A Variable Bulk Arrival and Static Bulk Service Queueing Model for Blockchain

Seol, Jongho; Kancharla, Abhilash; Ke, Zuqiang; Kim, Hye Young; Park, Nohpill

doi:10.1145/3384943.3409423

Cited by 7 publications

(5 citation statements)

References 20 publications

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“…This paper has presented a bivariate quantitative model to assess the performance of a NFT Chain with further extensive simulations and results. The quantitative model proposed in [17] was based on and extended from the baseline model [16] in which the performance takes into account only a single variable, i.e., the number of slots in transactions on chain only. The new model proposed in [17] in a form of an embedded Markovian queueing model, takes into account an additional variable which is the number of slots off chain by applying the equivalent temporal partitioning of transactions executed off chain for the modeling purpose.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

“…Particularly from the systems perspective of NFT chain, the following works also have been conducted [14,15,16] and reviewed in this section to provide preliminaries towards the work proposed in this paper.…”

Section: Preliminaries and Reviewmentioning

confidence: 99%

“…A novel embedded Markovian queueing-based model has been proposed to establish a theoretical foundation to model and design the performance a blockchain-based system with specific interest in Ethereum [16]. The model assumes a variable bulk arrival of transactions in Poisson distribution, and a static bulk service of transactions in exponential time for posting in the current block.…”

Section: Preliminaries and Reviewmentioning

confidence: 99%

“…Extensive numerical simulations have been conducted to validate the efficacy of the model. Note that the model in [16] demonstrates as the singlevariate version of the performance model for a baseline chain [16] which is extended to a bi-variate model for a NFT chain in this paper.…”

Section: Preliminaries and Reviewmentioning

confidence: 99%

“…The proposed quantitative model is based on and extended from the baseline model [16] in which the performance takes into account only a single variable, i.e., the number of slots (an evenly sized temporal partition in each transaction and each transaction is expected to be executed by taking positive number of slots that is less than or equal to the capacity of each block in the chain, n) in transactions on chain only. The new model in a form of an embedded Markovian queueing model, takes into account an additional variable which is the number of slots off chain by applying the equivalent temporal partitioning of transactions executed off chain for the modeling purpose.…”

Section: Proposed Nft Chain Modelmentioning

confidence: 99%

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A NFT (Non-Fungible Token) Chain Model and Performance Study

Seol

Kancharla

et al. 2023

Preprint

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This paper presents a quantitative model to assess the performance of a NFT (Non-Fungible Token)-centered chain as referred to as a NFT Chain in this paper. The model was introduced in [17] and more extensive simulations are conducted and the results are presented in this work. NFT chain in general stores its data distributed across on chain (e.g., NFT registration ledger data and an address pointing at the data located off chain such as meta data table and ultimate digital asset's data) due to the high cost to store the potentially high volume of data for digital assets. Therefore, it is expected that the overall performance of NFT chain is primarily to be dominated and bound by the off-chain performance. The proposed performance model employing an embedded Markovian queueing process model, tracks a bivariate state of the NFT chain such that \(\left(\varvec{i},\varvec{j}\right)\) where \(\varvec{i}\) stochastically tracks the number of slots of the transactions executed on chain and \(\varvec{j}\) stochastically tracks the number of transactions off chain as well, and the states transition as determined by \({\varvec{\lambda }}_{\varvec{o}\varvec{n}}\), \({\varvec{\lambda }}_{\varvec{o}\varvec{f}\varvec{f}}\), \(\varvec{\mu }\), and the number of slots in the current block. Extensive numerical simulations are performed to validate the efficacy of the model. The primary set of variables used in the simulations consists of \({\varvec{\lambda }}_{\varvec{o}\varvec{n}}\), \({\varvec{\lambda }}_{\varvec{o}\varvec{f}\varvec{f}}\), \(\varvec{\mu }\) and the average number of slots of the transactions during a block posting, \(\varvec{L}\), is simulated based on both \({\varvec{L}}_{\varvec{o}\varvec{n}}\)and \({\varvec{L}}_{\varvec{o}\varvec{f}\varvec{f}}\); and the average waiting time \(\varvec{W}\) based on both \({\varvec{W}}_{\varvec{o}\varvec{n}}\)and \({\varvec{W}}_{\varvec{o}\varvec{f}\varvec{f}}\), in an intermingled manner in order to take into account of the nature of NFT transactions executed across on- and off-chain without loss of generality. The simulation results in [17] has demonstrated a good agreement with the expected and intuitive trends. The results of more extensive simulations are presented in this paper to further demonstrate the efficacy and versatility of the proposed model. Ultimately, the proposed NFT chain model will serve as a sound theoretical foundation for the design of NFT chains from the performance's perspective.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Section: Preliminaries and Reviewmentioning

confidence: 99%

Section: Preliminaries and Reviewmentioning

confidence: 99%

Section: Preliminaries and Reviewmentioning

confidence: 99%

Section: Proposed Nft Chain Modelmentioning

confidence: 99%

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