Distributed data management systems employ data sharding techniques to achieve scalability. Traditional sharding approaches typically operate under the assumption of a trusted environment, where nodes may crash,but do not act adversarially. In untrustworthy environments, however, this assumption is no longer valid. This paper presents M arlin, an adaptive scalable data management system specifically designed for untrustworthy environments. M arlin leverages data sharding to enhance scalability while dynamically redistributing data across clusters to adapt to dynamic workloads. We propose two architectures: a centralized architecture serving as a baseline, which employs hypergraph partitioning within a trusted administrative domain, and a decentralized architecture that eliminates the need for such a trusted domain by managing shards across nodes in a decentralized manner. Both architectures utilize real-time monitoring and adaptive algorithms to dynamically adjust sharding in response to workload characteristics and adversarial conditions. Experimental results show that M arlin maintain consistent performance under diverse dynamic scenarios in untrustworthy environments by continuously optimizing shard distributions.

Ensuring order-fairness in distributed data management systems deployed in untrustworthy environments is crucial to prevent adversarial manipulation of transaction ordering, particularly in unpredictable markets where transaction order directly influences financial outcomes. While Byzantine Fault-Tolerant (BFT) consensus protocols guarantee safety and liveness, they inherently lack mechanisms to enforce order-fairness, exposing distributed systems to attacks such as frontrunning and sandwiching. Previous attempts to integrate order-fairness have often introduced substantial performance overhead, largely due to limitations of the underlying consensus protocols. This paper presents DAG of DAGs (DoD), a high-performance order-fairness protocol designed on top of DAG-based BFT consensus protocols. By leveraging the high throughput and resilience of DAG-based protocols, DoD addresses the performance limitations of existing order-fairness solutions. DoD's novel DAG of DAGs architecture enables seamless integration of order fairness with BFT consensus protocols. Through concurrent block proposals and a wave-based leader election mechanism, DoD significantly improves resilience against adversarial manipulation. A prototype implementation and experimental evaluation demonstrate that DoD effectively provides order fairness with minimal performance overhead.

Distributed data management systems use state Machine Replication (SMR) to provide fault tolerance. The SMR algorithm enables Byzantine Fault-Tolerant (BFT) protocols to guarantee safety and liveness despite the malicious failure of nodes. However, SMR does not prevent the adversarial manipulation of the order of transactions, where the order assigned by a malicious leader differs from the order in that transactions are received from clients. While order-fairness has been recently studied in a few protocols, such protocols rely on synchronized clocks, suffer from liveness issues, or incur significant performance overhead. This paper presents Rashnu , a high-performance fair ordering protocol. Rashnu is motivated by the fact that fair ordering among two transactions is needed only when both transactions access a shared resource. Based on this observation, we define the notion of data-dependent order fairness where replicas capture only the order of data-dependent transactions and the leader uses these orders to propose a dependency graph that represents fair ordering among transactions. Replicas then execute transactions using the dependency graph, resulting in the parallel execution of independent transactions. We implemented a prototype of Rashnu where our experimental evaluation reveals the low overhead of providing order-fairness in Rashnu.

Byzantine Fault Tolerant (BFT) protocols serve as a fundamental yet intricate component of distributed data management systems in untrustworthy environments. BFT protocols exhibit different design principles and performance characteristics under varying workloads and fault scenarios. The proliferation of BFT protocols and their growing complexity have made it increasingly challenging to analyze the performance and possible application scenarios of each protocol. This demonstration showcases BFTGym , an interactive platform that allows audience members to (1) evaluate, compare, and gather insights into the performance of various BFT protocols under a wide range of conditions, and (2) prototype new BFT protocols rapidly.

This paper articulates our vision for a learning-based untrustworthy distributed database. We focus on permissioned blockchain systems as an emerging instance of untrustworthy distributed databases and argue that as novel smart contracts, modern hardware, and new cloud platforms arise, future-proof permissioned blockchain systems need to be designed with full-stack adaptivity in mind. At the application level, a future-proof system must adaptively learn the best-performing transaction processing paradigm and quickly adapt to new hardware and unanticipated workload changes on the fly. Likewise, the Byzantine consensus layer must dynamically adjust itself to the workloads, faulty conditions, and network configuration while maintaining compatibility with the transaction processing paradigm. At the infrastructure level, cloud providers must enable cross-layer adaptation, which identifies performance bottlenecks and possible attacks, and determines at runtime the degree of resource disaggregation that best meets application requirements. Within this vision of the future, our paper outlines several research challenges together with some preliminary approaches.

We present Saguaro, a permissioned blockchain system designed specifically for edge computing networks. Saguaro leverages the hierarchical structure of edge computing networks to reduce the overhead of wide-area communication by presenting several techniques. First, Saguaro proposes coordinator-based and optimistic protocols to process cross-domain transactions with low latency where the lowest common ancestor of the involved domains coordinates the protocol or detects inconsistency. Second, data are collected over hierarchy enabling higher-level domains to aggregate their sub-domain data. Finally, transactions initiated by mobile edge devices are processed without relying on high-level fog and cloud servers. Our experimental results across a wide range of workloads demonstrate the scalability of Saguaro in supporting a range of cross-domain and mobile transactions.

