Permissioned blockchains are becoming popular as data management systems in the enterprise setting. Compared to traditional distributed databases, blockchain platforms provide increased security guarantees but significantly lower performance. Further, these platforms are quite expensive to run for the low throughput they provide. The following are two ways to improve performance and reduce cost: (1) make the system utilize allocated resources efficiently; (2) allow rapid and dynamic scaling of allocated resources based on load. We explore both of these in this work. We first investigate the reasons for the poor performance and scalability of the dominant permissioned blockchain flavor called Execute-Order-Validate (EOV). We do this by studying the scaling characteristics of Hyperledger Fabric, a popular EOV platform, using vertical scaling and horizontal scaling. We find that the transaction throughput scales very poorly with these techniques. At least in the permissioned setting, the real bottleneck is transaction processing, not the consensus protocol. With vertical scaling, the allocated vCPUs go under-utilized. In contrast, with horizontal scaling, the allocated resources get wasted due to redundant work across nodes within an organization. To mitigate the above concerns, we first improve resource efficiency by (a) improving CPU utilization with a pipelined execution of validation & commit phases; (b) avoiding redundant work across nodes by introducing a new type of peer node called sparse peer that selectively commits transactions. We further propose a technique that enables the rapid scaling of resources. Our implementation - SmartFabric, built on top of Hyperledger Fabric demonstrates 3x higher throughput, 12-26x faster scale-up time, and provides Fabric's throughput at 50% to 87% lower cost.
Kaihua Qin, Liyi Zhou, Benjamin Livshits, Arthur Gervais
Credit allows a lender to loan out surplus capital to a borrower. In the traditional economy, credit bears the risk that the borrower may default on its debt, the lender hence requires upfront collateral from the borrower, plus interest fee payments. Due to the atomicity of blockchain transactions, lenders can offer flash loans, i.e., loans that are only valid within one transaction and must be repaid by the end of that transaction. This concept has lead to a number of interesting attack possibilities, some of which were exploited in February 2020. This paper is the first to explore the implication of transaction atomicity and flash loans for the nascent decentralized finance (DeFi) ecosystem. We show quantitatively how transaction atomicity increases the arbitrage revenue. We moreover analyze two existing attacks with ROIs beyond 500k%. We formulate finding the attack parameters as an optimization problem over the state of the underlying Ethereum blockchain and the state of the DeFi ecosystem. We show how malicious adversaries can efficiently maximize an attack profit and hence damage the DeFi ecosystem further. Specifically, we present how two previously executed attacks can be "boosted" to result in a profit of 829.5k USD and 1.1M USD, respectively, which is a boost of 2.37x and 1.73x, respectively.
Graph algorithms enormously contribute to the domains such as blockchains, social networks, biological networks, telecommunication networks, and several others. The ever-increasing demand of data-volume, as well as speed of such applications, have essentially transported these applications from their comfort zone: static setting, to a challenging territory of dynamic updates. At the same time, mainstreaming of multi-core processors have entailed that the dynamic applications should be able to exploit concurrency as soon as parallelization gets inhibited. Thus, the design and implementation of efficient concurrent dynamic graph algorithms have become significant. This paper reports a novel library of concurrent shared-memory algorithms for breadth-first search (BFS), single-source shortest-path (SSSP), and betweenness centrality (BC) in a dynamic graph. The presented algorithms are provably non-blocking and linearizable. We extensively evaluate C++ implementations of the algorithms through several micro-benchmarks. The experimental results demonstrate the scalability with the number of threads. Our experiments also highlight the limitations of static graph analytics methods in a dynamic setting.
This work focuses on the electric power market, comparing the status quo with the recent trend towards the increase in distributed self-generation capabilities by prosumers. Starting from the existing tension between the intrinsically hierarchical current structure of the electricity distribution network and the substantially distributed and self-organising nature of the self-generation, we explore the limitations imposed by the current conditions. Initially, we introduce a potential multi-layered architecture for a Peer-to-Peer (P2P) energy market, discussing the fundamental aspects of local production and local consumption as part of a microgrid. Secondly, we analyse the consequent changes for the different users' roles, also in connection with some incentive models connected with the decentralisation of the power production. To give a full picture to the reader, we also scrutinise relevant elements of energy trading, such as Smart Contract and grid stability. Thirdly, we present an example of a typical P2P settlement, showcasing the role of all the previously analysed aspects. To conclude, we performed a review of relevant activities in this domain, to showcase where existing projects are going and what are the most important themes covered. Being this a work in progress, many open questions are still on the table and will be addressed in the next stages of the research. Eventually, by providing a reference model as base for further discussions and improvements, we would like to engage ourselves in a dialog with the different users and the broad community, oriented towards a more fair and ecological-friendly solution for the electricity market of the future.
