Consensus algorithms play a central role in many distributed systems, including blockchains. The most practical consensus algorithms are based on the partial synchrony model. While partially synchronous protocols are relatively simple, they cannot maintain liveness when the message delivery latency is uncertain. Asynchronous protocols do not rely on bounded latency to maintain liveness, but they are much more difficult to understand and implement, being usually described as a composition of several layers of algorithms. Moreover, due to the FLP impossibility theorem, they only provide probabilistic liveness guarantees. These factors make the correctness of asynchronous protocols challenging to verify, and there have been liveness bugs in these protocols that remain unnoticed for years. We introduce SureDistrib, a formal framework for specifying and verifying probabilistic safety and liveness properties of asynchronous distributed protocols. Our framework supports specifying probabilistic algorithms that depend on other probabilistic functionalities, such as binary agreement algorithms depending on common coins. We define refinement relations for such systems, and prove composition lemmas that replace the underlay functionality with an implementation, so that the composed system refines the original system with an abstract underlay. Based on our framework, we give the first mechanized proof that an asynchronous byzantine fault-tolerant binary agreement algorithm terminates with probability 1 ("almost-sure termination").

Blockchains operating at the global scale demand high-performance byzantine fault-tolerant (BFT) consensus protocols. Most classic PBFT-like protocols suffer from an issue known as the leader bottleneck, which severely limits their throughput and resource utilization. Recently, Directed Acyclic Graph, or DAG-based protocols, have emerged as a promising approach for eliminating the leader bottleneck and achieving better performance. They attain higher throughput by separating data dissemination and block ordering. However, their safety and liveness logic is also significantly more elaborate. So far, most DAG-based protocols have only enjoyed on-paper security proofs, and it is not clear how to construct formal proofs of these protocols efficiently. We introduce LiDO-DAG, a concurrent object model that abstracts the common logic of these protocols. LiDO-DAG is constructed by combining a DAG abstraction and LiDO, a recently proposed abstraction for leader-based consensus. To demonstrate that our framework enables rapid validation of new DAG-based protocol designs, we implemented LiDO-DAG in Coq and applied it to three recent DAG-based protocols, including Narwhal, Bullshark, and Sailfish. Our framework readily yields mechanized safety and liveness proofs for all three protocols, which are also the first mechanized liveness proofs of any DAG-based protocol. Our framework has also revealed an optimization for Sailfish that improves its worst-case latency.

Achieving consensus is a challenging and ubiquitous problem in distributed systems that is only made harder by the introduction of malicious byzantine servers. While significant effort has been devoted to the benign and byzantine failure models individually, no prior work has considered the mechanized verification of both in a generic way. We claim this is due to the lack of an appropriate abstraction that is capable of representing both benign and byzantine consensus without either losing too much detail or becoming impractically complex. We build on recent work on the atomic distributed object model to fill this void with a novel abstraction called AdoB. In addition to revealing important insights into the essence of consensus, this abstraction has practical benefits for easing distributed system verification. As a case study, we proved safety and liveness properties for AdoB in Coq, which are the first such mechanized proofs to handle benign and byzantine consensus in a unified manner. We also demonstrate that AdoB faithfully models real consensus protocols by proving it is refined by standard network-level specifications of Fast Paxos and a variant of Jolteon.

Byzantine fault-tolerant state machine replication (SMR) protocols, such as PBFT, HotStuff, and Jolteon, are essential for modern blockchain technologies. However, they are challenging to implement correctly because they have to deal with any unexpected message from Byzantine peers and ensure safety and liveness at all times. Many formal frameworks have been developed to verify the safety of SMR implementations, but there is still a gap in the verification of their liveness. Existing liveness proofs are either limited to the network level or do not cover popular partially synchronous protocols. We introduce LiDO, a consensus model that enables the verification of both safety and liveness of implementations through refinement. We observe that current consensus models cannot handle liveness because they do not include a pacemaker state. We show that by adding a pacemaker state to the LiDO model, we can express the liveness properties of SMR protocols as a few safety properties that can be easily verified by refinement proofs. Based on our LiDO model, we provide mechanized safety and liveness proofs for both unpipelined and pipelined Jolteon in Coq. This is the first mechanized liveness proof for a byzantine consensus protocol with non-trivial optimizations such as pipelining.

