An Analysis of Transaction Handling in Bitcoin

Bitcoin has become the leading cryptocurrency system, but the limit on its transaction processing capacity has resulted in increased transaction fees and delayed transaction confirmation. As such, it is pertinent to understand and probably predict how transactions are handled by Bitcoin such that a user may adapt the transaction requests and a miner may adjust the block generation strategy and/or the mining pool to join. To this aim, the present paper introduces results from an analysis of transaction handling in Bitcoin. Specifically, the analysis consists of two-part. The first part is an exploratory data analysis revealing key characteristics in Bitcoin transaction handling. The second part is a predictability analysis intended to provide insights on transaction handling such as (i) transaction confirmation time, (ii) block attributes, and (iii) who has created the block. The result shows that some models do reasonably well for (ii), but surprisingly not for (i) or (iii)

Analysis of the confirmation time in proof-of-work blockchains

In blockchain networks driven by Proof of Work, clients spend a certain amount of cryptocurrency (called fees) to control the speed of confirmation of the transactions that they generate. In fact, transactions are confirmed according to a strong priority policy that favors those offering the highest fees. The problem of determining the optimal fee to offer to satisfy certain delay requirements is still widely open and, at the state of the art, mainly reactive methods based on historical data are available. In this work, we propose a queueing model based on the exact transient analysis of a M/MB/1 system to address this problem. The model takes into account (i) the state of the Mempool (the backlog of pending work) when the transaction is generated, (ii) the current transaction arrival intensity and (iii) the distribution of the fees offered by other transactions to the miners. We apply the model to study the performance of the Bitcoin blockchain. Its parameterization is based on an extensive statistical analysis of the transaction characteristics. To this aim, we collected data from over 1.5 million of pending transactions observed in the Mempool of our Bitcoin node. The outcome of our analysis allows us to provide an algorithm to quickly compute the expected transaction confirmation time given the blockchain state, and to highlight new insights on the relations between the transaction fees and confirmation time in BTC blockchain

Evolutionary Game for Mining Pool Selection in Blockchain Networks

In blockchain networks adopting the proof-of-work schemes, the monetary incentive is introduced by the Nakamoto consensus protocol to guide the behaviors of the full nodes (i.e., block miners) in the process of maintaining the consensus about the blockchain state. The block miners have to devote their computation power measured in hash rate in a crypto-puzzle solving competition to win the reward of publishing (a.k.a., mining) new blocks. Due to the exponentially increasing difficulty of the crypto-puzzle, individual block miners tends to join mining pools, i.e., the coalitions of miners, in order to reduce the income variance and earn stable profits. In this paper, we study the dynamics of mining pool selection in a blockchain network, where mining pools may choose arbitrary block mining strategies. We identify the hash rate and the block propagation delay as two major factors determining the outcomes of mining competition, and then model the strategy evolution of the individual miners as an evolutionary game. We provide the theoretical analysis of the evolutionary stability for the pool selection dynamics in a case study of two mining pools. The numerical simulations provide the evidence to support our theoretical discoveries as well as demonstrating the stability in the evolution of miners' strategies in a general case.Comment: Submitted to IEEE Wireless Communication Letter

Bitcoin Transaction Fee Estimation Using Mempool State and Linear Perceptron Machine Learning Algorithm

Bitcoin, the world’s most valued cryptocurrency, uses a network of computers across the globe to create an immutable transaction record on a public ledger known as the blockchain. The blockchain consists of a series of timestamped blocks, where each block contains a series of transactions selected for inclusion in the block, generally based on how high of a fee the transaction allocates to the party responsible for confirming the transaction. Estimating an appropriate fee for Bitcoin transactions is a challenge for many transacting parties using Bitcoin as a digital currency. This work aims to help Bitcoin users save funds in their transaction fees when building multisig transactions by providing fee estimates that referenced the current state of the unconfirmed transaction pool using the perceptron machine learning classification algorithm.