of StateFun in order to leverage Flink's exactly-once processing and state management guarantees. We base our evaluation on the YCSB benchmark, which we extended with transactional operations and adapted for the SFaaS programming model. Our experiments show that our transactional orchestration adds 10% overhead to the original system and that Sagas can achieve up to 34% more transactions per second than two-phase commit transactions at a sub-200ms latency.

In this paper, we describe our journey of transforming SAP IQ into a relational database management system (RDBMS) that utilizes cheap, elastically scalable object stores on the cloud. SAP IQ is a three-decade old, disk-based, columnar RDBMS that is optimized for complex online analytical processing (OLAP) workloads. Traditionally, SAP IQ has been designed to operate on shared storage devices with strong consistency guarantees (e.g., high-caliber storage area network devices). Therefore, deploying SAP IQ on the cloud, as is, would have meant utilizing storage solutions such as NetApp or AWS EFS that provide a POSIX compliant file interface and strong consistency guarantees, but at a much higher monetary cost. These costs can accumulate easily to diminish the economies of scale that one would expect on the cloud, which can be undesirable. Instead, we have enhanced the design of SAP IQ to operate on cloud object stores such as AWS S3 and Azure Blob Storage. Object stores rely on a weaker consistency model, and potentially have higher latency; however, because of these design trade-offs, they are able to offer (i) better pricing, (ii) enhanced durability, (iii) improved elasticity, and (iv) higher throughput. By enhancing SAP IQ to operate under these design trade-offs, we have unlocked many of the opportunities offered by object stores. More specifically, we have extended SAP IQ's buffer manager and transaction manager, and have introduced a new caching layer that utilizes instance storage on AWS EC2. Experiments using the TPC-H benchmark demonstrate that we can gain an order of magnitude reduction in data-at rest storage costs while improving query and load performance.

The scalability of existing shared-nothing databases quickly degrades in the presence of distributed transactions with the networking being the main reason. When distributed databases were designed, networks had low bandwidth, high latency, and high CPU overhead per message because of the traditional network stack. As a result, distributed database were designed to avoid distributed transactions as much as possible. Yet, with the advent of next generation of high-speed RDMA-enabled networks, it is time to revisit this design mantra of distributed DBMSs. In my thesis, the main focus was thus on the redesign of distributed transaction processing systems by efficiently leveraging modern RDMA-enabled networks. With Remote Direct Memory Access (RDMA), it is possible to bypass the CPU when transferring data from one machine to another. Moreover, the current generation of these networks is already able to provide a bandwidth similar to that of the main memory. However, our first finding is that simply upgrading the network does not automatically yield scalability without redesigning the underlying distributed database. In the quest for building fast, scalable and highly available OLTP databases, my thesis revisited the design of distributed DBMSs for RDMA and made various core contributions to the DBMS community: 1- Do we need a new DBMS architecture? We propose a new abstraction called Network-Attached-Memory (NAM) which fully exploits RDMA for distributed DBMSs. The main idea is to decouple compute and storage to enable independent scalability, and allow compute nodes to access data on the storage nodes using RDMA operations. Using this architecture, physical co-location of compute and storage to improve performance becomes a second class design consideration, as opposed to being a necessity for scalability. 2- Do we need new transaction protocols? Using the NAM architecture, it is possible to design scalable OLTP systems which efficiently leverage low overhead RDMA. However, it requires revisiting existing data structures and transaction protocols as they were designed in the era of slow networks with high per message cost. To this end, we presented the design of our novel scalable OLTP engine called NAM-DB. It implements the very common Snapshot Isolation scheme but tailored for RDMA. For example, NAM-DB builds on a scalable timestamp generation algorithm which efficiently utilizes one-sided RDMA operations to decentralize this task. The experiments on NAM-DB show that distributed transactions can indeed scale, without an inherent bottleneck other than those imposed by the workload itself (as I will discuss next). 3- Do we need new partitioning strategies? The primary goal of existing partitioning techniques in distributed DBMSs is to minimize the number cross-partition transactions, simply because network used to be the dominant bottleneck. In modern networks, however, we found that the new bottleneck which hinders scalability is data contention, while minimizing network communication plays only a subordinate role. To this end, we developed a new solution called Chiller that extends NAM-DB in two directions: (1) a novel commit protocol based on re-ordering transaction operations with the goal of minimizing the lock duration for contended records, and (2) contention-aware partitioning so that the most critical records can be updated without additional coordination. 4- Do we need new high-availability protocols? Finally, we revisited high availability for NAM-DB. Same as for partitioning, the main goal of existing high availability approaches is to minimize the network overhead which is no longer a bottleneck with fast RDMA-enables networks. This calls also for new protocols to fully unleash the potential of RDMA networks for high availability. Hence, as a last contribution we present a novel strongly consistent replication scheme called Active-Memory. Our proposed primary-backup replication algorithm allows an RDMA-based OLTP system to maintain its high performance in the presence of failures through an efficient RDMA-based undo-logging scheme, achieving much better performance compared to the existing techniques.