Rapid progress in the physical implementation of quantum computers gave birth to multiple recent quantum machines implemented with superconducting technology. In these NISQ machines, each qubit is physically connected to a bounded number of neighbors. This limitation prevents most quantum programs from being directly executed on quantum devices. A compiler is required for converting a quantum program to a hardware-compliant circuit, in particular, making each two-qubit gate executable by mapping the two logical qubits to two physical qubits with a link between them. To solve this problem, existing studies focus on inserting SWAP gates to dynamically remap logical qubits to physical qubits. However, most of the schemes lack the consideration of time-optimality of generated quantum circuits, or are achieving time-optimality with certain constraints. In this work, we propose a theoretically time-optimal SWAP insertion scheme for the qubit mapping problem. Our model can also be extended to practical heuristic algorithms. We present exact analysis results by using our model for quantum programs with recurring execution patterns. We have for the first time discovered an optimal qubit mapping pattern for quantum fourier transformation (QFT) on 2D nearest neighbor architecture. We also present a scalable extension of our theoretical model that can be used to solve qubit mapping for large quantum circuits.

Recent expansions of Internet-of-Things (IoT) applying cloud computing have been growing at a phenomenal rate. As one of the developments, heterogeneous cloud computing has enabled a variety of cloud-based infrastructure solutions, such as multimedia big data. Numerous prior researches have explored the optimizations of on-premise heterogeneous memories. However, the heterogeneous cloud memories are facing constraints due to the performance limitations and cost concerns caused by the hardware distributions and manipulative mechanisms. Assigning data tasks to distributed memories with various capacities is a combinatorial NP-hard problem. This paper focuses on this issue and proposes a novel approach, Cost-Aware Heterogeneous Cloud Memory Model (CAHCM), aiming to provision a high performance cloud-based heterogeneous memory service offerings. The main algorithm supporting CAHCM is Dynamic Data Allocation Advance (2DA) Algorithm that uses genetic programming to determine the data allocations on the cloud-based memories. In our proposed approach, we consider a set of crucial factors impacting the performance of the cloud memories, such as communication costs, data move operating costs, energy performance, and time constraints. Finally, we implement experimental evaluations to examine our proposed model. The experimental results have shown that our approach is adoptable and feasible for being a cost-aware cloud-based solution.

Increasing popular big data applications bring about invaluable information, but along with challenges to industrial community and academia. Cloud computing with unlimited resources seems to be the way out. However, this panacea cannot play its role if we do not arrange fine allocation for cloud infrastructure resources. In this paper, we present a multi-objective optimization algorithm to trade off the performance, availability, and cost of Big Data application running on Cloud. After analyzing and modeling the interlaced relations among these objectives, we design and implement our approach on experimental environment. Finally, three sets of experiments show that our approach can run about 20 percent faster than traditional optimization approaches, and can achieve about 15 percent higher performance than other heuristic algorithms, while saving 4 to 20 percent cost.

The expected advanced network explorations and the growing demand for mobile data sharing and transferring have driven numerous novel applications in Cyber-Physical Systems (CPSs), such as Intelligent Transportation Systems (ITSs). However, current ITS implementations are restricted by the conflicts between security and communication efficiency. Focusing on this issue, this article proposes a Security-Aware Efficient Data Sharing and Transferring (SA-EAST) model, which is designed for securing cloud-based ITS implementations. In applying this approach, we aim to obtain secure real-time multimedia data sharing and transferring. Our experimental evaluation has shown that our proposed model provides an effective performance in securing communications for ITS.