Bitcoin scalability analysis

Layer 2 solutions are designed to increase the speed and efficiency of blockchains. Take a look at their different forms and how they work in this guide. Join us in showcasing the cryptocurrency revolution, one newsletter at a time. Layer 2 scaling solutions increase throughput without tampering with any of the original decentralization or security characteristics that are integral to the original blockchain.

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Content:

• Solving the Blockchain Trilemma: Decentralization, Security & Scalability
• How Bitcoin Can Scale
• The Bitcoin Challenge: How To Tame A Digital Predator – Analysis
• Scalability
• Crypto Long & Short: Could Scalable Payments for Bitcoin Undermine Its Value?
• Bitcoin Transaction Networks: An Overview of Recent Results

WATCH RELATED VIDEO: Bitcoin scalability problem (explained)

Solving the Blockchain Trilemma: Decentralization, Security & Scalability

Lightning Network LN is designed to amend the scalability and privacy issues of Bitcoin. It is a payment channel network where Bitcoin transactions are issued off the blockchain and onion routed through a private payment path with the aim to settle transactions in a faster, cheaper, and more private manner, as they are not recorded in a costly-to-maintain, slow, and public ledger.

The simulator relies only on publicly available data of the network structure and capacities, and generates transactions under assumptions that we attempt to validate based on information spread by certain blog posts of LN node owners.

Our findings on the estimated revenue from transaction fees are in line with the widespread opinion that participation is economically irrational for the majority of the large routing nodes who currently hold the network together. Either traffic or transaction fees must increase by orders of magnitude to make payment routing economically viable. We give worst-case estimates for the potential fee increase by assuming strong price competition among the routers.

We also estimate how current channel structures and pricing policies respond to a potential increase in traffic, how reduction in locked funds on channels would affect the network, and show examples of nodes who are estimated to operate with economically feasible revenue.

Our second set of findings considers privacy. Based on our simulation experiments, we 1 quantitatively characterize the privacy shortcomings of current LN operation, and 2 propose a method to inject additional hops in routing paths to demonstrate how privacy can be strengthened with very little additional transactional cost. Bitcoin is a peer-to-peer, decentralized cryptographic currency [1].

It is a censorship-resistant, permissionless, digital payment system. Anyone can join and leave the network whenever they would like to. Participants can issue payments, which are inserted into a distributed, replicated ledger called blockchain.

Since there is no trusted central party to issue money and guard this financial system, payment validity is checked by all network participants. The necessity of full validation severely limits the scalability of decentralized cryptocurrencies: Bitcoin could theoretically process 27 27 2 7 transactions per second tps [2] ; however, in practice its average transaction throughput is 7 7 7 tps [3].

This is in stark contrast with the throughput of mainstream payment providers; for example, in peak hours Visa is able to achieve 47, tps on its network [4]. To alleviate scalability issues, the cryptocurrency community is continuously inventing new protocols and technologies.

A major line of research is focused on amending existing currencies without modifying the consensus layer by introducing a new layer, i. These proposals are called Layer-2 protocols: they allow parties to exchange transactions locally, without broadcasting them to the blockchain network, updating a local balance sheet instead and only utilizing the blockchain as a recourse for disputes.

For an exhaustive review of off-chain protocols, refer to [8]. Among these proposals, the most prominent ones are payment channel networks PCN , in which nodes have several open payment channels, being able to connect to all nodes, possibly through multiple hops. LN is suitable for several application scenarios, for instance, micropayments or e-commerce, with the intent to make everyday Bitcoin usage more convenient and frictionless.

The main difficulty with analyzing how LN operates is that the exact transaction routes are cryptographically hidden from eavesdroppers due to onion routing [10]. LN can only be observed through public information on nodes and channel openings, closings, and capacity changes. The actual amount of Bitcoins circulated in LN is unknown, although in blog posts, some node owners publish high-level statistics, such as their revenue [11] [12] , which can be used as grounds for estimation.