International audience; The widespread decentralized applications and Blockchain components significantly boost the security frameworks in many vertical applications and use-cases including different secured payment methods and smart contracts. The integral part of any smart contract is the validation of the stake-holder identity, in general, while ideally being achieved without the third-party involvement. Recent industrial research works introduce the sovereign-identity system, where Blockchain becomes a decentralized component to establish a self-certified identity and to avoid a centralized trust third party. Hence, the classification of distributed transactions with respect to identity validation across several users becomes more challenging, especially because of the massive and sensitive identities that are issued through many users and IoT devices and that are used to validate transactions. In this context, it is important to identify and classify the malicious and non-malicious types of transactions. Our proposed method achieves the target of identity classifications from variety of transaction data. Since different users may have different device usage patterns, the data samples and labels located on any individual device may follow a different distribution, which cannot represent the global data distribution. Therefore, the solution could be bi-focal to compensate the gap. This paper coins the approach of hybridizing the consensus where as to initiate a machine learning mechanism to collect the local data globally through a permission driven and a federated approach. We introduce here a Federated Reinforcement learning to be improvised for distributed independent data as a policy of consortium while binding the proof of consensus more centrally authenticated.

Background The Health Avatar Platform provides a mobile health environment with interconnected patient Avatars, physician apps, and intelligent agents (termed IoA3) for data privacy and participatory medicine; however, its fully decentralized architecture has come at the expense of decentralized data management and data provenance. Objective The introduction of blockchain and smart contract technologies to the legacy Health Avatar Platform with a clinical metadata registry remarkably strengthens decentralized health data integrity and immutable transaction traceability at the corresponding data-element level in a privacy-preserving fashion. A crypto-economy ecosystem was built to facilitate secure and traceable exchanges of sensitive health data. Methods The Health Avatar Platform decentralizes patient data in appropriate locations (ie, on patients’ smartphones and on physicians’ smart devices). We implemented an Ethereum-based hash chain for all transactions and smart contract–based processes to guarantee decentralized data integrity and to generate block data containing transaction metadata on-chain. Parameters of all types of data communications were enumerated and incorporated into 3 smart contracts, in this case, a health data transaction manager, a transaction status manager, and an application programming interface transaction manager. The actual decentralized health data are managed in an off-chain manner on appropriate smart devices and authenticated by hashed metadata on-chain. Results Metadata of each data transaction are captured in a Health Avatar Platform blockchain node by the smart contracts. We provide workflow diagrams each of the 3 use cases of data push (from a physician app or an intelligent agents to a patient Avatar), data pull (request to a patient Avatar by other entities), and data backup transactions. Each transaction can be finely managed at the corresponding data-element level rather than at the resource or document levels. Hash-chained metadata support data element–level verification of data integrity in subsequent transactions. Smart contracts can incentivize transactions for data sharing and intelligent digital health care services. Conclusions Health Avatar Platform and interconnected patient Avatars, physician apps, and intelligent agents provide a decentralized blockchain ecosystem for health data that enables trusted and finely tuned data sharing and facilitates health value-creating transactions with smart contracts.

