Fabric is a modular and extensible open-source system for deploying and operating permissioned blockchains and one of the Hyperledger projects hosted by the Linux Foundation (www.hyperledger.org). Fabric is the first truly extensible blockchain system for running distributed applications. It supports modular consensus protocols, which allows the system to be tailored to particular use cases and trust models. Fabric is also the first blockchain system that runs distributed applications written in standard, general-purpose programming languages, without systemic dependency on a native cryptocurrency. This stands in sharp contrast to existing blockchain platforms that require "smart-contracts" to be written in domain-specific languages or rely on a cryptocurrency. Fabric realizes the permissioned model using a portable notion of membership, which may be integrated with industry-standard identity management. To support such flexibility, Fabric introduces an entirely novel blockchain design and revamps the way blockchains cope with nondeterminism, resource exhaustion, and performance attacks. This paper describes Fabric, its architecture, the rationale behind various design decisions, its most prominent implementation aspects, as well as its distributed application programming model. We further evaluate Fabric by implementing and benchmarking a Bitcoin-inspired digital currency. We show that Fabric achieves end-to-end throughput of more than 3500 transactions per second in certain popular deployment configurations, with sub-second latency, scaling well to over 100 peers.

This paper presents adaptive algorithms for mutual exclusion using only read and write operations; the performance of the algorithms depends only on the point contention, i.e., the number of processes that are concurrently active during the algorithm execution (and not on n, the total number of processes). Our algorithm has O(k) remote step complexity and O(log k) system response time, where k is the point contention. The remote step complexity is the maximal number of steps performed by a process where a wait is counted as one step. The system response time is the time interval between subsequent entries to the critical section, where one time unit is the minimal interval in which every active process performs at least one step. The space complexity of this algorithm is O (N log n), where N is the range of processes' names. We show how to make the space complexity of our algorithm depend solely on n, while preserving the other performance measures of the algorithm.

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A distributed algorithm is adaptive if its performance depends on k, the number of processes that are concurrently active during the algorithm execution (rather than on n, the total number of processes). This paper presents adaptive algorithm for mutual exclusion using only read and write operations.The worst case step complexity cannot be a measure for the performance of mutual exclusion algorithms, because it is always unbounded in the presence of contention. Therefore, a number of different parameters are used to measure the algorithm's performance: The remote step complexity is the maximal number of steps performed by a process where a wait is counted as one step. The system response time is the time interval between subsequent entries to the critical section, where one time unit is the minimal interval in which every active process performs at least one step.The algorithm presented here has O(k) remote step complexity and O(log k) system response time, where k is the point contention. The space complexity of this algorithm is O(nN), where N is the range of processes' names.The space complexity of all previously known adaptive algorithms for various long-rived problems depends on N. We present a technique that reduces the space complexity of our algorithm to be a function of n, while preserving the other performance measures of the algorithm.

Recent evidence of successful Internet-based attacks and frauds involving financial institutions highlights the inadequacy of the existing protection mechanisms, in which each instutition implements its own isolated monitoring and reaction strategy. Analyzing on-line activity and detecting attacks on a large scale is an open issue due to the huge amounts of events that should be collected and processed. In this paper, we propose a large-scale distributed event processing system, called intelligence cloud, allowing the financial entities to participate in a widely distributed monitoring and detection effort through the exchange and processing of information locally available at each participating site. We expect this approach to be able to handle large amounts of events arriving at high rates from multiple domains of the financial scenario. We describe a framework based on the intelligence cloud where each participant can receive early alerts enabling them to deploy proactive countermeasures and mitigation strategies.