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Chapters/Methodology.tex
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Chapters/Methodology.tex
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% Chapter Template
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\chapter{Methodology} % Main chapter title
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\label{Methodology} % Change X to a consecutive number; for
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% referencing this chapter elsewhere, use \ref{ChapterX}
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%----------------------------------------------------------------------------------------
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% SECTION 1
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%----------------------------------------------------------------------------------------
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This chapter describes the methodology used to evaluate and analyze
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the Clan framework. A summary of the logical flow of this research is
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depicted in Figure \ref{fig:clan_thesis_argumentation_tree}.
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\begin{figure}[H]
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\centering
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\includesvg[width=1\textwidth,
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keepaspectratio]{Figures/clan_thesis_argumentation_tree.drawio.svg}
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\caption{Argumentation Tree for the Clan Thesis}
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\label{fig:clan_thesis_argumentation_tree}
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\end{figure}
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The structure of this study adopts a multi-faceted approach,
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addressing several interrelated challenges in enhancing the
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reliability and manageability of \ac{P2P} networks.
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The primary objective is to assess how the Clan framework effectively
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addresses these challenges.
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The research methodology consists of two main components:
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\begin{enumerate}
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\item \textbf{Development of a Theoretical Model} \\
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A theoretical model of the Clan framework will be constructed.
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This includes a formal specification of the system's foundational
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axioms, outlining the principles and properties that guide its
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design. From these axioms, key theorems will be derived, along
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with their boundary conditions. The aim is to understand the
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mechanisms underpinning the framework and establish a basis for
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its evaluation.
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\item \textbf{Empirical Validation of the Theoretical Model} \\
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Practical experiments will be conducted to validate the
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predictions of the theoretical model. These experiments will
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evaluate how well the model aligns with observed performance in
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real-world settings. This step is crucial to identifying the
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model’s strengths and limitations.
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\end{enumerate}
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The methodology will particularly examine three core components of
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the Clan framework:
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\begin{itemize}
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\item \textbf{Clan Deployment System} \\
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The deployment system is the core of the Clan framework, enabling
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the configuration and management of distributed software
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components. It simplifies complex configurations through Python
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code, which abstracts the intricacies of the Nix language.
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Central to this system is the "inventory," a mergeable data
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structure designed for ensuring consistent service configurations
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across nodes without conflicts. This component will be analyzed
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for its design, functionality, efficiency, scalability, and fault
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resilience.
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\item \textbf{Overlay Networks / Mesh VPNs} \\
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Overlay networks, also known as "Mesh VPNs," are critical for
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secure communication in Clan’s \ac{P2P} deployment. The study
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will evaluate their performance in terms of security,
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scalability, and resilience to network disruptions. Specifically,
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the assessment will include how well these networks handle
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traffic in environments where no device has a public IP address,
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as well as the impact of node failures on overall
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connectivity. The analysis will focus on:
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\begin{itemize}
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\item \textbf{ZeroTier}: A globally distributed "Ethernet Switch".
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\item \textbf{Mycelium}: An end-to-end encrypted IPv6 overlay network.
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\item \textbf{Hyprspace}: A lightweight VPN leveraging IPFS and libp2p.
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\end{itemize}
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\item \textbf{Data Mesher} \\
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The Data Mesher is responsible for data synchronization across
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nodes, ensuring eventual consistency in Clan’s decentralized network. This
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component will be evaluated for synchronization speed, fault
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tolerance, and conflict resolution mechanisms. Additionally, it
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will be analyzed for its resilience in scenarios involving
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malicious nodes, measuring how effectively it prevents and
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mitigates manipulation or integrity violations during data
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replication and distribution.
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\end{itemize}
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\section{Related Work}
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The Clan framework operates within the realm of software deployment
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and peer-to-peer networking,
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necessitating a deep understanding of existing methodologies in these
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areas to tackle contemporary challenges.
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This section will discuss related works encompassing system
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deployment, peer data management,
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and low maintenance structured peer-to-peer overlays, which inform
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the development and positioning of the Clan framework.
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\subsection{Nix: A Safe and Policy-Free System for Software Deployment}
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Nix addresses significant issues in software deployment by utilizing
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a technique that employs cryptographic
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hashes to ensure unique paths for component instances \cite{dolstra_nix_2004}.
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The system is distinguished by its features, such as concurrent
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installation of multiple versions and variants,
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atomic upgrades, and safe garbage collection.
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These capabilities lead to a flexible deployment system that
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harmonizes source and binary deployments.
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Nix conceptualizes deployment without imposing rigid policies,
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thereby offering adaptable strategies for component management.
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This contrasts with many prevailing systems that are constrained by
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policy-specific designs,
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making Nix an easily extensible, safe and versatile deployment solution
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for configuration files and software.
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As Clan makes extensive use of Nix for deployment, understanding the
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foundations and principles of Nix is crucial for evaluating inner workings.
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\subsection{NixOS: A Purely Functional Linux Distribution}
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NixOS is an extension of the principles established by Nix,
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presenting a Linux distribution that manages system configurations
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using purely functional methods \cite{dolstra_nixos_2008}. This model
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ensures that system
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configurations are reproducible and isolated
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from stateful interactions typical in imperative models of package management.