READ transactions that read data distributed across servers dominate the workloads of real-world distributed storage systems. The SNOW Theorem stated that ideal READ transactions that have optimal latency and the strongest guarantees, i.e. "SNOW" READ transactions, are impossible in one specific setting that requires three or more clients: at least two readers and one writer. However, it left many open questions. We close all of these open questions with new impossibility results and new algorithms. First, we prove rigorously the result from the The SNOW Theorem paper saying that it is impossible to have a READ transactions system that satisfies SNOW properties with three or more clients. The insight we gained from this proof led to teasing out the implicit assumptions that are required to state the results and also, resolving the open question regarding the possibility of SNOW with two clients. We show that it is possible to design an algorithm, where SNOW is possible in a multi-writer, single-reader (MWSR) setting when a client can send messages to other clients; on the other hand, we prove it is impossible to implement SNOW in a multi-writer, single-reader (MWSR) setting, which is more general than the two-client setting, when client-to-client communication is disallowed. We also correct the previous claim in The SNOW Theorem paper that incorrectly identified one existing system, Eiger, as supporting the strongest guarantees (SW) and whose read-only transactions had bounded latency. Thus, there were no previous algorithms that provided the strongest guarantees and had bounded latency. Finally, we introduce the first two algorithms to provide the strongest guarantees with bounded latency.

Providing ACID transactions under conflicts across globally distributed data is the Everest of transaction processing protocols. Transaction processing in this scenario is particularly costly due to the high latency of cross-continent network links, which inflates concurrency control and data replication overheads. To mitigate the problem, we introduce Ocean Vista - a novel distributed protocol that guarantees strict serializability . We observe that concurrency control and replication address different aspects of resolving the visibility of transactions, and we address both concerns using a multi-version protocol that tracks visibility using version watermarks and arrives at correct visibility decisions using efficient gossip. Gossiping the watermarks enables asynchronous transaction processing and acknowledging transaction visibility in batches in the concurrency control and replication protocols, which improves efficiency under high cross-datacenter network delays. In particular, Ocean Vista can process conflicting transactions in parallel, and supports efficient write-quorum / read-one access using one round trip in the common case. We demonstrate experimentally in a multi-data-center cloud environment that our design outperforms a leading distributed transaction processing engine (TAPIR) more than 10-fold in terms of peak throughput, albeit at the cost of additional latency for gossip. The latency penalty is generally bounded by one wide area network (WAN) round trip time (RTT), and in the best case (i.e., under light load) our system nearly breaks even with TAPIR by committing transactions in around one WAN RTT.

Efficient transaction processing over large databases is a key requirement for many mission-critical applications. Although modern databases have achieved good performance through horizontal partitioning, their performance deteriorates when cross-partition distributed transactions have to be executed. This article presents SolarDB, a distributed relational database system that has been successfully tested at a large commercial bank. The key features of SolarDB include (1) a shared-everything architecture based on a two-layer log-structured merge-tree; (2) a new concurrency control algorithm that works with the log-structured storage, which ensures efficient and non-blocking transaction processing even when the storage layer is compacting data among nodes in the background; and (3) find-grained data access to effectively minimize and balance network communication within the cluster. According to our empirical evaluations on TPC-C, Smallbank, and a real-world workload, SolarDB outperforms the existing shared-nothing systems by up to 50x when there are close to or more than 5% distributed transactions.

Support for distributed transactions across heterogeneous storage technologies is either nonexistent or suffers from poor operational and performance characteristics. In contrast, OLEP is increasin...

With the rapid development of distributed power supply, the pilot project of distributed power generation transaction has been carried out in China. The development of distributed transactions can help stimulate the potential of user demand response and promote the nearby consumption of renewable energy. Under this background, this paper proposes a distributed generation market trading mechanism, designs the trading rules of distributed trading, on this basis, puts forward a distributed trading cooperative game strategy considering the calculation of network fees, and uses the kernel method to distribute the game profits so as to determine the transaction price. Finally, the feasibility and effectiveness of the proposed distributed transaction mechanism are analyzed through an example, which provides an idea for the future development of distributed transaction.

