Chapter 2
Move2Kube Architecture and Core Concepts
What makes Move2Kube adaptable to complex, real-world migrations? This chapter dissects the engine behind the automation, revealing how modular components, sophisticated pipelines, and extensibility intersect to transform diverse workloads for Kubernetes. From service discovery to hardened security foundations, you'll see what sets Move2Kube apart-and how its thoughtful architecture fosters confidence in even the most demanding migration scenarios.
2.1 Move2Kube System Architecture
Move2Kube's architecture is designed to facilitate the seamless transformation and migration of legacy applications into cloud-native environments, balancing automation with user-driven customization. The system is composed primarily of three core components: transformers, planners, and execution engines. These components interact through well-defined data flows and event hooks, coordinated to achieve scalable, reliable migration pipelines.
At the heart of the system lie the transformers, modular units responsible for converting application artifacts into cloud-native descriptors. Each transformer embodies a distinct transformation logic-for instance, container image extraction, configuration restructuring, or workload definition generation. Transformers are designed as stateless microservices, allowing parallel execution and on-demand scaling. Inputs to transformers are typically application source artifacts, intermediate transformation state, or metadata generated by preceding components. The output is a normalized intermediate representation (IR) that serves as a lingua franca facilitating further processing. The IR supports fine-grained extensibility by exposing transformation metadata and context, allowing downstream components to reconcile conflicting transformation outputs or merge parallel execution threads efficiently.
The planners serve as the strategic decision-making layer. Their role is to analyze the intermediate representation, assess target deployment environments, and generate detailed migration plans. These plans specify resource topology, service dependencies, network configurations, and scaling policies tailored to the target infrastructure (e.g., Kubernetes, OpenShift). Planners incorporate heuristics and policy-driven rule engines to balance optimization goals such as minimizing resource consumption, maximizing availability, and aligning with organizational compliance mandates. Architecturally, planners operate as orchestrators, invoking multiple sub-planner modules, each specialized in particular domains such as security hardening, cost optimization, or compliance verification. The planners persist their outputs as declarative manifests that serve as input for execution engines.
The execution engines are responsible for the actual deployment and lifecycle management of migrated applications in target environments. They consume planner-generated manifests and translate them into platform-specific API calls, managing cluster resources, configuring networking, and applying policies. Execution engines implement robust error-handling mechanisms and retries, ensuring idempotent operations that support failure recovery and progressive rollouts. They expose event hooks that integrate with monitoring and alerting systems, providing feedback loops to both planners and transformers for iterative refinement. These hooks also serve as extensibility points, enabling custom user scripts or third-party integrations to intervene at critical phases, thus maintaining user-driven control within an otherwise automated pipeline.
Data flows in Move2Kube systematically traverse these core components adhering to a unidirectional pipeline: initial input artifacts enter the transformer module, producing intermediate representations that are passed to planners. Planner outputs feed execution engines, which interact with external cloud environments. This pipeline is augmented by bi-directional control signals and event hooks. For example, execution engines emit deployment status events that trigger planner re-evaluations or transformer recalibrations in response to runtime anomalies or new constraints. This event-driven design introduces reactive maturity to the architecture, enabling dynamic adaptation without manual intervention.
Inter-process communication (IPC) within Move2Kube leverages asynchronous message passing over a resilient message bus, decoupling component lifecycles and enabling horizontal scalability. Communication payloads conform to well-defined protocol schemas, ensuring backward compatibility and seamless component evolution. Shared persistent storage stores intermediate states and audit logs, enabling reproducibility and post-mortem diagnostics. Critical metadata such as transformation context IDs, planning decisions, and execution outcomes are tracked meticulously to support traceability and audit trails.
Architectural trade-offs in Move2Kube's design address the tension between automation and customization. On one hand, the stateless microservice design and asynchronous communication maximize automation efficiency and scalability, allowing pipelines to process large-scale migrations with minimal human intervention. On the other hand, rich event hooks and modular plugin interfaces empower users to customize transformation heuristics, planning policies, and execution strategies. This extensibility is achieved without compromising the system's modularity or introducing tight coupling, preserving maintainability and stability. By adopting a layered approach-transformers performing fine-grained transformations, planners synthesizing macro-level decisions, and execution engines managing deployment state-the architecture encapsulates complexity at appropriate abstraction boundaries, facilitating focused enhancements or user extensions at each layer.
Move2Kube's system architecture orchestrates its core components through a carefully engineered pipeline of transformations, planning, and execution. This architecture supports scalable and reliable application migrations while offering flexible customization interfaces. The interplay of asynchronous data flows, event-driven controls, and modular design patterns enables the system to address diverse migration scenarios, balancing efficiency, robustness, and user empowerment within a coherent, maintainable framework.
2.2 Service Discovery and Analysis
Service discovery in Move2Kube constitutes a critical foundational step, enabling precise characterization of complex application landscapes prior to their migration or transformation. The process is rooted in a systematic static analysis that dissects the input artifacts-source code, deployment descriptors, and runtime configurations-to construct an accurate and comprehensive model of constituent services and their interactions. This section elucidates the core methodologies and heuristics employed in revealing underlying platforms and building dependency graphs essential for subsequent transformation phases.
The initial phase of service discovery involves parsing the heterogeneous input landscape for recognizable patterns indicative of diverse deployment paradigms, including legacy virtual machines, container orchestrations, and Platform-as-a-Service (PaaS) frameworks. Move2Kube leverages a rule-based static analysis engine augmented by domain-specific heuristics to identify service boundaries and execution environments. For legacy VMs, the detection process examines configuration files such as systemd service scripts, init.d scripts, and package manifests to infer installed middleware and service lifecycles. In contrast, containerized applications are identified through Docker Compose YAML files, Kubernetes manifests, or Cloud Foundry manifest files, each presenting unique syntactic and semantic structures that the analysis engine must decode with precision.
The static analysis harnesses abstract syntax trees (ASTs), regular expressions, and specialized parsers tailored for supported artifact types. For instance, Docker Compose files are parsed to extract service definitions, environment variables, volumes, port mappings, and inter-service dependencies declared via depends_on attributes. The heuristics extend beyond syntactical identification by applying semantic rules; for example, a container exposing specific ports combined with health check directives reveals service availability constraints and potential communication endpoints. Similarly, Cloud Foundry manifests are analyzed to extract application names, buildpacks, service bindings, and environment-specific configurations, which inform the service interaction graph.
To distinguish overlapping or nested platforms, the heuristics prioritize evidence strength and context. An input directory containing Dockerfiles alongside Cloud Foundry manifests is subjected to a confidence scoring mechanism that weighs file presence, naming conventions, and explicit platform indicators like labels or annotations. These confidence scores inform decision logic that assigns dominant platform identities, crucial for accurate platform-aware transformations.
Subsequent to platform recognition, Move2Kube constructs inter-service dependency graphs that map explicit and ...
