Description:FIELD OF THE INVENTION
[0001] The present invention relates to cloud migration assessment within IT (Information Technology) infrastructure management. More specifically, the present invention provides a cloud-based platform and a method that enables assessment across diverse computing environments through agentless metadata retrieval and unified analysis.
BACKGROUND FOR THE INVENTION:
[0002] A cloud platform refers to a scalable, network-accessible computing infrastructure that integrates both hardware and software components to deliver distributed services over the internet or private networks. At the hardware level, the cloud platform comprises interconnected computing devices equipped with processors, memory units, storage systems, and network interfaces, which may be deployed across data centres, virtualized environments, or edge nodes. These devices operate in coordination to support high availability, fault tolerance, and elastic resource provisioning. Similarly, the cloud platform includes orchestration layers, virtualization technologies, container runtimes, and service management frameworks (software components) that enable dynamic allocation and execution of workloads. The cloud platform also incorporates APIs, middleware, and user-facing interfaces for interaction, automation, and integration with external systems. The cloud platform is designed to support multi-tenant operations, secure data exchange, and seamless interoperability across public, private, and hybrid cloud configurations, making it adaptable to diverse enterprise and application requirements.
[0003] In the context of cloud computing, migration refers to the process of moving digital assets - such as applications, data, workloads, and services - from one computing environment(source) to another (target). This could involve transitioning from on-premises infrastructure to a cloud environment, from one cloud provider to another, or between different configurations within the same cloud. In this process, a source environment is the original location where the assets currently reside, which could be a physical data centre, a private cloud, or a legacy system. A target system (host, environment) is the destination environment where these assets are intended to be deployed, typically a cloud-based infrastructure that offers improved scalability, performance, or cost-efficiency. The Migration involves not only transferring the assets but also adapting them to the architecture, protocols, and operational models of the target system while minimizing downtime and ensuring data integrity.
[0004] A Migration assessment in cloud computing refers to the systematic evaluation of an existing IT environment to determine its readiness, suitability, and strategy for migrating workloads, applications, and data to a cloud-based infrastructure. This process (migration assessment) involves analyzing the source environment where current systems and applications reside to gather metadata about hardware configurations, software dependencies, performance metrics, and compliance requirements. The goal is to identify potential challenges, estimate costs, map dependencies, and recommend optimal migration paths to the target system, which is typically a cloud platform offering improved scalability, reliability, or cost-efficiency.
[0005] The interaction with the cloud platform occurs through a set of integrated modules and services that automate and streamline this assessment. The cloud platform provides the computational resources, interfaces, and connectivity needed to access the source environment remotely using agentless methods and collect relevant metadata. The cloud platform then processes and standardizes this data to generate actionable insights, such as migration feasibility, resource mapping, and performance benchmarks. This interaction enables organizations to perform assessments at scale, across hybrid and multi-cloud environments, without disrupting existing operations or requiring intrusive software installations.
[0006] Agentless migration refers to the process of transferring applications, data, and workloads from one computing environment to another typically to a cloud platform without installing software agents on the source systems. Instead of relying on locally deployed agents to collect system information, agentless migration uses remote access protocols (such as APIs, SSH, or SDKs) to retrieve metadata and configuration details. This approach minimizes disruption to the source environment, reduces security and compliance risks, and simplifies deployment, especially in legacy or regulated infrastructures where agent installation is restricted or impractical.
[0007] In agentless migration assessment, retrieving detailed hardware-level metadata such as CPU configuration, memory allocation, disk topology, and network interface statistics without deploying agents poses a significant technical challenge. Many legacy systems and virtualized environments restrict direct access to low-level hardware metrics due to security policies or architectural limitations. Agentless tools must rely on remote interfaces or system-level APIs, which often expose limited or abstracted views of the underlying hardware. This results in incomplete or inaccurate metadata, especially in resource-constrained environments where performance benchmarking and infrastructure readiness assessments require precise hardware profiling. The absence of direct hardware interaction mechanisms in agentless setups undermines the reliability of migration planning and can lead to suboptimal resource allocation in the target system(s).
[0008] Enterprise IT landscapes increasingly span multiple cloud platforms such as AWS, Azure, Google Cloud (Trade names), private clouds, and on-premises infrastructure each with its own metadata exposure mechanisms, authentication models, and system architectures. In agentless migration assessment, the lack of a unified framework that can seamlessly adapt to these diverse environments creates fragmentation in metadata collection. Tools must be custom configured for each platform, leading to increased complexity, maintenance overhead, and risk of data inconsistency. Without a cloud-agnostic mechanism to standardize access and normalize metadata formats, agentless solutions struggle to deliver consistent and scalable assessments. This limits their effectiveness in hybrid and multi-cloud scenarios, where unified visibility and interoperability are critical for accurate migration planning and execution.