Fog computing is an emerging computing paradigm that has come into consideration for the deployment of IoT applications amongst researchers and technology industries over the last few years. Fog is highly distributed and consists of a wide number of autonomous end devices, which contribute to the processing. However, the variety of devices offered across different users are not audited. Hence, the security of Fog devices is a major concern in the Fog computing environment. Furthermore, mitigating and preventing those security measures is a research issue. Therefore, to provide the necessary security for Fog devices, we need to understand what the security concerns are with regards to Fog. All aspects of Fog security, which have not been covered by other literature works needs to be identified and need to be aggregate all issues in Fog security. It needs to be noted that computation devices consist of many ordinary users, and are not managed by any central entity or managing body. Therefore, trust and privacy is also a key challenge to gain market adoption for Fog. To provide the required trust and privacy, we need to also focus on authentication, threats and access control mechanisms as well as techniques in Fog computing. In this paper, we perform a survey and propose a taxonomy, which presents an overview of existing security concerns in the context of the Fog computing paradigm. We discuss the Blockchain-based solutions towards a secure Fog computing environment and presented various research challenges and directions for future research.
Blockchain-based cryptocurrencies received a lot of attention recently for their applications in many domains. IoT domain is one of such applications, which can utilize cryptocur-rencies for micro payments without compromising their payment privacy. However, long confirmation times of transactions and relatively high fees hinder the adoption of cryptoccurency based micro-payments. The payment channel networks is one of the proposed solutions to address these issue where nodes establish payment channels among themselves without writing on blockchain. IoT devices can benefit from such payment networks as long as they are capable of sustaining their overhead. Payment channel networks pose unique characteristics as far as the routing problem is concerned. Specifically, they should stay balanced to have a sustainable network for maintaining payments for longer times, which is crucial for IoT devices once they are deployed.In this paper, we present a payment channel network design that aims to keep the channels balanced by using a common weight policy across the network. We additionally propose using multi-point connections to nodes for each IoT device for unbalanced payment scenarios. The experiment results show that we can keep the channels in the network more equally balanced compared to the minimal fee approach. In addition, multiple connections from IoT devices to nodes increase the success ratio significantly.
The confluence of Internet of Things(IoT) , Blockchain(BC) and Artificial Intelligence(AI) acts as a key accelerator for enabling Machine Economy. To be ready for future businesses these technologies needs to be adapted by extending the IoT capabilities to Economy of Things (EoT) capabilities. In this paper we focus on one such implementation experience for Smart Toll Transaction application in the domain of mobility. Our paper showcases a possible solution by leveraging negotiations, decision making, distributed learning capabilities at the devices level using AI-enabled Multi-Agent Systems and the real-time smart contracts between the Cars and Tolls using Blockchain. This solution also showcases the monetization of real time data coming from various IoT devices which are part of vehicles and infrastructure. While blockchain secures the privacy of the participants it also acts as an economic transactional layer and governance layer between the devices in the networ
The interoperability across multiple or many blockchains would play a critical role in the forthcoming blockchain-based data management paradigm. In particular, how to ensure the ACID properties of those transactions across an arbitrary number of blockchains remains an open problem in both academic and industry: Existing solutions either work for only two blockchains or requires a centralized component, neither of which would meet the scalability requirement in practice. This short paper shares our vision and some early results toward scalable cross-blockchain transactions. Specifically, we design two distributed commit protocols and, both analytically and experimentally, demonstrate their effectiveness.
Vicent Cholvi, Antonio Fernández Anta, Chryssis Georgiou, Nicolas Nicolaou · 5 authors
A Distributed Ledger Object (DLO) is a concurrent object that maintains a totally ordered sequence of records, and supports two basic operations: append, which appends a record at the end of the sequence, and get, which returns the sequence of records. In this work we provide a proper formalization of a Byzantine-tolerant Distributed Ledger Object (BDLO), which is a DLO in a distributed system in which processes may deviate arbitrarily from their indented behavior, i.e. they may be Byzantine. Our formal definition is accompanied by algorithms to implement BDLOs by utilizing an underlying Byzantine Atomic Broadcast service. We then utilize the BDLO implementations to solve the Atomic Appends problem against Byzantine processes. The Atomic Appends problem emerges when several clients have records to append, the record of each client has to be appended to a different BDLO, and it must be guaranteed that either all records are appended or none. We present distributed algorithms implementing solutions for the Atomic Appends problem when the clients (which are involved in the appends) and the servers (which maintain the BDLOs) may be Byzantine.
