Report on the energy consumption of the GoShimmer network

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Report on the energy consumption of the IOTA 2.0

prototype network (GoShimmer 0.8.3) under different

testing scenarios

Louis Helmer, Andreas Penzkofer

IOTA Foundation

Pappelallee 78/79, 10437 Berlin, Germany

louis.helmer@iota.org

andreas.penzkofer@iota.org

Abstract

The high energy consumption of proof of work-based distributed ledgers has become an important environmental

concern. Bitcoin, for example, consumes as much energy in a year as a developed country. Alternative consensus

mechanisms, such as proof of stake, have been shown to use drastically less energy than proof of work-based DLTs.

For example, the IOTA DLT, built upon a directed acyclic graph (DAG) architecture, uses an alternative consensus

mechanism that requires significantly less energy than other DLTs. Because the (DLT) space is constantly and rapidly

evolving, the question of how much energy DLTs actually consume demands to be continuously studied and

answered. Studying the energy consumption of alternative DLTs is important as it contributes to improving the

understanding of the general public that not all cryptocurrencies use excessive energy resources. Previous research

into the energy consumption of the IOTA network has shown that an optimization in the overall protocol correlates to

an optimization in energy consumption. The planned IOTA 2.0 update, built upon the GoShimmer research prototype,

promises to further optimize the protocol by removing the network's centralized Coordinator. This report presents the

results of measuring the energy consumption of a private GoShimmer network while comparing these findings to

previous research into the current mainnet, which is called Chrysalis. The main findings of this report are that the

IOTA 2.0 research prototype shows both improvements and increase in the energy consumption metrics compared to

the Chrysalis network. Additionally, this report defines a model to estimate the total annual energy consumption of an

IOTA network. This model should be significant for future research as it enables a way to estimate the total cost of

running the IOTA network as well as its carbon emissions. Moreover, having an annual power consumption metric

allows for better objective comparisons to different DLTs.

Table of contents

Abstract 1

Table of contents 2

1. Introduction 4

2. Literature review 5

3. Methodology 6

3.1. GoShimmer Private Network 6

3.2. Measurement 6

3.3. Measurement accuracy 7

3.4. Hardware 8

3.4.1. Setup diagram 8

3.4.2. Measurement device - TC6CC 8

3.5. Software 9

3.5.1. Node software 9

3.5.2. Docker Private Network 9

3.5.3. Spamming tool 12

3.5.4. Raspberry Pi OS 12

3.5.5. Golang-Go 12

3.5.6. Docker Engine 12

3.5.7. Docker Compose 12

4. Results 13

4.1. Unit conversion table 13

4.2. Trials and measurement duration 13

4.3. Power consumption results 14

4.4. Energy per message consumption results 16

4.5. Measurement results 17

4.5.1. Reference 17

4.5.2. Resting 18

4.5.3. 50mps 18

4.5.4. 100mps 18

4.5.5. 200mps 19

4.6. Data Normalization 20

4.6.1. Scenario consumption per GoShimmer node - Reference 20

4.6.2. Scenario consumption per GoShimmer node - Resting and Reference 20

4.7. Energy consumption per message 20

4.7.1. Calculating the energy consumption per message 22

4.8. Relative Analysis and Comparison 22

4.8.1. Energy consumption per message - comparison 22

4.8.2. Power consumption per node - comparison 23

5. Model for estimating the total annual energy consumption of an IOTA network 24

5.1. Background 24

5.2. The model 25

5.3. Best estimate “T.A.E.C.” model for a data-only IOTA network 27

5.4. Example - hypothetical network calculation 28

5.5. Applying the model to Chrysalis 30

6. Conclusion 31

7. Limitations 32

7.1. Accuracy and hardware 32

7.2. Chrysalis energy benchmarking 32

7.3. Annual power consumption model 32

8. Outlook and future research 33

9. Acknowledgements 34

10. References 34

1. Introduction

The urgency of climate change causes us to reevaluate the energy consumption and efficiency of the products and

services that underpin our daily lives. One example is the growing criticism of the energy consumed by proof of work

(PoW)-based distributed ledger technology (DLT) projects, including Bitcoin1and (with the increased use of

non-fungible tokens, or NFTs2) Ethereum3. It is appropriate, therefore, that cryptocurrencies should also be included

in the energy consumption debate, especially if they aim to play a significant role in the future global monetary

infrastructure.

Research from Alex de Vries for the Digiconomist Platform estimates that the Bitcoin network currently consumes

around 204.50 TWh annually4. This can be compared to the total energy consumption of the country of South Africa,

which consumes an estimated 202 TWh annually5. However, even though these figures are alarming, one should

interpret this comparison as a means to make the energy consumption figures of Bitcoin tangible to the average

person who is not familiar with electricity measurements. The limitation of this comparison is that one compares

apples and oranges. Nevertheless, the question arises: why does Bitcoin as a monetary system need to consume

such a large amount of energy? The answer: In order for Bitcoin nodes to add new transactions to the ledger, they

must solve a cryptographic puzzle that requires a vast amount of computing resources. However, other consensus

mechanisms, such as proof of stake (PoS), have shown that consensus can be achieved without the immense

energy requirements by at least three orders of magnitude6.

The IOTA 2.0 protocol introduces several new concepts, such as a novel consensus mechanism7,8, and a new type of

access control algorithm9. Through the latter, the protocol aims to address (together with other challenges) the energy

consumption inefficiency by removing PoW. The lightweight design allows for low computational demand, meaning

that it can run on low-powered devices (Raspberry Pis, for example). Many of the design decisions were influenced

by IOTA’s vision of enabling the software to run on a wide spectrum of IoT devices.