[0009] US20210174280A1 describes a system for cloud migration planning and prioritization, leveraging application profiling. The system does not disclose a unified, agentless metadata collection mechanism applicable across diverse cloud platforms.
[0010] EP2808790A2 presents a method for evaluating application compatibility with cloud platforms, particularly Platform-as-a-Service (PaaS). The method does not encompass infrastructure-level metadata collection or standardization across hybrid environments.
[0011] US20170090994A1 outlines a framework for cross-cloud orchestration of analytics workflows. The framework omits support for agentless metadata retrieval and unified schema generation essential for pre-migration assessment.
[0012] US9021097B2 introduces modular components for cloud infrastructure deployment. The infrastructure does not address agentless metadata extraction or comprehensive assessment of existing environments.
[0013] US9442810B2 provides a system for cloud resource orchestration and optimization. The system lacks provisions for agentless, cross-platform metadata collection and transformation necessary for migration readiness.
[0014] US20170155723A1 a system for discussing strategies for cloud service deployment and optimization. The system does not include a unified framework for agentless metadata standardization across heterogeneous systems.
[0015] Therefore, there is a need for a cloud platform and a method for migration assessment operations which overcomes the problems of prior art.
OBJECTS OF THE INVENTION:
[0016] An object of the present invention is to provide a cloud-based solution that enables non-intrusive evaluation of computing environments for migration readiness, without requiring software installation on existing systems.
[0017] One more object of the present invention is to enable consistent and scalable metadata collection across diverse cloud infrastructures, including public, private, hybrid, and on-premises environments, through a unified operational framework.
[0018] One more object of the present invention is to facilitate accurate and efficient transformation of heterogeneous system data into a standardized format suitable for analysis, reporting, and decision-making in cloud migration scenarios.
[0019] One more object of the present invention is to reduce operational complexity and improve automation in migration planning by integrating remote access capabilities with centralized processing and presentation mechanisms.
SUMMARY OF THE INVENTION:
[0020] The invention discloses a cloud-based platform for performing agentless migration assessment operations across heterogeneous computing environments, including hybrid infrastructure, public cloud, private cloud, and on-premises systems. The platform addresses the technical challenge of retrieving detailed infrastructure and application metadata without installing software agents on source systems, which is particularly critical in legacy or regulated environments. Existing tools lack a unified, cloud-agnostic framework for metadata collection, leading to fragmented data, inconsistent analysis, and increased operational complexity during cloud migration planning.
[0021] The platform comprises five integrated modules a user interface (UI), a cross-cloud integration module (CIM), a data collection engine (DCE), a backend processing system (BPS), and a metadata standardization module (MSM). The CIM standardizes access mechanisms such as RESTful APIs (e.g., AWS EC2 APIs, Azure Resource Graph), SSH, SNMP, WINRM, and SDKs (e.g., boto3, azure-mgmt, google-cloud-sdk) to retrieve metadata from diverse environments. The DCE retrieves metadata from target systems without requiring agent installation on source environments. The BPS initiates parallel automation tasks to process the retrieved metadata and transform it into a standardized format using rule-based mapping, machine learning classification, and template-driven formatting. The MSM converts the standardized metadata into a unified schema using dynamic schema templates and versioning mechanisms, enabling consistent analysis and visualization.
[0022] The method for performing migration assessment begins with configuring the platform modules within a network-enabled computing device. The user initiates assessment operations via the UI, which communicates with the CIM to establish secure, standardized access to computing environments. The DCE retrieves metadata from target systems such as virtual machines, databases, or application servers hosted on platforms like AWS, Azure, or VMware. The BPS processes the metadata in parallel and transforms into a normalized format. The MSM then converts the data into a unified schema, which is rendered through the UI in real-time dashboards. The dashboards display infrastructure health metrics (e.g., CPU usage, memory availability), migration readiness scores based on compatibility and dependency analysis, and cost projections derived from resource utilization and cloud pricing models.
[0023] From a user’s perspective, the platform offers a significant advantage by enabling secure, scalable, and non-intrusive migration assessments without disrupting existing systems. For example, an enterprise IT team can assess readiness across a hybrid environment comprising AWS EC2 instances, on-premises VMware clusters, and Azure VMs—without deploying agents—while receiving standardized, real-time insights through a centralized dashboard. This reduces deployment complexity, accelerates decision-making, and enhances visibility across diverse environments, making the platform particularly valuable for IT architects, infrastructure managers, and cloud strategists planning enterprise-scale migrations.