A distributed database utilizing the wide-spread edge computing servers to provide low-latency data access with the serializability guarantee is highly desirable for emerging edge computing applications. In an edge database, nodes are divided into regions, and a transaction can be categorized as intra-region (IRT) or cross-region (CRT) based on whether it accesses data in different regions. In addition to serializability, we insist that a practical edge database should provide low tail latency for both IRTs and CRTs, and such low latency must be scalable to a large number of regions. Unfortunately, none of existing geo-replicated serializable databases or edge databases can meet such requirements. In this paper, we present Dast (Decentralized Anticipate and STretch), the first edge database that can meet the stringent performance requirements with serializability. Our key idea is to order transactions by anticipating when they are ready to execute: Dast binds an IRT to the latest timestamp and binds a CRT to a future timestamp to avoid the coordination of CRTs blocking IRTs. Dast also carries a new stretchable clock abstraction to tolerate inaccurate anticipations and to handle cross-region data reads. Our evaluation shows that, compared to three relevant serializable databases, Dast's 99-percentile latency was 87.9%~93.2% lower for IRTs and 27.7%~70.4% lower for CRTs; Dast's low latency is scalable to a large number of regions.

State-of-the-art distributed in-memory datastores (FaRM, FaSST, DrTM) provide strongly-consistent distributed transactions with high performance and availability. Transactions in those systems are fully general; they can atomically manipulate any set of objects in the store, regardless of their location. To achieve this, these systems use complex distributed transactional protocols. Meanwhile, many workloads have a high degree of locality. For such workloads, distributed transactions are an overkill as most operations only access objects located on the same server -- if sharded appropriately. In this paper, we show that for these workloads, a single-node transactional protocol combined with dynamic object re-sharding and asynchronously pipelined replication can provide the same level of generality with better performance, simpler protocols, and lower developer effort. We present Zeus, an in-memory distributed datastore that provides general transactions by acquiring all objects involved in the transaction to the same server and executing a single-node transaction on them. Zeus is fault-tolerant and strongly-consistent. At the heart of Zeus is a reliable dynamic object sharding protocol that can move 250K objects per second per server, allowing Zeus to process millions of transactions per second and outperform more traditional distributed transactions on a wide range of workloads that exhibit locality., Comment: Sixteenth European Conference on Computer Systems

With the development of distributed trading mechanism, using Peer-to-Peer (P2P) to realize fair and developed transactions becomes a reality. P2P method is used to schedule the charging behavior of electric vehicles, which can not only achieve the function of peak clipping and valley filling, but also alleviate the influence of three-phase imbalance. This paper proposes an energy transaction model which consists of two parts. The first part is a new auction in which participants can look at historical data to see how competitive they are. The second part is the interaction between the charging station operator and the participants who are not matched in the first part. Case study shows the effectiveness of this distributed trading mechanism.

The participation of demand side resources in the distributed trading market has become an important means for countries in the world to alleviate the pressure of supply and demand during peak hours. How to make full use of the time-varying price signals in the spot market and effectively tap the demand side response potential of customers has become the key issue of the key marketing strategy research for power sales companies. Firstly, this paper constructs distributed transaction models to clarify the transaction characteristics of both parties; Then, the cost model of adjustable load participating in distributed trading market is constructed with the objective of minimizing the cost of power consumption and the cost of satisfaction reduction caused by participating in demand response regulation, and the corresponding constraints are set; Finally, based on blockchain technology and smart contract system, the optimal strategy of adjustable load participating in distributed trading market is solved. Case simulation results show that the proposed strategy can effectively achieve the goal of load reduction on the grid side, and stimulate the willingness of users to participate in demand response, which has a certain practical significance.

Distributed energy, mainly composed of new energy, plays an important role in promoting the development of new energy. At present, the development of distributed energy is greatly hindered by imperfect trading platform and unstable output of new energy. Blockchain is decentralized, autonomous and requires collaborative management. Its own technical characteristics have the inherent advantages of reconstructing the energy system. The alliance chain in the blockchain is more suitable for building a distributed energy trading platform. The paper constructs a distributed energy transaction model based on alliance blockchain, studies the integration mode of blockchain and distributed energy transaction, and explores the application of blockchain in distributed transaction. The paper provides a new idea for optimizing and reconstructing the traditional distributed energy trading platform, and providing decision support for promoting distributed energy trading.