Distributed ledgers are enabling novel applications in traditional domains, such as finance, healthcare and supply chains, and in emerging domains such as metaverse. We observe that the current ecosystem is fragmented, with different blockchains operating in silos. Interledger applications that can access resources in different ledgers can tap into the billions of dollars worth of locked resources, but they require support for interledger communication. Existing interledger communications, however, are either insecure, inefficient, or application specific. Our goal is to design a system for interledger applications. To this end, we design and implement that achieves strong security guarantees and high performance while providing the general message passing abstraction to the applications. Our system leverages trusted hardware for performance and threshold signature schemes for strong security. It consists of multiple lightweight clients that verify transactions within their trusted execution environments before generating threshold signatures. We conduct extensive performance evaluations of and compare it against three state-of-the-art systems, namely WeCross, Cosmos IBC and LayerZero-V2. The results show that is efficient, achieving up to$42\times$higher throughput than the baselines.

Database systems are paying more attention to data security in recent years. Immutable systems such as blockchains, verifiable databases, and ledger databases are equipped with various verifiability mechanisms to protect data. Such systems often adopt different threat models, and techniques, therefore, have different performance implications compared to traditional database systems. So far, there is no uniform benchmarking tool for evaluating the performance of these systems, especially at the level of verification functions. In this paper, we first survey the design space of theverifiability-enabled database systemsalong five dimensions: threat model, authenticated data structure (ADS), query processing, verification, and auditing. Based on this survey, we design and implement VeriBench, a benchmark framework forverifiability-enabled database systems.VeriBench enables a fair comparison of systems designed with different underlying technologies that share the client-side verification scheme, and focuses on design space exploration to provide a deeper understanding of different system design choices. VeriBench incorporates micro- and macro-benchmarks to provide a comprehensive evaluation. Further, VeriBench is designed to enable easy extension for benchmarking new systems and workloads. We run VeriBench to conduct a comprehensive analysis of state-of-the-art systems comprising blockchains, ledger databases, and log transparency technologies. The results expose the weaknesses and strengths of each underlying design choice, and the insights should serve as guidance for future development.

This paper presents a systematic and comprehensive survey on blockchain interoperability, where interoperability is defined as the ability of blockchains to flexibly transfer assets, share data, and invoke smart contracts across a mix of public, private, and consortium blockchains without any changes to the underlying blockchain systems. Analyzing the vast landscape of both research papers and industry projects, we classify the existing works into five categories, namely, (1) sidechains, (2) notary schemes, (3) hashed time lock contracts (HTLC), (4) relays, and (5) blockchain agnostic protocols. We analyze the existing works under a taxonomy that consists of system and safety characteristics, such as decentralization, direction of communication, locking mechanism, verification mechanism, trust, safety, liveness, and atomicity. Different from other surveys, we are the first to evaluate the performance of some representative interoperability approaches between Bitcoin and Ethereum covering sidechains, notary schemes, and HTLCs. Even though the performance of cross-chain transactions is low (typically fewer than 10 transactions per second), the main reason is the underlying blockchain (e.g., Bitcoin and Ethereum) and not the interoperability approach. Finally, we discuss existing challenges and possible research directions in blockchain interoperability. For example, we identify challenges in interoperability across permissioned and permissionless blockchains, in interacting with scripting blockchains, in security and privacy.

With the emergence of hybrid blockchain database systems, we aim to provide an in-depth analysis of the performance and trade-offs among a few representative systems. To achieve this goal, we implement Veritas and BlockchainDB from scratch. For Veritas, we provide two flavors to target the crash fault-tolerant (CFT) and Byzantine fault-tolerant (BFT) application scenarios. Specifically, we implement Veritas with Apache Kafka to target CFT application scenarios, and Veritas with Tendermint to target BFT application scenarios. We compare these three systems with the existing open-source implementation of BigchainDB. BigchainDB uses Tender-mint for consensus and provides two flavors: a default implementation with blockchain pipelining and an optimized version that includes blockchain pipelining and parallel transaction validation. Our experimental analysis confirms that CFT designs, which are typically used by distributed databases, exhibit much higher performance than BFT designs, which are specific to blockchains. On the other hand, our extensive analysis highlights the variety of design choices faced by the developers and sheds some light on the trade-offs that need to be done when designing a hybrid blockchain database system.

In the last few years, distributed machine learning has been usually executed over heterogeneous networks such as a local area network within a multi-tenant cluster or a wide area network connecting data centers and edge clusters. In these heterogeneous networks, the link speeds among worker nodes vary significantly, making it challenging for state-of-the-art machine learning approaches to perform efficient training. Both centralized and decentralized training approaches suffer from low-speed links. In this paper, we propose a decentralized approach, namely NetMax, that enables worker nodes to communicate via high-speed links and, thus, significantly speed up the training process. NetMax possesses the following novel features. First, it consists of a novel consensus algorithm that allows worker nodes to train model copies on their local dataset asynchronously and exchange information via peer-to-peer communication to synchronize their local copies, instead of a central master node (i.e., parameter server). Second, each worker node selects one peer randomly with a fine-tuned probability to exchange information per iteration. In particular, peers with high-speed links are selected with high probability. Third, the probabilities of selecting peers are designed to minimize the total convergence time. Moreover, we mathematically prove the convergence of NetMax. We evaluate NetMax on heterogeneous cluster networks and show that it achieves speedups of 3.7×, 3.4×, and 1.9× in comparison with the state-of-the-art decentralized training approaches Prague, Allreduce-SGD, and AD-PSGD, respectively.