BRIEF DESCRIPTION OF DRAWINGS:
[0024] Figure 1 shows a schematic block diagram of a cloud platform in accordance with the present invention;
[0025] Figures 2, 3, 4, 5, 6 & 7 show various schematic views of various components of the cloud platform shown in Figure 1; and
[0026] Figure 8 shows a schematic block diagram of alternative embodiments of a cloud platform in accordance with the present invention; and
[0027] Figure 9 shows a flow chart of a method for performing migration assessment operations in using the cloud platform shown in Figure 1.
DETAILED DESCRIPTION OF DRAWINGS:
[0028] In a preferable embodiment of the invention (Figure 1), a cloud platform (300) for migration assessment operations is provided. The assessment operations include infrastructure readiness assessment, application dependency mapping, cost estimation, performance benchmarking, and compliance evaluation. These operations collectively support informed decision-making for cloud migration by analysing technical, financial, and regulatory aspects of existing systems.
[0029] The cloud platform (300) includes a user interface (UI) (100), a cross-cloud integration module (CIM) (130), a data collection engine (DCE) (150), a backend processing system (BPS) (180) and a metadata standardization module (MSM) (170). The user interface (UI) (100) is configured in a network-enabled computing device (110). The network-enabled computing device (110) has a memory (112) and a processor (114). The network-enabled computing device (110) functions as the central processing unit for executing migration assessment operations. The memory (112) stores system configurations, metadata, and intermediate processing data required for assessment workflows. The processor (114) performs execution of instructions related to data retrieval, transformation, and communication between platform modules. The network capability enables connectivity with multiple cloud and on-premises environments, supporting real-time data exchange and remote access for distributed assessment tasks.
[0030] The user interface (UI) (100) is configured within a network-enabled computing device (110) by deploying a set of modules(instructions) that allow interaction with various modules of the migration assessment platform. The computing device includes the memory (112) to store UI configurations, user inputs, and retrieved metadata, and the processor (114) to execute UI-related operations such as rendering screens, handling user commands, and communicating with backend systems. Network capability enables the UI to connect with remote computing environments, retrieve assessment data, and present results in real time, supporting centralized control and monitoring of migration tasks.
[0031] The user interface (100) is configured to initiate and manage one or more assessment operations across one or more computing environments (120a, 120b, 120c) connected therewith. More specifically, the user interface (100) is configured to initiate and manage assessment operations across the computing environments (120a, 120b, 120c) (120) by enabling interaction with underlying modules responsible for data collection, processing, and analysis. The user interface (100) allows selection of assessment parameters, triggering of automated workflows, and monitoring of execution status. The user interface (100) allows integration with network-enabled computing device (110) ensures connectivity with distributed environments, supporting centralized execution and control of migration assessment tasks.
[0032] The computing environment(s) (120a, 120b, 120c, 120) includes a hybrid computing infrastructure (121), a public cloud (122), a private cloud (123), and on-premises infrastructure (124), or combinations thereof. The hybrid infrastructure (121) typically integrates on-premises systems with cloud services such as Microsoft Azure Stack or AWS Outposts. The public cloud (122) environments may include platforms like Amazon Web Services (AWS), Google Cloud Platform (GCP), or Microsoft Azure, offering scalable and shared resources. The private cloud (123) setups, such as VMware vSphere or OpenStack (trade names), provide dedicated infrastructure with enhanced control and security. The on-premises infrastructure (124) consists of locally hosted servers, storage, and networking systems managed within enterprise data centers. The computing environments (121, 122, 123, 124) are collectively supporting diverse deployment models and operational requirements for migration assessment.
[0033] The cross-cloud integration module (CIM) (130) is configured within the network-enabled computing device (110). The CIM (130) is implemented as a set of instructions (modules) that resides in the device’s memory (112) and operates through the processor (114). Configuration of the CIM (130) enables the CIM (130) to be accessible to other modules within the platform for integration-related tasks. The cross-cloud integration module (CIM) (130) is communicatively coupled to the user interface (100). The coupling is established through internal communication protocols within the network-enabled computing device (110), allowing data exchange and coordination between the CIM (130) and the user interface (100). The connection (coupling) enables the user interface (100) to access integration functionalities provided by the CIM (130) for interacting with various computing environments.
