Blockchain networks use consensus mechanisms so that participants can exchange transactions without the need to rely on a trusted third party. Consensus mechanisms using Proof of Work burn significant energy to select a block miner, and this delay limits performance. Other consensus mechanisms such as Proof of Stake or Practical Byzantine Fault Tolerance still designate a single validator to append a block to the chain, preventing blocks from being built and published in parallel. In this article, we introduce a new consensus mechanism, Proof of Descriptor, enabling clients to work together to publish blockchain transactions using a descriptor object which stores information on the cooperative parallel execution of transactions. Proof of Descriptor consensus allows commutative transactions to be mined concurrently. It does not require a single leader to append transactions to the ledger, enabling clients to cooperate on publishing transactions. We also propose a novel graph-based ledger with multiple entry points to facilitate the scalability of Proof of Descriptor, as well as a secure hashing scheme to resist long-range attacks. We demonstrate that our approach is as secure as related works with respect to a malicious leader, 51% attack, and other known blockchain vulnerabilities. Furthermore, our experimental evaluation shows that our approach scales with the size of the network, experiencing up to a 4 \(\times\) improvement in throughput over the fastest sequential blockchain, Solana .

Bitcoin is the leading example of a blockchain application that facilitates peer-to-peer transactions without the need for a trusted third party. This paper considers possible attacks related to the decentralized network architecture of Bitcoin. We perform a data driven study of Bitcoin and present possible attacks based on spatial and temporal characteristics of its network. Towards that, we revisit the prior work, dedicated to the study of centralization of Bitcoin nodes over the Internet, through a fine-grained analysis of network distribution, and highlight the increasing centralization of the Bitcoin network over time. As a result, we show that Bitcoin is vulnerable to spatial, temporal, spatio-temporal, and logical partitioning attacks with an increased attack feasibility due to the network dynamics. We verify our observations through data-driven analyses and simulations, and discuss the implications of each attack on the Bitcoin network. We conclude with suggested countermeasures.

Architectural imperatives due to the slowing of Moore's Law, the broad acceptance of relaxed semantics and the O(n!) worst case verification complexity of generating sequential histories motivate a new approach to concurrent correctness. Desiderata for a new correctness condition are that it be independent of sequential histories, compositional, flexible as to timing, modular as to semantics and free of inherent locking or waiting. We propose Quantifiability, a novel correctness condition that models a system in vector space to launch a new mathematical analysis of concurrency. The vector space model is suitable for a wide range of concurrent systems and their associated data structures. This paper formally defines quantifiability and demonstrates that quantifiability is compositional and non-blocking. Analysis is facilitated with linear algebra, better supported and of much more efficient time complexity than traditional combinatorial methods.

Smart contract transactions demonstrate issues of performance and correctness that application programmers must work around. Although the blockchain consensus mechanism approaches ACID compliance, use cases that rely on frequent state changes are impractical due to the block publishing interval of O(10^1) seconds. The effective isolation level is Read-Committed, only revealing state transitions at the end of the block interval. Values read may be stale and not match program order, causing many transactions to fail when a block is committed. This paper perceives the blockchain as a transactional data structure, using this analogy in the development of a new algorithm, Hash-Mark-Set (HMS), that improves transaction throughput by providing a Read-Uncommitted view of state variables. HMS creates a directed acyclic graph (DAG) from the pending transaction pool. The transaction order derived from the DAG is used to provide a Read-Uncommitted view of the data for new transactions, which enter the DAG as they are received. An implementation of HMS is provided, interoperable with Ethereum and ready for use in smart contracts. Over a wide range of transaction mixes, HMS is demonstrated to improve throughput. A side product of the implementation is a new technique, Runtime Argument Augmentation (RAA), that allows smart contracts to communicate with external data services before submitting a transaction. RAA has use cases beyond HMS and can serve as a lightweight replacement for blockchain oracles.

Bitcoin is the leading example of a blockchain application that facilitates peer-to-peer transactions without the need for a trusted intermediary. This paper considers possible attacks related to the decentralized network architecture of Bitcoin. We perform a data driven study of Bitcoin and present possible attacks based on spatial and temporal characteristics of its network. Towards that, we revisit the prior work, dedicated to the study of centralization of Bitcoin nodes over the Internet, through a fine-grained analysis of network distribution, and highlight the increasing centralization of the Bitcoin network over time. As a result, we show that Bitcoin is vulnerable to spatial, temporal, spatio-temporal, and logical partitioning attacks with an increased attack feasibility due to network dynamics. We verify our observations by simulating attack scenarios and the implications of each attack on the Bitcoin . We conclude with suggested countermeasures.

The following topics are dealt with: cloud computing; resource allocation; queueing theory; optimisation; storage management; cache storage; telecommunication network routing; parallel processing; virtual machines; learning (artificial intelligence).