Blockchain has come a long way - a system that was initially proposed specifically for cryptocurrencies is now being adapted and adopted as a general-purpose transactional system. As blockchain evolves into another data management system, the natural question is how it compares against distributed database systems. Existing works on this comparison focus on high-level properties, such as security and throughput. They stop short of showing how the underlying design choices contribute to the overall differences. Our work fills this important gap.

Smart contracts have enabled blockchain systems to evolve from simple cryptocurrency platforms to general transactional systems. A new architecture called execute-order-validate has been proposed in Hyperledger Fabric to support parallel transactions. However, this architecture might render many invalid transactions when serializing them. This problem is further exaggerated as the block formation rate is inherently limited due to other factors beside data processing, such as cryptography and consensus. Inspired by optimistic concurrency control in modern databases, we propose a novel method to enhance the execute-order-validate architecture, by reordering transactions to reduce the abort rate. In contrast to existing blockchains that adopt database's preventive approaches which might over-abort serializable transactions, our method is theoretically more fine-grained: unserializable transactions are aborted before reordering and the rest are guaranteed to be serializable. We implement our method in two blockchains respectively, FabricSharp on top of Hyperledger Fabric, and FastFabricSharp on top of FastFabric. We compare the performance of FabricSharp with vanilla Fabric and three related systems, two of which are respectively implemented with one standard and one state-of-the-art concurrency control techniques from databases. The results demonstrate that FabricSharp achieves 25% higher throughput compared to the other systems in nearly all experimental scenarios. Moreover, the FastFabricSharp's improvement on FastFabric is up to 66%.

With 5G on the verge of being adopted as the next mobile network, there is a need to analyze its impact on the landscape of computing and data management. In this paper, we analyze the impact of 5G on both traditional and emerging technologies and project our view on future research challenges and opportunities. With a predicted increase of 10-100× in bandwidth and 5-10x decrease in latency, 5G is expected to be the main enabler for smart cities, smart IoT and efficient healthcare, where machine learning is conducted at the edge. In this context, we investigate how 5G can help the development of federated learning. Network slicing, another key feature of 5G, allows running multiple isolated networks on the same physical infrastructure. However, security remains the main concern in the context of virtualization, multi-tenancy and high device density. Formal verification of 5G networks can be applied to detect security issues in massive virtualized environments. In summary, 5G will make the world even more densely and closely connected. What we have experienced in 4G connectivity will pale in comparison to the vast amounts of possibilities engendered by 5G.

Existing blockchain systems scale poorly because of their distributed consensus protocols. Current attempts at improving blockchain scalability are limited to cryptocurrency. Scaling blockchain systems under general workloads (i.e., non-cryptocurrency applications) remains an open question. This work takes a principled approach to apply sharding to blockchain systems in order to improve their transaction throughput at scale. This is challenging, however, due to the fundamental difference in failure models between databases and blockchain. To achieve our goal, we first enhance the performance of Byzantine consensus protocols, improving individual shards' throughput. Next, we design an efficient shard formation protocol that securely assigns nodes into shards. We rely on trusted hardware, namely Intel SGX, to achieve high performance for both consensus and shard formation protocol. Third, we design a general distributed transaction protocol that ensures safety and liveness even when transaction coordinators are malicious. Finally, we conduct an extensive evaluation of our design both on a local cluster and on Google Cloud Platform. The results show that our consensus and shard formation protocols outperform state-of-the-art solutions at scale. More importantly, our sharded blockchain reaches a high throughput that can handle Visa-level workloads, and is the largest ever reported in a realistic environment.

The continuous increase in volume, variety and velocity of Big Data exposes datacenter resource scaling to an energy utilization problem. Traditionally, datacenters employ x86-64 (big) server nodes with power usage of tens to hundreds of Watts. But lately, low-power (small) systems originally developed for mobile devices have seen significant improvements in performance. These improvements could lead to the adoption of such small systems in servers, as announced by major industry players. In this context, we systematically conduct a performance study of Big Data execution on small nodes in comparison with traditional big nodes, and present insights that would be useful for future development. We run Hadoop MapReduce, MySQL and in-memory Shark workloads on clusters of ARM big. LITTLE boards and Intel Xeon server systems. We evaluate execution time, energy usage and total cost of running the workloads on self-hosted ARM and Xeon nodes. Our study shows that there is no one size fits all rule for judging the efficiency of executing Big Data workloads on small and big nodes. But small memory size, low memory and I/O bandwidths, and software immaturity concur in canceling the lower-power advantage of ARM servers. We show that I/O-intensive MapReduce workloads are more energy-efficient to run on Xeon nodes. In contrast, database query processing is always more energy-efficient on ARM servers, at the cost of slightly lower throughput. With minor software modifications, CPU-intensive MapReduce workloads are almost four times cheaper to execute on ARM servers.