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Because NixOS configurations are built by pure functions, they can overcome the
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challenges of easily rolling back changes, deploying multiple package versions
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side-by-side, and achieving deterministic configuration reproduction .
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The solution is particularly compelling in environments necessitating rigorous
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reproducibility and minimal configuration drift—a valuable feature
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for distributed networks .
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Clan also leverages NixOS for system configuration and deployment,
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making it essential to understand how NixOS's functional model works.
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\subsection{Disnix: A Toolset for Distributed Deployment}
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Disnix extends the Nix philosophy to the challenge of distributed
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deployment, offering a toolset that enables system administrators and
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developers to perform automatic deployment of service-oriented
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systems across a network of machines \cite{van_der_burg_disnix_2014}.
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Disnix leverages the features of Nix to manage complex intra-dependencies.
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Meaning dependencies that exist on a network level instead on a binary levle.
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The overlap with the Clan framework is evident in the focus on deployment, how
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they differ will be explored in the evaluation of Clan's deployment system.
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\subsection{State of the Art in Software Defined Networking}
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The work by Bakhshi \cite{bakhshi_state_2017} surveys the
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foundational principles and recent developments in Software Defined
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Networking (SDN). It describes SDN as a paradigm that separates the
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control plane from the data plane, enabling centralized, programmable
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control over network behavior. The paper focuses on the architectural
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components of SDN, including the three-layer abstraction model—the
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application layer, control layer, and data layer—and highlights the
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role of SDN controllers such as OpenDaylight, Floodlight, and Ryu.
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A key contribution of the paper is its identification of challenges
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and open research questions in SDN. These include issues related to
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scalability, fault tolerance, and the security risks introduced by
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centralized control.
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This work is relevant to evaluating Clan’s role as a
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Software Defined Network deployment tool and as a
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comparison point against the state of the art.
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\subsection{Low Maintenance Peer-to-Peer Overlays}
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Structured Peer-to-Peer (P2P) overlay networks offer scalability and
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efficiency but often require significant maintenance to handle
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challenges such as peer churn and mismatched logical and physical
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topologies. Shukla et al. propose a novel approach to designing
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Distributed Hash Table (DHT)-based P2P overlays by integrating
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Software Defined Networks (SDNs) to dynamically adjust
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application-specific network policies and rules
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\cite{shukla_towards_2021}. This method reduces maintenance overhead
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by aligning overlay topology with the underlying physical network,
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thus improving performance and reducing communication costs.
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The relevance of this work to Clan lies in its addressing of
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operational complexity in managing P2P networks.
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\subsection{Full-Mesh VPN Performance Evaluation}
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The work by Kjorveziroski et al. \cite{kjorveziroski_full-mesh_2024}
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provides a comprehensive evaluation of full-mesh VPN solutions,
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specifically focusing on their use as underlay networks for
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distributed systems, such as Kubernetes clusters. Their benchmarks
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analyze the performance of VPNs with built-in NAT traversal
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capabilities, including ZeroTier, emphasizing throughput, reliability
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under packet loss, and behavior when relay mechanisms are used. For
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the Clan framework, these insights are particularly relevant in
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assessing the performance and scalability of its Overlay Networks
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component. By benchmarking ZeroTier alongside its peers, the paper
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offers an established reference point for evaluating how Mesh VPN
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solutions like ZeroTier perform under conditions similar to the
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intricacies of peer-to-peer systems managed by Clan.
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\subsection{AMC: Towards Trustworthy and Explorable CRDT Applications}
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Jeffery and Mortier \cite{jeffery_amc_2023} present the Automerge
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Model Checker (AMC), a tool aimed at verifying and dynamically
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exploring the correctness of applications built on Conflict-Free
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Replicated Data Types (CRDTs). Their work addresses critical
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challenges associated with implementing and optimizing
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operation-based (op-based) CRDTs, particularly emphasizing how these
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optimizations can inadvertently introduce subtle bugs in distributed
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systems despite rigorous testing methods like fuzz testing. As part
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of their contributions, they implemented the "Automerge" library in
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Rust, an op-based CRDT framework that exposes a JSON-like API and
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supports local-first and asynchronous collaborative operations.
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This paper is particularly relevant to the development and evaluation
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of the Data Mesher component of the Clan framework, which utilizes
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state-based (or value-based) CRDTs for synchronizing distributed data
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across peer-to-peer nodes. While Automerge addresses issues pertinent
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to op-based CRDTs, the discussion on verification techniques, edge
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case handling, and model-checking methodologies provides
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cross-cutting insights to the complexities of ops based CRDTs and is
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a good argument for using simpler state based CRDTs.
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\subsection{Keep CALM and CRDT On}
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The work by Laddad et al. \cite{laddad_keep_2022} complements and
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expands upon concepts presented in the AMC paper. By revisiting the
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foundations of CRDTs, the authors address limitations related to
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reliance on eventual consistency and propose techniques to
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distinguish between safe and unsafe queries using monotonicity
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results derived from the CALM Theorem. This inquiry is highly
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relevant for the Data Mesher component of Clan, as it delves into
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operational and observable consistency guarantees that can optimize
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both efficiency and safety in distributed query execution.
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Specifically, the insights on query models and coordination-free
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approaches advance the understanding of how CRDT-based systems, like
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the Data Mesher, manage distributed state effectively without
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compromising safety guarantees.
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