Edge computing while bringing computation and data closer to users in order to improve response time, distributes edge servers in wide area networks resulting in increased communication latency between the servers. Synchronizing globally distributed edge servers, especially in the presence of Byzantine servers, becomes costly due to the high communication complexity of Byzantine fault-tolerant consensus protocols. In this paper, we present Ziziphus, a geo-distributed system that partitions edge servers into fault-tolerant zones where each zone processes transactions initiated by nearby clients locally. Global synchronization among zones is required only in special situations, e.g., migration of clients from one zone to another. On the one hand, the two-level architecture of Ziziphus confines the malicious behavior of nodes within zones requiring a much cheaper protocol at the top level for global synchronization. On the other hand, Ziziphus processes local transactions within zones by edge servers closer to clients resulting in enhanced performance. Ziziphus further introduces zone clusters to enhance scalability where instead of running global synchronization among all zones, only zones of a single cluster are synchronized.

This paper presents AdaChain , a learning-based blockchain framework that adaptively chooses the best permissioned blockchain architecture to optimize effective throughput for dynamic transaction workloads. AdaChain addresses the challenge in Blockchain-as-a-Service (BaaS) environments, where a large variety of possible smart contracts are deployed with different workload characteristics. AdaChain supports automatically adapting to an underlying, dynamically changing workload through the use of reinforcement learning. When a promising architecture is identified, AdaChain switches from the current architecture to the promising one at runtime in a secure and correct manner. Experimentally, we show that AdaChain can converge quickly to optimal architectures under changing workloads and significantly outperform fixed architectures in terms of the number of successfully committed transactions, all while incurring low additional overhead.

International audience

Today's large-scale data management systems need to address distributed applications' confidentiality and scalability requirements among a set of collaborative enterprises. This paper presents Qanaat , a scalable multi-enterprise permissioned blockchain system that guarantees the confidentiality of enterprises in collaboration workflows. Qanaat presents data collections that enable any subset of enterprises involved in a collaboration workflow to keep their collaboration private from other enterprises. A transaction ordering scheme is also presented to enforce only the necessary and sufficient constraints on transaction order to guarantee data consistency. Furthermore, Qanaat supports data consistency across collaboration workflows where an enterprise can participate in different collaboration workflows with different sets of enterprises. Finally, Qanaat presents a suite of consensus protocols to support intra-shard and cross-shard transactions within or across enterprises.

While permissioned blockchains enable a family of data center applications, existing systems suffer from imbalanced loads across compute and memory, exacerbating the underutilization of cloud resources. This paper presents FlexChain , a novel permissioned blockchain system that addresses this challenge by physically disaggregating CPUs, DRAM, and storage devices to process different blockchain workloads efficiently. Disaggregation allows blockchain service providers to upgrade and expand hardware resources independently to support a wide range of smart contracts with diverse CPU and memory demands. Moreover, it ensures efficient resource utilization and hence prevents resource fragmentation in a data center. We have explored the design of XOV blockchain systems in a disaggregated fashion and developed a tiered key-value store that can elastically scale its memory and storage. Our design significantly speeds up the execution stage. We have also leveraged several techniques to parallelize the validation stage in FlexChain to further improve the overall blockchain performance. Our evaluation results show that FlexChain can provide independent compute and memory scalability, while incurring at most 12.8% disaggregation overhead. FlexChain achieves almost identical throughput as the state-of-the-art distributed approaches with significantly lower memory and CPU consumption for compute-intensive and memory-intensive workloads respectively.

Scalability is one of the main roadblocks to business adoption of blockchain systems. Despite recent intensive research on using sharding techniques to enhance the scalability of blockchain systems, existing solutions do not efficiently address cross-shard transactions. In this paper, we introduce SharPer, a scalable permissioned blockchain system. In SharPer, nodes are clustered and each data shard is replicated on the nodes of a cluster. SharPer supports networks consisting of either crash-only or Byzantine nodes. In SharPer, the blockchain ledger is formed as a directed acyclic graph and each cluster maintains only a view of the ledger. SharPer incorporates decentralized flattened protocols to establish cross-shard consensus. The decentralized nature of the cross-shard consensus in SharPer enables parallel processing of transactions with nonoverlapping clusters. Furthermore, SharPer provides deterministic safety guarantees. The experimental results reveal the efficiency of SharPer in terms of performance and scalability especially in workloads with a low percentage of cross-shard transactions.

The unique features of blockchains such as immutability, transparency, provenance, and authenticity have been used by many large-scale data management systems to deploy a wide range of distributed applications including supply chain management, healthcare, and crowdworking in permissioned settings. Unlike permissionless settings, e.g., Bitcoin, where the network is public, and anyone can participate without a specific identity, a permissioned blockchain system consists of a set of known, identified nodes that might not fully trust each other. While the characteristics of permissioned blockchains are appealing to a wide range of largescale data management systems, these systems, have to satisfy four main requirements: confidentiality, verifiability, performance, and scalability. Various approaches have been developed in industry and academia to satisfy these requirements with varying assumptions and costs. The focus of this tutorial is on presenting many of these techniques while highlighting the trade-offs among them. We demonstrate the practicality of such techniques in real-life by presenting three different applications, i.e., supply chain management, large-scale databases, and multi-platform crowdworking environments, and show how those techniques can be utilized to meet the requirements of such applications.