To analyze LN efficiency and profitability, we designed a traffic simulator for LN to analyze the routing costs and potential revenue at different nodes. We assigned roles to nodes by collecting external data 1 , labeling nodes as wallet services, shops, and other merchants. Using node labels, we simulated the flow of Bitcoin transactions from ordinary users towards merchants over time, based on the natural assumption that transactions are routed through the path that charges the minimum total transaction fee.

By taking the dynamically changing transaction fees of the LN nodes into account, we designed a method to predict the optimal fee pricing policy for individual nodes in case of the cheapest path routing. To the best of our knowledge, there has been no previous empirical study on LN transaction fees.

Our traffic simulator hence opens the possibility for addressing questions of transaction routes, amounts, fees, and other measures otherwise depending upon strictly private information, based solely on the observable network structure.

By releasing the source code of our tool, we allow node owners to fit various parameters to their private observation on LN traffic. In particular, in this paper the simulator enables us to draw two major conclusions:. Economic incentives: Currently, LN provides little to no financial incentive for payment routing. Low routing fees do not sufficiently compensate the routing nodes that essentially hold the network together.

Our results show that in general, transaction fees are underpriced, since for many possible payments there is no alternative path to execute the transaction. We also give estimates of how the current network and fee structure responds to increase in traffic and decrease in channel capacities, thus assessing the income potential in different strategies. We provide an open source tool for nodes to experimentally design their channels, capacities, and fees by incorporating all possible information that they privately infer from the traffic over their channels.

Privacy: We quantitatively analyze the privacy provisions of LN. Despite onion routing, we observe that strong statistical evidence can be gathered about the sender and receiver of LN payments, since a substantial portion of payments involve only a single routing intermediary, who can easily de-anonymize participants.

We find that using deliberately suboptimal, longer routing paths can potentially restore privacy while only marginally increasing the cost of an average transaction, as it is partially already incorporated in other implementations of the Lightning protocol [13].

The rest of the paper is organized as follows. In Section 3 , we provide a brief background on LN and its fee structure. In Section 4 , our traffic simulator is presented.

We discuss our experimental results in three sections. We investigate the price competition and the potential to increase fees, under various assumptions, in Section 5. We estimate the profitability of the central router nodes under estimated current and potentially increased future traffic in Section 6. Finally, we estimate the amount of privacy shortcomings due to too short paths and potential mitigations in Section 7.

We conclude our paper in Section 8. To the best of our knowledge, we have conducted the first empirical analysis on LN transaction fees, similar to the way empirical and theoretical studies on on-chain transaction fees have been conducted during the early adoption of cryptocurrencies. Kaskaloglu asserted that near-zero transaction fees cannot last long as block rewards diminish [15]. Easley et al. Recently, BitMEX, using a single LN node, has experimented with setting different transaction fees to measure the effect on routing revenue [12] , which shows a similar pattern to our simulation experiments.

Unlike on-chain transactions, the LN transaction fee market is not yet consolidated. Some actors behave financially rationally, while the vast majority exhibit altruistic behavior, which parallels the early days of Bitcoin [14].

Similarly to on-chain fees, we expect to see more maturity and a similar evolution in the LN transaction fee market in the future. Branzei et al. They conjectured a lower miner income from on-chain transaction fees as users tend to use and issue transactions on LN. In [18] , the transaction fees of various payment channels are compared, however, without reference to the underlying network dynamics. Depleted payment channels account for many efficiency issues in PCNs. Khalil and Gervais devised a handy algorithm to revive imbalanced payment channels without opening new ones [19].

PCNs can also be considered to be creation games. A user might decide to create a payment channel to a destination node or just route the payment in the already existing PCN. The former is more expensive; however, repeated payments can amortize the on-chain cost of opening a payment channel.

Avarikioti et al. In a similar game-theoretic work, the effect of routing fees was analyzed [21]. It was again found that the star graph is a near-optimal solution to the network design problem.