Previous research into the energy consumption of the current IOTA main network (called “Chrysalis”) was published

on May 14, 202110. However, to protect the network against attacks, the Chrysalis network still uses a centralized

node run by the IOTA Foundation (called “Coordinator”) and uses a small PoW requirement as spam protection.

While the Chrysalis network is already considered green in comparison with other DLTs (for the energy consumption

of one Bitcoin transaction, one billion IOTA transactions can be sent)11, the PoW still contributes significantly to the

overall energy consumption of running the Chrysalis network. With the launch of the IOTA 2.0 prototype on June 2nd,

2021, a prototype software (called GoShimmer) was released and operated in a public testnet12, in which the

12 https://blog.iota.org/iotav2devnet/

11 https://blog.iota.org/an-intro-to-the-iota-ecosystem/

10 https://blog.iota.org/internal-energy-benchmarks-for-iota/

9Cullen, A., Ferraro, P., Sanders, W., Vigneri, L., & Shorten, R. (2021, July 14). Access control for distributed ledgers in the

internet of things: A networking approach. Retrieved February 07, 2022, from https://arxiv.org/abs/2005.07778

8https://blog.iota.org/iota-2-0-details-on-current-status-and-outlook/

7https://blog.iota.org/improvements-to-the-iota-2-0-consensus-mechanism/

6Platt, M., Sedlmeier, J., et. al. (2021). Energy Footprint of Blockchain Consensus Mechanisms Beyond Proof-of-Work.

Discussion Paper Series, from https://arxiv.org/abs/2109.03667

5U.S. Energy Information Administration, Retrieved January 15, 2022 from

https://www.eia.gov/international/data/world/electricity/electricity-consumption

4de Vries, A. (2014). Bitcoin Energy Consumption index. Digiconomist. Retrieved May 22, 2022, from

https://digiconomist.net/ethereum-energy-consumption

3NFTexplained.info. (2022, January 09). “How Much Energy Does An NFT Use?”, from

https://nftexplained.info/how-much-energy-does-an-nft-use/

2https://dappradar.com/ethereum/marketplaces/opensea, retrieved January 25, 2022

1de Vries, A., Gallersdörfer, U., Klaaßen, L., Stoll, C. (2022). Revisiting Bitcoin's carbon footprint. Joule, Volume 6, Issue 3,

2022, from https://www.sciencedirect.com/science/article/abs/pii/S2542435122000861

Coordinator is removed. Although PoW is still employed in the current version to protect the network against spam

attacks, a novel access control is being implemented, which allows it to phase out PoW. In the future, the Chrysalis

network will be replaced by the Coordinator-free IOTA 2.0 network. With a new consensus mechanism, access

control and improved network architecture, an additional reduction in the energy consumption of the IOTA 2.0

network in comparison to the Chrysalis network is expected.

The energy consumption of the GoShimmer network will be measured by running a private network of three nodes on

a Raspberry Pi 4B. Measurements will be conducted under different testing scenarios. For the purpose of this study

and to align this study closer to the case where the IOTA Congestion Control Algorithm (ICCA)13, which controls the

access, is fully implemented, we set the PoW in our experiment to a low value in the prototype.

The objective of this report is to study the energy consumption required to process a single message14 and to

determine the power consumption per GoShimmer node. As testing was done on the prototype network (0.8.3), the

values presented in this report might change in the future based on improvements made to the 2.0 protocol. Future

updates to this report will be published.

2. Literature review

As aforementioned, previous research into the energy consumption of the Chrysalis network has been published on

May 14, 2021. The energy consumption analysis of the Chrysalis network was made in comparison to the legacy

IOTA 1.0 network. This was done because the second phase of the Chrysalis upgrade introduced many performance

improvements to the IOTA protocol. These changes reduced much of the computational load that nodes need to carry

in order to run the network. The previous measurements were done in a private Chrysalis network, with the

Coordinator node being deployed on a laptop, while two nodes were run on one Raspberry Pi 3B+ and one

Raspberry Pi 4. A custom breadboard circuit was set up, with which the power consumption of each Raspberry Pi

was measured with a Texas Instruments INA219 and leveraged with a breakout board by Adafruit. There were four

tests. The first measured the energy consumption per transaction while spamming 50tps with remote PoW (done by

the Coordinator node), the second spammed 100tps with remote PoW, the third spammed 0.0730 transactions per

second while doing local PoW and the last measured the energy consumption of running a node at no spam (0tps).

The reduction in energy consumption due to the Chrysalis upgrade (in different testing scenarios) is between 33%

and 95%.

The author of the initial benchmarking remarks that one should resist the urge to make any assumptions regarding

the linearity of the energy consumption/transaction. This is illustrated by the fact that the energy consumption per

transaction of the 50 tps spam rate is higher than the energy consumption per transaction of the 100 tps spam rate.

The author explains that this is due to transactions being issued much faster at a higher spam rate and, since energy

is equal to power multiplied by time, less time issuing transactions results in less energy spent.

This report aims to expand upon the initial research made on the Chrysalis network and apply a similar testing set-up

to determine the energy consumption metrics for GoShimmer.

14 A message is a data transaction

13 Cullen, A., Ferraro, P., Sanders, W., Vigneri, L., & Shorten, R. (2021, July 14). Access control for distributed ledgers in the

internet of things: A networking approach. Retrieved February 07, 2022, from https://arxiv.org/abs/2005.07778

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ReportontheenergyconsumptionoftheIOTA2.0prototypenetwork(GoShimmer0.8.3)underdifferenttestingscenariosLouisHelmer,AndreasPenzkoferIOTAFoundationPappelallee78/79,10437Berlin,Germanylouis.helmer@iota.organdreas.penzkofer@iota.orgAbstractThehighenergyconsumptionofproofofwork-baseddistributedledgershasb...

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