Conference PaperPDF Available

An Overview and Current Status of Blockchain Simulators

May 2021

May 2021

DOI:10.1109/ICBC51069.2021.9461114

Conference: 2021 IEEE International Conference on Blockchain and Cryptocurrency (ICBC)

Authors:

Remigijus Paulavičius

Vilnius University

Ernestas Filatovas

Vilnius University

Content uploaded by Remigijus Paulavičius

Content may be subject to copyright.

An Overview and Current Status of

Blockchain Simulators

Remigijus Paulaviˇ

cius

Institute of Data Science and

Digital Technologies

Vilnius University

Vilnius, Lithuania

remigijus.paulavicius@mif.vu.lt

Saulius Grigaitis

Institute of Data Science and

Digital Technologies

Vilnius University

Vilnius, Lithuania

saulius.grigaitis@mif.vu.lt

Ernestas Filatovas

Institute of Data Science and

Digital Technologies

Vilnius University

Vilnius, Lithuania

ernestas.ﬁlatovas@mif.vu.lt

Abstract—Since the introduction of Bitcoin, blockchain has

attracted tremendous interest from both academia and industry.

During the last decade, various large-scale blockchain sys-

tems were developed. However, the complexity of large-scale

distributed systems makes the performance evaluation process

challenging and costly. Here, blockchain simulators give the

possibility to repeat complex real-world processes at a low cost.

Simulators are easily extensible and can test distributed ledger

performance using different settings and parameter variations.

This work reviews and summarizes the current status of the

state-of-the-art blockchain simulators.

Index Terms—Blockchain, Bitcoin, simulators, review.

I. INTRODUCTION

Since its ﬁrst introduction in cryptocurrency, Bitcoin [1],

blockchain has been recognized as a breakthrough technol-

ogy and attracted much attention from both academia and

industry [2], [3]. However, compared to traditional central-

ized solutions, the decentralized nature limits blockchain

performance [4], which becomes a signiﬁcant constraint of

broader blockchain applications in production [5]. Moreover,

blockchain-based system blind development without initial

performance evaluation may have an immense negative impact

during the actual deployment stage [6]. Therefore, perfor-

mance evaluation is a crucial topic for blockchain and all

distributed ledger technologies (DLTs). The architecture of

blockchain systems is complex, and even its simpliﬁed abstrac-

tion consists of ﬁve layers [2], [7]: network layer, consensus

layer, data layer, execution layer, and application layer. The

complexity of such systems makes the performance evaluation

process challenging.

Here, the simulation of blockchain systems often is the only

alternative to evaluating the blockchain system’s performance

under different scenarios. Blockchain simulators attempt to

reproduce a real system’s performance and progress over time

by implementing and running a simulation model [8]. By

changing the simulated model variables and conditions, the

simulated system can be thoroughly investigated without the

need to implement an entire system [6]. Therefore, simulators

are valuable tools in the development of blockchain protocols

and systems.

Unfortunately, the design and development of blockchain

simulators are complex. Thus, most blockchain simulators are

developed to reproduce only one or several aspects of the

system realistically (e.g., mining and miner behavior, attacks

on the network, and data dissimulation). The implementation

of other processes (e.g., ﬁxing network delays, hashing power,

transaction propagation) is simpliﬁed, while other features

(e.g., Merkle trees, UTXO) may be skipped altogether. Con-

sequently, the selection of a suitable simulator may be a

challenging task. To address this, an overview and current

status of blockchain simulators have been performed during

this work.

The remainder of the paper is organized as follows. Sec-

tion II presents a more in-depth overview and comparison

of the identiﬁed blockchain simulators, while Section III

concludes this work.

II. OVE RVIEW OF IDENTIFIED BLOCKCHAIN SIMULATOR S

To identify the key publications on blockchain simulators,

we performed a literature search in scientiﬁc databases fol-

lowing PRISMA methodology [9]. Our performed analysis

covered leading publishers of computer science journals and

conferences, e.g., IEEE Xplore, WoS and Scopus databases.

To ﬁnd relevant publications for our research, we used

the following search string: ((DLT OR Blockchain OR

Bitcoin OR Ethereum OR Hyperledger OR DAG)

AND (Simulator)). All searches were conducted in

November 2020. At the end of this process, we identiﬁed 26

blockchain simulators and their extensions.

In Table I, we summarize the 26 identiﬁed blockchain

simulators. It can be observed that the ﬁrst blockchain sim-

ulators were presented in 2013. The majority of simulators

appeared in 2019 and 2020 (16 of 26). This shows that the

development/application of blockchain simulators has received

signiﬁcant attention only recently, and the ﬁeld requires much

more consideration from the research community. In the next

columns, we provide: supported blockchain platform or its

type, simulated layer(s), simulation model type, programming

language and framework (if any). The links to the source code

(if provided) are given in References.