Even though transactions in LN are not recorded on the blockchain, they do not provide privacy guarantees. As early as , Herrera et al. Single-intermediary payments do not provide privacy, although they have higher utility. Tang et al. Although a recently devised cryptographic protocol solves the privacy issues of single-intermediary routed payments [24] , the protocol is not yet in use due to its complexity of implementation.

After the launch of LN, several studies have investigated the graph properties of LN [25] [26] [27]. They described the topology of LN at an arbitrarily chosen point in time and found that LN exhibits a hub and spoke topology, and its degree distribution can be well approximated with a scale-free distribution [25] [26].

Furthermore, these works assessed the robustness of the network against various types of attack strategies: they showed that LN is susceptible to both node [25] [27] and channel [26] removal based attacks. These works are restricted to a static snapshot of LN.

The lack of temporal data has largely limited the insights and results of these contributions. In a Youtube video [28] , an estimate of the routing income is given based on the assumption that the payment probability between any node pair is the same. As it is easy to see, under this assumption the routing income of a node is proportional to its betweenness centrality. In our simulation experiments, we will explicitly compare our prediction with the one based on betweenness centrality and show how the finer structure of our estimation procedure yields more plausible results.

At the time of writing, four research groups published results on payment channel network simulators, each serving purposes very different from ours. Out of them, the simulator of Branzei et al. Their simulator only considers single bidirectional channels or a star topology, and its main goal is to analyze channel opening costs and depletion.

CLoTH [30] is able to provide performance statistics e. In contrast, our LN traffic simulator can produce insights in those areas as well. Finally, the simulator in [31] is a distributed method to minimize the transaction fee of a payment path, subject to the timeliness and feasibility constraints for the success ratio and the average accepted value of the transactions.

In this section we provide a light background on LN and how transaction fee mechanism in LN is structured. Throughout the paper we are using the following notations. Sometimes we omit the time parameter. Let d i , j d i,j d i , j denote the length of the shortest path between a node i i i and another node j j j. The transitivity or global clustering coefficient of a network is the ration of present triangles and all possible triangles.

To assess centrality we apply effectiveness and central point dominance CPD :. The CPD of a complete graph is 0 0 0 , while it is 1 1 1 for a star graph. A payment channel allows users to make multiple cryptocurrency transactions without committing all of the transactions to the blockchain.

In a typical payment channel, only two transactions are added to the blockchain, but theoretically, an unlimited number of payments can be made between the participants.

Parties can open a payment channel by escrowing funds on the blockchain for subsequent use only between those two parties. The sum of the individual balances on the two sides of the channel is usually referred to as the capacity. We illustrate the operation of a payment channel by an example. Let Alice and Bob escrow 1 and 2 tokens respectively, by committing a transaction to the blockchain that sets up a new channel.

How Bitcoin Can Scale

I will be making some assumptions in order to conduct this analysis, but each will be stated clearly. The point of this article is simply to find out what would happen in the future if both Bitcoin and the Lightning Network were to achieve significant mainstream adoption with the current block size. The Lightning Network initially launched with some fanfare in March Meanwhile, Bitcoin Cash has gone through a bit of a rough patch, to put it kindly. And, indeed, it may be. However, the bear market has taken its toll on transaction volume, and Bitcoin is not being used as much today as it was a year ago.

The Bitcoin Challenge: How To Tame A Digital Predator – Analysis

JavaScript is disabled for your browser. Some features of this site may not work without it. Date Author Madenouei, Nahid Alimohammadi. Metadata Show full item record. Abstract During the past few years, blockchain technologies have attracted substantial attention from researchers, engineers, and enterprises. These technologies provide decentralized platforms to validate different types of transactions without relying on a central authority. Since the advent of the popular Bitcoin network [2], [3], various types of protocols, among them DAG-based distributed ledgers, have been offered to improve the Bitcoin networks shortcomings in the areas of scalability, throughput, and security. Researchers have also been motivated to study blockchain-based applications across multiple domains with novel designs and capabilities [4].