Envisioned to be the future of secured distributed systems, blockchain networks have received increasing attention from both the industry and academia in recent years. However, blockchain mining processes demand high hardware costs and consume a vast amount of energy (studies have shown that the amount of energy consumed in Bitcoin mining is almost the same as the electricity used in Ireland). To address the high mining cost problem of blockchain networks, in this paper, we propose a blockchain mining resources allocation algorithm to reduce the mining cost in PoW-based (proof-of-work-based) blockchain networks. We first propose an analytical queueing model for general blockchain networks. In our queueing model, transactions arrive randomly to the queue and are served in a batch manner with unknown service rate probability distribution and agnostic to any priority mechanism. Then, we leverage the Lyapunov optimization techniques to propose a dynamic mining resources allocation algorithm (DMRA), which is parameterized by a tuning parameter $K \gt 0$. We show that our algorithm achieves an $[O(1/ K),O(K)]$ cost-optimality-gap-vs-delay tradeoff. Our simulation results also demonstrate the effectiveness of DMRA in reducing mining costs.
SURFACE, standing for Secure, Use-case adaptive, and Relatively Fork-free Approach of Chain Extension, is a consensus algorithm that is designed for real-world networks and enjoys the benefits from both the Nakamoto consensus and Byzantine Fault Tolerance (BFT) consensus. In SURFACE, a committee is randomly selected every round to validate and endorse the proposed new block. The size of the committee can be adjusted according to the underlying network to make the blockchain mostly fork-free with a reasonable overhead in communication. Consequently, the blockchain can normally achieve fast probabilistic confirmation with high throughput and low latency. SURFACE also provides a BFT mechanism to guarantee ledger consistency in case of an extreme network situation such as large network partition or being under massive DDoS attacks.
Guntur Dharma Putra, Volkan Dedeoglu, Salil S Kanhere, Raja Jurdak
An Intrusion Detection System (IDS) aims to alert users of incoming attacks by deploying a detector that monitors network traffic continuously. As an effort to increase detection capabilities, a set of independent IDS detectors typically work collaboratively to build intelligence of holistic network representation, which is referred to as Collaborative Intrusion Detection System (CIDS). However, developing an effective CIDS, particularly for the IoT ecosystem raises several challenges. Recent trends and advances in blockchain technology, which provides assurance in distributed trust and secure immutable storage, may contribute towards the design of effective CIDS. In this poster abstract, we present our ongoing work on a decentralized CIDS for IoT, which is based on blockchain technology. We propose an architecture that provides accountable trust establishment, which promotes incentives and penalties, and scalable intrusion information storage by exchanging bloom filters. We are currently implementing a proof-of-concept of our modular architecture in a local test-bed and evaluate its effectiveness in detecting common attacks in IoT networks and the associated overhead.
Most current blockchains require all full nodes to execute all tasks limits the throughput of existing blockchains, which are well documented and among the most significant hurdles for the widespread adoption of decentralized technology. This paper extends out presentation of Flow, a pipelined blockchain architecture, which separates the process of consensus on the transaction order from transaction computation. As we experimentally showed in our previous white paper, our architecture provides a significant throughput improvement while preserving the security of the system. Flow exploits the heterogeneity offered by the nodes, in terms of bandwidth, storage, and computational capacity, and defines the roles for the nodes based on their tasks in the pipeline, i.e., Collector, Consensus, Execution, and Verification. While transaction collection from the user agents is completed through the bandwidth-optimized Collector Nodes, the execution of them is done by the compute-optimized Execution Nodes. Checking the execution result is then distributed among a more extensive set of Verification Nodes, which confirm the result is correct in a distributed and parallel manner. In contrast to more traditional blockchain architectures, Flow's Consensus Nodes do not execute the transaction. Instead, Verification Nodes report observed faulty executions to the Consensus Nodes, which adjudicate the received challenges and slash malicious actors. In this paper, we detail the lifecycle of the transactions from the submission to the system until they are getting executed. The paper covers the Collector, Consensus, and Execution role. We provide a protocol specification of collecting the transactions, forming a block, and executing the resulting block. Moreover, we elaborate on the safety and liveness of the system concerning these processes.