Crowdworking platforms provide the opportunity for diverse workers to execute tasks for different requesters. The popularity of the ”gig” economy has given rise to independent platforms that provide competing and complementary services. Workers as well as requesters with specific tasks may need to work for or avail from the services of multiple platforms resulting in the rise of multi-platform crowdworking systems. Recently, there has been increasing interest by governmental, legal and social institutions to enforce regulations, such as minimal and maximal work hours, on crowdworking platforms. Platforms within multi-platform crowdworking systems, therefore, need to collaborate to enforce cross-platform regulations. While collaborating to enforce global regulations requires the transparent sharing of information about tasks and their participants, the privacy of all participants needs to be preserved. In this paper, we propose an overall vision exploring the regulation, privacy, and architecture dimensions for the future of work multi-platform crowdworking environments. We then present Separ, a multi-platform crowdworking system that enforces a large sub-space of practical global regulations on a set of distributed independent platforms in a privacy-preserving manner. Separ, enforces privacy using lightweight and anonymous tokens, while transparency is achieved using fault-tolerant blockchain ledgers shared among multiple platforms. The privacy guarantees of Separ against covert adversaries are formalized and thoroughly demonstrated, while the experiments reveal the efficiency of Separ in terms of performance and scalability.

Modern large-scale data management systems utilize consensus protocols to provide fault tolerance. Consensus protocols are extensively used in the distributed database infrastructure of large enterprises such as Google, Amazon, and Facebook as well as permissioned blockchain systems like IBM’s Hyperledger Fabric. In the last four decades, numerous consensus protocols have been proposed to cover a broad spectrum of distributed database systems. On one hand, distributed networks might be synchronous, partially synchronous, or asynchronous, and on the other hand, infrastructures might consist of crashonly nodes, Byzantine nodes or both. In addition, a consensus protocol might follow a pessimistic or optimistic strategy to process transactions. Furthermore, while traditional consensus protocols assume a priori known set of nodes, in permissionless blockchains, nodes are assumed to be unknown. Finally, consensus protocols have explored a variety of performance trade-offs between the number of phases/messages (latency), the number of required nodes, message complexity, and the activity level of participants. In this tutorial, we discuss consensus protocols that are used in modern large-scale data management systems, classify them into different categories based on their assumptions on network synchrony, failure model of nodes, etc., and elaborate on their main advantages and limitations.

Large scale data management systems utilize State Machine Replication to provide fault tolerance and to enhance performance. Fault-tolerant protocols are extensively used in the distributed database infrastructure of large enterprises such as Google, Amazon, and Facebook. However, and in spite of years of intensive research, existing fault-tolerant protocols do not adequately address hybrid cloud environments consisting of private and public clouds which are widely used by enterprises. In this paper, we consider a private cloud consisting of nonmalicious nodes (crash-only failures) and a public cloud with possible malicious failures. We introduce SeeMoRe, a hybrid State Machine Replication protocol that uses the knowledge of where crash and malicious failures may occur in a public/private cloud environment to improve overall performance. SeeMoRe has three different modes that can be used depending on the private cloud load and the communication latency between the public and private clouds. SeeMoRe can dynamically transition from one mode to another. Furthermore, an extensive evaluation reveals that SeeMoRe’s performance is close to the state of the art crash fault-tolerant protocols while tolerating malicious failures.

The uprise of Bitcoin and other peer-to-peer cryptocurrencies has opened many interesting and challenging problems in cryptography, distributed systems, and databases. The main underlying data structure is blockchain, a scalable fully replicated structure that is shared among all participants and guarantees a consistent view of all user transactions by all participants in the system. In this tutorial, we discuss the basic protocols used in blockchain, and elaborate on its main advantages and limitations. To overcome these limitations, we provide the necessary distributed systems background in managing large scale fully replicated ledgers, using Byzantine Agreement protocols to solve the consensus problem. Finally, we expound on some of the most recent proposals to design scalable and efficient blockchains in both permissionless and permissioned settings. The focus of the tutorial is on the distributed systems and database aspects of the recent innovations in blockchains.

Despite recent intensive research, existing blockchain systems do not adequately address all the characteristics of distributed applications. In particular, distributed applications collaborate with each other following service level agreements (SLAs) to provide different services. While collaboration between applications, e.g., cross-application transactions, should be visible to all applications, the internal data of each application, e.g, internal transactions, might be confidential . In this paper, we introduce CAPER , a permissioned blockchain system to support both internal and cross-application transactions of collaborating distributed applications. In CAPER, the blockchain ledger is formed as a directed acyclic graph where each application accesses and maintains only its own view of the ledger including its internal and all cross-application transactions. CAPER also introduces three consensus protocols to globally order cross-application transactions between applications with different internal consensus protocols. The experimental results reveal the efficiency of CAPER in terms of performance and scalability.