Annotated Bibliography: Blockchain network simulations

This article was first published on Dr. Craig Wright’s blog, and we republished with permission from the author.

[This blog post has been published by Dr. Craig Wright’s editor on behalf of Dr. Wright.]

The annotated bibliography entries provide a comprehensive understanding of the use of experimental simulations, specifically involving AWS EC2 nodes, for measuring network performance and economically modeling the cost of deployment in blockchain networks. Foundational knowledge about Amazon EC2 (NASDAQ: AMZN) instance types and their use cases aids in designing the simulated network environment, while performance analysis methodologies, economic considerations, and platform comparisons from different sources guide the experimental setup, decision-making processes, and optimization of blockchain systems. The insights provided by the authors contribute to a thorough assessment of network performance, scalability issues, and economic feasibility, ultimately driving the advancement and efficiency of blockchain networks.

Annotated Bibliography: Blockchain network simulations

The use of experimental simulations, specifically AWS EC2 nodes, for measuring network performance and economically modeling the cost of deployment of nodes and infrastructure in blockchain networks is a critical aspect of the research discussed in the annotated bibliography entries. The foundational knowledge AWS (n.d.) provides about Amazon EC2 instance types and their use cases are crucial for setting up a simulated network environment suitable for testing. It is a basis for designing and deploying the network simulation on AWS EC2 nodes.

Dancheva et al. (2023) offer a systematic approach to performance analysis of HPC applications in Amazon EC2, including factors such as CPU performance, memory bandwidth, inter-node latency, and disk IO operations. The insights regarding the economic implications of deploying applications on EC2 and their testing methodology guide designing the experimental simulations and assessing EC2 performance under varying loads.

Raj and Deka (2018) systematically analyze blockchain technology, including a comparative analysis of platforms, scalability limitations, and economic considerations. Their insights on selecting tools for use cases and the economic implications of deployment aid in designing the study, guiding decision-making processes, and understanding the economic feasibility of the deployment.

Shudo et al. (2023) contribute to the field by leveraging experimental simulations using SimBlock and AWS EC2 nodes to evaluate network performance and economically model the cost of deploying blockchain nodes and infrastructure. Their work emphasizes the significance of accurate performance evaluation and cost estimation in ensuring the efficiency and feasibility of blockchain networks, providing valuable insights into network performance evaluation and cost analysis.

Yuan et al. (2021) introduce the CoopEdge platform, focusing on cooperative edge computing within blockchain networks. Their exploration of network latency challenges and performance evaluation of CoopEdge offer practical insights into transaction management and scalability assessment, informing the design and execution of the study.

These works provide a thorough understanding of the use of experimental simulations, particularly AWS EC2 nodes, in measuring network performance and economically modeling the cost of deployment in blockchain networks. The various sources contribute foundational knowledge, performance analysis methodologies, platform comparisons, and insights into economic implications, enabling a rigorous and thorough assessment of the experimental setup and optimization of blockchain systems.

Annotated Bibliography

AWS. (n.d.). Compute – Amazon EC2 Instance Types – AWS. Amazon Web Services, Inc. Retrieved 16 July 2023, from https://aws.amazon.com/ec2/instance-types/

AWS (n.d.) offers a primary resource providing comprehensive details about Amazon’s Elastic Compute Cloud (EC2) instances, essentially virtual servers for scalable computing. It explains various instances available for setup and their distinctive use cases. This is foundational knowledge for creating a simulated network environment suitable for testing. This knowledge is crucial for this research as it provides the basis for designing and deploying the network simulation on AWS EC2 nodes.

Dancheva, T., Alonso, U., & Barton, M. (2023). Cloud benchmarking and performance analysis of an HPC application in Amazon EC2. Cluster Computing.
https://doi.org/10.1007/s10586-023-04060-4

Dancheva et al. (2023) examine the performance analysis of High-Performance Computing (HPC) applications within Amazon’s EC2 environment. In their study, the authors benchmark different instances, making this an indispensable source for the current study, which also employs Amazon’s EC2 for setting up a network of Bitcoin nodes.

The paper offers a systematic approach to performance analysis, examining factors such as CPU performance, memory bandwidth, inter-node latency, and disk IO operations in the cloud environment. Analyzing different instance types benefits this study by providing an idea of which EC2 instances will deliver optimal results for the existing and particular use case.

One of the strengths of Dancheva et al. (2023) is their investigation of the economic implications of deploying HPC applications on Amazon EC2. Their cost-benefit analysis proved to be a valuable resource in predicting the potential financial requirements and constraints of the study, which aims to measure network performance while also considering the economic model of the deployment of nodes and infrastructure.

Moreover, the paper’s approach to testing and comparing different instance types provided a framework that helped us design the experimental simulations in the research. By applying their methodology to the experiment, we can ensure a rigorous and thorough assessment of the EC2 setup and its performance under varying loads.

Finally, Dancheva et al. (2023) explore the challenges and limitations associated with using EC2 for HPC applications is an important factor to consider. This understanding aids in anticipating potential roadblocks and implementing preventative measures during the experimental setup in the research. The paper holds detailed information on the development and execution of components analogous to those planned for the current study. The cloud benchmarking and performance analysis insights have provided theoretical knowledge and practical advice in setting up and operating a similar EC2 environment.

Raj, P., & Deka, G. C. (2018). Blockchain technology: Platforms, tools and use cases. Academic Press.