Aman Sharma, Ankit Agrawal, Ashutosh Bhatia, Kamlesh Tiwari
Bitcoin is the most popular cryptocurrency used worldwide. It provides pseudonymity to its users by establishing identity using public keys as transaction end-points. These transactions are recorded on an immutable public ledger called Blockchain which is an append-only data structure. The popularity of Bitcoin has increased unreasonably. The general trend shows a positive response from the common masses indicating an increase in trust and privacy concerns which makes an interesting use case from the analysis point of view. Moreover, since the blockchain is publicly available and up-to-date, any analysis would provide a live insight into the usage patterns which ultimately would be useful for making a number of inferences by law-enforcement agencies, economists, tech-enthusiasts, etc. In this paper, we study various applications and techniques of performing data analytics over Bitcoin blockchain from a graph theoretic perspective. We also propose a framework for performing such data analytics and explored a couple of use cases using the proposed framework.
Although recent advances of blockchain systems, notably in the form of cryptocurrency, have drawn tremendous interests from both researchers and practitioners, limited studies existed toward the theoretical foundation of blockchains. This paper presents the first study on the algebraic structure of blockchains with an emphasis on the internal properties under algebraic groups. We axiomatically construct a blockchain group and derive some interesting properties that can be potentially taken into the design space and parametric analysis of real-world blockchain systems.
Haoqian Zhang, Yancheng Zhao, Abhishek Paryani, Ke Yi
Internet censorship has been implemented in several countries to prevent citizens from accessing information and to suppress discussion of specific topics. This paper presents Infnote, a platform that helps eliminate the problem of sharing content in these censorship regimes. Infnote is a decentralized information sharing system based on blockchain and peer-to-peer network, aiming to provide an easy-to-use medium for users to share their thoughts, insights and views freely without worrying about data tampering and data loss. Infnote provides a solution that is able to work on any level of Internet censorship. Infnote uses multi-chains architecture to support various independent applications or different functions in an application.
This paper presents IBFT, a simple and elegant Byzantine fault-tolerant consensus algorithm that is used to implement state machine replication in the \emph{Quorum} blockchain. IBFT assumes a partially synchronous communication model, where safety does not depend on any timing assumptions and only liveness depends on periods of synchrony. The algorithm is deterministic, leader-based, and optimally resilient - tolerating $f$ faulty processes out of $n$, where $n \geq 3f+1$. During periods of good communication, IBFT achieves termination in three message delays and has $O(n^2)$ total communication complexity.
We study the problem of state machine replication (SMR)---the underlying problem addressed by blockchain protocols---in the presence of a malicious adversary who can corrupt some fraction of the parties running the protocol. Existing protocols for this task assume either a synchronous network (where all messages are delivered within some known time $Δ$) or an asynchronous network (where messages can be delayed arbitrarily). Although protocols for the latter case give seemingly stronger guarantees, this is not the case since they (inherently) tolerate a lower fraction of corrupted parties. We design an SMR protocol that is network-agnostic in the following sense: if it is run in a synchronous network, it tolerates $t_s$ corrupted parties; if the network happens to be asynchronous it is resilient to $t_a \leq t_s$ faults. Our protocol achieves optimal tradeoffs between $t_s$ and $t_a$.
Pu Yuan, Kan Zheng, Xiong Xiong, Kuan Zhang · 5 authors
As a highly scalable permissioned blockchain platform, Hyperledger Fabric supports a wide range of industry use cases ranging from governance to finance. In this paper, we propose a model to analyze the performance of a Hyperledgerbased system by using Generalised Stochastic Petri Nets (GSPN). This model decomposes a transaction flow into multiple phases and provides a simulation-based approach to obtain the system latency and throughput with a specific arrival rate. Based on this model, we analyze the impact of different configurations of ordering service on system performance to find out the bottleneck. Moreover, a mathematical configuration selection approach is proposed to determine the best configuration which can maximize the system throughput. Finally, extensive experiments are performed on a running system to validate the proposed model and approaches.