TABLE I

OVERVIEW OF IDENTIFIED BLOCKCHAIN SIMULATORS AND THEIR EXTENSIONS

Simulator’s name, year and source Platform(s) Layer(s) Model type Language/Framework

“Bitcoin privacy simulator”a(2013) [10] Bitcoin data discrete-event C++

BTCsim (2013) [11] Bitcoin consensus, network discrete-event Python

Bitcoin protocol simulator (2014) [12] Bitcoin consensus discrete-event not speciﬁed

Simbit (2014) [13] Bitcoin consensus, network discrete-even Javascript

Shadow-Bitcoin (2015) [14] Bitcoin consensus, data, network discrete-even Python/Shadow

Bitcoin simulator (2016) [15] PoW blockchains consensus, network discrete-event C++/NS3

Bitcoin mining simulator (2017) [16] Bitcoin consensus not speciﬁed C++

VIBES (2017) [17] PoW blockchains consensus, data discrete-event Scala

eVIBES (2018) [18] Pow Ethereum consensus, data discrete-event Scala

CLoTH (2018) [19] Bitcoin network discrete-event Go

“Algorand simulator” (2019) [20] Algorand consensus not speciﬁed Java

Bitcoin network simulator (2019) [21] Bitcoin consensus discrete-event Python

Ext1-Bitcoin-simulatorb(2019) [22] PoW blockchains consensus, network discrete-event C++/NS3

BlockSim:Alharby (2019) [23] PoW blockchains consensus, data discrete-event Python

BlockSim:Faria (2019) [8] Bitcoin, Ethereum consensus, data, network discrete-event Python/SimPy

BlockSim:Hao (2019) [24] PoW blockchains network discrete-event Java/PeerSim

DAGsim (2019) [25] DAG consensus, network agent-based Python

“Mining strategy simulator” (2019) [26] Bitcoin consensus agent-based Python/Mesa

LUNES-Blockchain (2019) [27] Bitcoin consensus, network agent-based ART`

IS+GAIA

“Proof of Prestige Simulator” (2019) [28] Proof-of-Prestige consensus not described Python

SimBlock (2019) [29] PoW blockchains consensus, network discrete-event Java

Ext2-Bitcoin-simulator (2020) [30] Raft consensus, network discrete-event C++/NS3

I-Green simulator (2020) [31] PoW, PoS, PoGeneration application, consensus not speciﬁed Python

Local Bitcoin Network Simulator (2020) [32] Bitcoin network virtualization-based Python

Ext-BlockSim:Alharby (2020) [33] Bitcoin consensus, data, network discrete-event Python/SimPy

Ext-SimBlock (2020) [34] PoW blockchains consensus, network discrete-event Java

aThe simulators not named by their authors were given names in quotation marks.

bNames of the extended versions of simulators were given a preﬁx “Ext”.

Note that most of the existing simulators implement con-

sensus, data, and network layers as they are integral parts of

blockchain systems; however, some are usually very simpli-

ﬁed. Thus, in the column “Layer(s),” we stress only those lay-

ers with more sophisticated models that could be investigated

under various parameters and scenarios. The next observation

is that most of the simulators implement blockchain systems

based on the PoW-type consensus algorithms (21 out of 26)

and mainly focus on simulating the Bitcoin network. More-

over, blockchain simulators typically follow a discrete-event

system simulation, i.e., simulation at a discrete set of points in

time (18 out of 22 simulations specifying their modeling type),

while only three simulators are agent-based. The most often

utilized programming language is Python — 11 simulators are

built using it. The architecture of blockchain simulators usually

follows the multi-layered paradigm and consists of such layers

as consensus, data, network, and models describing the behav-

ior of layers, components/resources, and interactions between

them. Our preliminary investigations showed that simulators,

covering fewer layers, have more sophisticated models and

simulate them more realistically. Clearly, an in-depth compar-

ative experimental analysis to identify blockchain simulators’

main features, advantages, and limitations is still required.

III. CONCLUSIONS

In this paper, we identiﬁed that the simulation of

blockchains is still in the early stages, and the existing simu-

lators are very limited. Moreover, the most developed simula-

tors are devoted to simulate various PoW-based blockchains,

therefore, there is a need for simulators based on other

types of consensus algorithms, such as PoS or various BFT

variations [35]. Unfortunately, most of the existing simulators

are not actively developed and improved further. Finally, an in-

depth comparative analysis to identify blockchain simulators’

main features, advantages, and limitations is required.

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Blockchain-based decentralized cryptocurrencies have drawn much attention and been widely-deployed in recent years. Bitcoin, the first application of blockchain, achieves great success and promotes more development in this field. However, Bitcoin encounters performance problems of low throughput and high transaction latency. Other cryptocurrencies based on proof-of-work also inherit the flaws, leading to more concerns about the scalability of blockchain. This paper attempts to cover the existing scaling solutions for blockchain and classify them by level. In addition, we make comparisons between different methods and list some potential directions for solving the scalability problem of blockchain.

BlockSim: Blockchain Simulator

Conference Paper

Full-text available

Jul 2019

Local Bitcoin Network Simulator for Performance Evaluation Using Lightweight Virtualization

Conference Paper

Full-text available

Feb 2020

This paper presents a new blockchain network simulator that uses bitcoin's original reference implementation as its main application. The proposed simulator leverages the use of lightweight virtualization technology to build a fine tuned local testing network. To enable fast simulation of a large scale network without disabling mining service, the simulator can adjust the bitcoin mining difficulty level to below the default minimum value. In order to assess the performance of blockchain under different network conditions, the simulator allows to define different network topologies, and integrates Linux kernel traffic control (tc) tool to apply distinct delay or packet loss on the network nodes. Moreover, to validate the efficiency of our simulator we conduct a set of experiments and study the impact of the computation power and network delay on the network's consistency in terms of number of forks and mining revenues. The impact of applying different mining difficulty levels is also studied and the block time as well as fork occurrences are evaluated. Furthermore, a comprehensive survey and taxonomy of existing blockchain simulators are provided along with a discussion justifying the need of new simulator. As part of our contribution, we have made the simulator available on Github for the community to use and improve it.

Simulating Difficulty Adjustment in Blockchain with SimBlock

Conference Paper