Scalability

Bitcoin and Ethereum are the most widely recognized projects of the blockchain movement, seeking to replace our current trading, financial and economic system with one that is more decentralized and secure. Millions have invested time and money into these networks in the belief that they will one day grow to dominate a decentralized digital world. Lack of scalability keeps many well funded and community backed blockchains operating in beta and alpha mode. Their coins, many of which are built to function as utilities, are bought primarily to be held as speculative securities because slow transaction speeds prevent them from being traded for everyday goods and services. Vitalik believes that at a fundamental level, Blockchains can only achieve 2 out of 3 of these traits at one time:.

Crypto Long & Short: Could Scalable Payments for Bitcoin Undermine Its Value?

From the moment Bitcoin was first announced, scalability has been brought up as one of the great challenges of digital currency. In a decentralized system with no leaders, the responsibility to maintain the integrity of the currency falls on every user equally. This leads to a trade-off between having an easy to verify system which stays smaller, or a large system that most users can't verify themselves. While the goal of an easily verifiable, widely scalable digital currency remains a challenge, there has been significant progress towards that goal, and the MIT DCI has been an important part of this progress. The size of this shared state is a scalability constraint for the network, as the size of the set expands as more users join the system, increasing resource requirements of all nodes. We introduce a hash based accumulator to locally represent the UTXO set, which is logarithmic in the size of the full set.

Bitcoin Transaction Networks: An Overview of Recent Results

Cryptocurrencies are distributed systems that allow exchanges of native and non- tokens between participants. The availability of the complete historical bookkeeping opens up an unprecedented possibility: that of understanding the evolution of a cryptocurrency's network structure while gaining useful insights into the relationships between users' behavior and cryptocurrency pricing in exchange markets. In this article we review some recent results concerning the structural properties of the Bitcoin Transaction Networks , a generic name referring to a set of three different constructs: the Bitcoin Address Network , the Bitcoin User Network , and the Bitcoin Lightning Network. The picture that emerges is of a system growing over time, which becomes increasingly sparse and whose mesoscopic structural organization is characterized by the presence of an increasingly significant core-periphery structure. Such a peculiar topology is accompanied by a highly uneven distribution of bitcoins, a result suggesting that Bitcoin is becoming an increasingly centralized system at different levels. A cryptocurrency is an online payment system for which the storage and verification of transactions—and therefore the safeguarding of the system's consistency itself—are decentralized , i. This result can be achieved by securing financial transactions through a clever combination of cryptographic technologies [ 1 ]. Bitcoin, the first and most popular cryptocurrency, was introduced in by Satoshi Nakamoto [ 2 ].

One of the most important motives behind the invention of cryptocurrencies is that they should have the ability to be an alternative financial system. That is, we, the users, should be able to use them just like how the fiat currencies are being used today, but with many more features and ease. While this thought is very ambitious, the journey has already begun with Bitcoin.

Bitcoin Basics. How to Store Bitcoin. Bitcoin Mining. Key Highlights. In a world with almost 8 billion people, this is not nearly enough throughput to allow Bitcoin to serve as the global currency.

The two largest cryptocurrencies, Bitcoin and Ethereum , are often compared and the debate over which is the better network is a never-ending one. While each of them has its respective set of merits, they also have certain inherent challenges, s calability being one of them. Recently, the Lightning Network LN reached new heights as it crossed the 25k node mark. The network is becoming stronger since participation is increasing on LN, which is also visible on the network itself. The total number of Bitcoin inside the channels reached This helps in creating more liquidity on the network. Moreover, network usage has increased at a phenomenal rate in the last 4 months with unique channels touching 63k and repeated channels reaching 4.

Of all the potential implications of blockchain for the energy sector, the energy use of cryptocurrencies — and bitcoin in particular — has captured the most interest. With bitcoin value tripling in recent months and Facebook announcing its new Libra coin, interest in the energy use of cryptocurrencies is again on the rise. In this commentary, we explain why and how bitcoin uses energy; dig into published estimates of bitcoin energy use and provide our own analysis; and discuss how these trends might evolve in the coming years.