[0034] The cross-cloud integration module (CIM) (130) is configured to interface with the computing environments (120a, 120b, 120c) and to standardize access mechanisms (210) for retrieving infrastructure and application metadata (140). Configurations of the CIM (130) include implementation of connectors, adapters, or API handlers that enable communication with diverse platforms. Standardization of access mechanisms (210) ensures uniform retrieval of metadata (140) regardless of the underlying environment type, supporting consistent input for further processing and analysis.
[0035] The access mechanisms (210) (Figure 2) enable standardized retrieval of infrastructure and application metadata (140) from diverse computing environments (145). The access mechanisms (210) include API-based access (212), secure shell (SSH) (213), agentless protocols (214), and SDK integrations (215). The API-based (212) access utilizes cloud-native interfaces such as AWS EC2 APIs, Azure Resource Graph, or GCP Compute APIs to extract metadata programmatically. The SSH (213) provides encrypted command-line access to Unix/Linux systems for direct data extraction. The Agentless protocols (214) such as SNMP, WINRM, and PowerShell Remoting allow metadata collection without deploying software agents, supporting environments with strict security policies.
[0036] The SDK integrations (215) involve the use of vendor-specific software development kits like boto3 for AWS, azure-mgmt for Azure, and google-cloud-sdk (Trade name) for GCP. Said SDKs simplify authentication, data parsing, and error handling during metadata retrieval. Technical enablement of said mechanisms requires installation of client libraries, configuration of credentials, and network accessibility to target systems. Together, the access mechanisms (210) ensure consistent and secure data acquisition across hybrid, public, private, and on-premises infrastructures, forming the foundation for backend processing and transformation into standardized formats.
[0037] The data collection engine (DCE) (150) is configured within the network-enabled computing device (110). The DCE (150) resides in the device’s memory (112) and operates through the processor (114). Configuration includes integration with access mechanisms (210) for metadata retrieval from computing environments.
[0038] The data collection engine (DCE) (150) is configured to retrieve metadata (140) from one or more target systems (127) (Figure 3) of the computing environments (120a, 120b) without requiring installation of software agents on one or more source environments (125) of the computing environments (120a, 120b). Configurations of the DCE (150) include support for agentless access mechanisms and remote connectivity protocols that enable metadata extraction directly from target systems.
[0039] The target system (127) refers to the specific computing resource within a cloud or hybrid environment from which infrastructure and application metadata is retrieved. The target system (127) may include virtual machines, databases, storage units, or application servers hosted on platforms such as AWS EC2, Azure VMs, or VMware (trade name) environments. The source environment (125), on the other hand, represents the original infrastructure setup where workloads currently reside, such as on-premises data centers or private cloud deployments. The source environments (125) are assessed without deploying software agents, preserving system integrity and minimizing operational overhead.
[0040] The data collection engine (DCE) (150) retrieves metadata from the target systems (127) using agentless access mechanisms, eliminating the need for software installation on source environments. This is achieved through protocols and tools such as RESTful APIs, SSH, SNMP, WINRM, and cloud SDKs. The protocols and tools allow remote execution of queries and commands to extract data related to system configuration, resource utilization, and application dependencies. The agentless approach ensures secure, scalable, and non-intrusive data collection across heterogeneous infrastructures.
[0041] The backend processing system (BPS) (180) is configured within the network-enabled computing device (110). The BPS (180) is implemented as a software module that operates using the processor (114) and utilizes the memory (112) for storing intermediate and processed data. Configurations of the BPS (180) enable the BPS (180) to interact with other components of the cloud platform (300) for handling data-related operations. The backend processing system (BPS) (180) is configured to receive assessment requests (193) from the user interface (100). The configurations include communication pathways within the network-enabled computing device (110) that allow structured transmission of request data from the user interface (100) to the BPS (180) for further processing.
[0042] Further, the BPS (180) being configured to initiate parallel automation tasks (195) according to the request (193) that retrieve metadata (140) from the target systems (127) and transform the retrieved metadata (140) into a standardized format (190) for consistent analysis and presentation.
[0043] The backend processing system (BPS) (180) (Figure 4) is architected to efficiently manage and execute parallel automation tasks in response to incoming requests. Each request acts as a trigger, prompting the system to launch multiple concurrent operations that connect to various target systems—such as databases, APIs, or configuration servers—to retrieve metadata. The metadata (140), which may differ in structure and semantics across systems, is then processed and transformed into a standardized format. Said transformation ensures uniformity, enabling seamless analysis and presentation regardless of the original source or format of the data.