Raj and Deka (2018) offer a comprehensive reference and guide for blockchain technology, providing insightful analysis of various platforms and tools available and detailing their potential use cases. The book provides a detailed and layered understanding of blockchain technology, from structural design to functional mechanisms, making it an invaluable resource for researchers and practitioners.

While many aspects are incorrect, the text focuses on its comparative analysis of various blockchain platforms. It breaks down their design components, features, strengths and weaknesses, thoroughly understanding their working and potential applications. The discussions around scalability limitations and potential solutions across these platforms are particularly insightful. In the context of research on Bitcoin scalability, these discussions served as the foundation for understanding the underlying issues and potential resolutions.

Raj and Deka (2018) also offer details on selecting the right tools for particular use cases, providing a practical guide for developers or researchers. These guidelines can aid in designing the study and informing the decision-making process for setting up the scalability testing environment. The detailed suggestions and insight helped guide the design and implementation of the experimental setup using AWS EC2 instances.

Regarding the practical application of the book’s content, the insights offered on the economic aspects of blockchain technology were valuable. Raj and Deka discuss the economic implications of deploying nodes and blockchain infrastructure, which helps frame the research within the broader context of economic feasibility.

Despite the errors, this is a well-rounded analysis of blockchain technology and its application. The wide-ranging approach, combining technical details, comparative analysis, and economic considerations, makes the book an invaluable resource for researchers investigating the scalability issues of Bitcoin’s blockchain. It provides both a theoretical framework and practical guidelines, driving the design and implementation of the research study.

Shudo, K., Hasegawa, T., Sakurai, A., & Banno, R. (2023). Blockchain Network Studies Enabled by SimBlock. 2023 IEEE International Conference on Blockchain and Cryptocurrency (ICBC), 1–2. https://doi.org/10.1109/ICBC56567.2023.10174929

Shudo et al. (2023) explore using experimental simulations to study blockchain networks, specifically focusing on their SimBlock framework. The authors discuss the importance of network performance evaluation and cost analysis in deploying nodes and infrastructure for blockchain systems.

The main objective of the study is to leverage experimental simulations, specifically using AWS EC2 nodes, to measure the network performance of blockchain systems and to economically model the cost of deploying nodes and infrastructure. The authors highlight the significance of accurate performance evaluation and cost estimation in ensuring the efficiency and feasibility of blockchain networks.

SimBlock, the framework developed by the authors, enables the simulation of blockchain network behavior and performance in a controlled environment. By utilizing AWS EC2 nodes, the authors can accurately replicate real-world scenarios and study the impact of different network parameters on performance and cost.

The experimental simulations using SimBlock allow the authors to evaluate various network metrics such as latency, throughput, and scalability. These metrics are essential in understanding the performance limitations and bottlenecks of blockchain systems. By quantifying the network performance, the authors can identify potential issues and propose optimizations to enhance the overall efficiency of the network.

Furthermore, the economic modeling aspect of the study is crucial in assessing the cost implications of deploying blockchain nodes and infrastructure. The authors can provide valuable insights into the economic feasibility of deploying blockchain networks in various scenarios by analyzing the costs associated with different configurations and setups. This analysis helps stakeholders make informed decisions regarding resource allocation and budget planning.

The paper contributes to the field of blockchain research by providing a framework for network performance evaluation and cost modeling. Using experimental simulations, specifically leveraging AWS EC2 nodes, allows for accurate measurements and economic analysis, which can greatly assist in designing and deploying blockchain networks. Shudo et al. (2023) demonstrate the value of experimental simulations in studying blockchain networks. Their work with SimBlock and using AWS EC2 nodes provide valuable insights into network performance evaluation and cost analysis, ultimately contributing to advancing and optimizing blockchain systems.

Yuan, L., He, Q., Tan, S., Li, B., Yu, J., Chen, F., Jin, H., & Yang, Y. (2021). CoopEdge: A Decentralized Blockchain-based Platform for Cooperative Edge Computing. Proceedings of the Web Conference 2021, 2245–2257. https://doi.org/10.1145/3442381.3449994

Yuan et al. (2021) introduce the CoopEdge platform, an innovative decentralized blockchain platform specifically designed for cooperative edge computing. The authors extensively delve into the inherent challenges of employing edge computing within the context of blockchain technology, critically examining the problem of network latency, a crucial issue in the successful scaling of blockchain networks. They further detail the integral design principles underpinning the CoopEdge platform, providing insights into how these design components can facilitate more efficient and effective blockchain network operations.

Moreover, the paper embarks on an in-depth exploration of the platform performance, presenting valuable data on the behavior of CoopEdge under various conditions. The study investigates key aspects such as load balancing and resource allocation, critical considerations for the project’s scalability assessment. It provides rigorous experimental evidence demonstrating the effectiveness of their approach in addressing these challenges.

The methodologies and results presented in this article offer insightful guidance on designing and managing transactions within the planned simulated blockchain network on AWS EC2. Furthermore, it contributes to the broader discourse on blockchain scalability, presenting practical solutions and sparking thought-provoking discussions on integrating edge computing and blockchain. 

Consequently, this source constitutes a valuable reference point in the development and execution of the study, informing the approach to network design, transaction management, and scalability assessment.

[This blog post has been published by Dr. Craig Wright’s editor on behalf of Dr. Wright.]

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