Automotive software is increasingly complex and critical to safe vehicle operation, and related embedded systems must remain up-to-date to ensure long-term system performance. Update mechanisms and data modification tools introduce opportunities for malicious actors to compromise these cyber-physical systems, and for trusted actors to mistakenly install incompatible software versions. A distributed and stratified "black box" audit trail for automotive software and data provenance is proposed to assure users, service providers, and original equipment manufacturers (OEMs) of vehicular software integrity and reliability. The proposed black box architecture is both layered and diffuse, employing distributed hash tables (DHT), a parity system and a public blockchain to provide high resilience, assurance, scalability, and efficiency for automotive and other high-assurance systems.
The blockchain paradigm provides a mechanism for content dissemination and distributed consensus on Peer-to-Peer (P2P) networks. While this paradigm has been widely adopted in industry, it has not been carefully analyzed in terms of its network scaling with respect to the number of peers. Applications for blockchain systems, such as cryptocurrencies and IoT, require this form of network scaling. In this paper, we propose a new stochastic network model for a blockchain system. We identify a structural property called \emph{one-endedness}, which we show to be desirable in any blockchain system as it is directly related to distributed consensus among the peers. We show that the stochastic stability of the network is sufficient for the one-endedness of a blockchain. We further establish that our model belongs to a class of network models, called monotone separable models. This allows us to establish upper and lower bounds on the stability region. The bounds on stability depend on the connectivity of the P2P network through its conductance and allow us to analyze the scalability of blockchain systems on large P2P networks. We verify our theoretical insights using both synthetic data and real data from the Bitcoin network.
The ambition of the Lightning Network is to provide a second layer to the Bitcoin network to enable transactions confirmed instantly, securely and anonymously with a world scale capacity using a decentralized protocol. Some of the current propositions and implementations present some difficulties in anonymity, scaling and decentalization. The Ant Routing algorithm for the Lightning Network was proposed in \cite{GrunspanPerez} for maximal decentralization, anonymity and potential scaling. It solves several problems of current implementation, such as channel information update and centralization by beacon nodes. Ant Routing nodes play all the same role and don't require any extra information on the network topology beside for their immediate neighbors. The goal of LN transactions are completed instantaneously and anonymously. We study the scaling of the Ant Routing protocol. We propose a precise implementation, with efficient memory management using AVL trees. We evaluate the efficiency of the algorithm and we estimate the memory usage of nodes by local node workload simulations. We prove that the number of transactions per second that Ant Routing can sustain is of the order of several thousands which is enough for a global payment network.
The state-of-the-art for auditing and reproducing scientific applications on high-performance computing (HPC) systems is through a data provenance subsystem. While recent advances in data provenance lie in reducing the performance overhead and improving the user's query flexibility, the fidelity of data provenance is often overlooked: there is no such a way to ensure that the provenance data itself has not been fabricated or falsified. This paper advocates to leverage blockchains to deliver immutable and autonomous data provenance services such that scientific data are trustworthy. The challenges for adopting blockchains to HPC include designing a new blockchain architecture compatible with the HPC platforms and, more importantly, a set of new consensus protocols for scientific applications atop blockchains. To this end, we have designed the proof-of-scalable-traceability (POST) protocol and implemented it in a blockchain prototype, namely SciChain, the very first blockchain system for HPC. We evaluated SciChain by comparing it with multiple state-of-the-art systems; Experimental results showed that SciChain guaranteed trustworthy data while incurring orders of magnitude lower overhead.
Suyash Gupta, Sajjad Rahnama, Jelle Hellings, Mohammad Sadoghi
Recent developments in blockchain technology have inspired innovative new designs in resilient distributed and database systems. At their core, these blockchain applications typically use Byzantine fault-tolerant consensus protocols to maintain a common state across all replicas, even if some replicas are faulty or malicious. Unfortunately, existing consensus protocols are not designed to deal with geo-scale deployments in which many replicas spread across a geographically large area participate in consensus. To address this, we present the Geo-Scale Byzantine FaultTolerant consensus protocol (GeoBFT). GeoBFT is designed for excellent scalability by using a topological-aware grouping of replicas in local clusters, by introducing parallelization of consensus at the local level, and by minimizing communication between clusters. To validate our vision of high-performance geo-scale resilient distributed systems, we implement GeoBFT in our efficient ResilientDB permissioned blockchain fabric. We show that GeoBFT is not only sound and provides great scalability, but also outperforms state-of-the-art consensus protocols by a factor of six in geo-scale deployments.