[0044] For example, consider a scenario where an enterprise needs to audit metadata from three different environments: a MySQL database, a RESTful API, and a Kubernetes cluster. The BPS (180) would initiate three parallel tasks each tailored to the specific system—to extract relevant metadata like table schemas, endpoint definitions, and deployment configurations. The extracted metadata is then normalized into a unified JSON schema, allowing analysts to compare, visualize, and report on the data consistently. Said approach not only accelerates the metadata processing pipeline but also enhances the reliability and clarity of insights derived from heterogeneous systems.
[0045] The backend processing system (BPS 180) employs multiple intelligent and structured techniques to transform retrieved metadata (140) into the standardized format (190), ensuring consistency and usability across diverse systems. The BPS (180) transforms the retrieved metadata (140) into the standardized format (190) using rule-based mapping (331) (Figure 5), where predefined rules are used to align source metadata fields with standardized ones. Alternatively, the BPS (180) transforms the retrieved metadata (140) into the standardized format (190) using a machine learning-based classification (332) which leverages trained models (not shown) to identify and categorize metadata elements based on learned patterns (predefined or learning patterns).
[0046] Another embodiment, the BPS (180) transforms the retrieved metadata (140) into the standardized format (190) by a template-driven formatting (333), which uses predefined templates to structure metadata uniformly. This ensures consistent presentation, such as formatting API metadata into a standard JSON layout with keys like endpoint, method, and response. Another embodiment, the BPS (180) transforms the retrieved metadata (140) into the standardized format (190) by a schema mapping (325) aligns source schemas with a target schema, preserving relationships and data types ideal for integrating relational databases into a centralized data warehouse (not shown). In another embodiment, the BPS (180) transforms the retrieved metadata (140) into the standardized format (190) by a data normalization (326) standardizes values and formats, while format transforms metadata between formats like XML to JSON or CSV to YAML, enabling compatibility with modern analytics platforms.
[0047] The metadata standardization module (MSM 170) (Figure 6) is configured within the computing device (110), which serves as a core component for ensuring uniformity and consistency of metadata across diverse and distributed sources. Operating locally within the computing device (110), the MSM (170) is optimized for real-time or near-real-time processing, enabling it to handle continuous streams of metadata with minimal latency and high responsiveness. This makes it particularly effective in dynamic environments where the metadata (140) is frequently retrieved from various target systems (127) residing in the source environments (125). The MSM (170) receives the metadata (140) via the data collection engine (DCE 150) and initiates a transformation process to convert the metadata (140) into a unified schema (185). The unified schema (185) is a standardized format that supports consistent interpretation, integration, and analysis. The MSM (170) is configured to convert the retrieved metadata (140) by the DCE (150) from the target systems (127) residing in the source environments (125) into the unified schema (185) for consistent presentation and analysis through the user interface (100).
[0048] The MSM (170) includes a metadata parser, schema mapping engine, normalization layer, and format converter. Said elements work in tandem to interpret the structure and semantics of the raw metadata, apply rule-based or intelligent mapping techniques, standardize values and naming conventions, and convert formats as needed. The resulting unified metadata is then made available for presentation and analysis through the user interface (100), allowing users to interact with a coherent and harmonized view of metadata from multiple systems. This configuration not only enhances interoperability across platforms but also supports accurate decision-making and streamlined data governance by providing a reliable and consistent metadata foundation.
[0049] The MSM (170) is configured to generate the unified schema (185) by leveraging dynamic schema templates (341) (figure 7) that is designed to adapt to evolving conditions within the source environments (125). These templates are responsive to changes in metadata structures, formats, and semantics across different systems. As new metadata types or configurations emerge from the target systems (127), the MSM (170) dynamically adjusts the dynamic schema templates (341) to accommodate these variations, ensuring that the standardized output remains accurate and relevant. This adaptability is crucial for maintaining interoperability and consistency in environments where system configurations and data models are frequently updated.
[0050] In addition to the dynamic adaptation, the MSM (170) supports versioning (342) of the unified schema (185), which is essential for audit and compliance tracking. Each transformation or update to the unified schema (185) is recorded with a version identifier, allowing the system to maintain a historical log of the unified schema (185) changes over time. This versioning capability enables traceability of metadata transformations, supports rollback operations if needed, and ensures that compliance requirements—such as data lineage, governance, and regulatory audits—can be met with precision. Together, the dynamic schema templates (341) and the versioning mechanisms (342) empower the MSM (170) to deliver robust, scalable, and compliant metadata standardization across diverse and evolving the computing environments (120a, 120b, 120c).
[0051] In an embodiment (300a) (Figure 1) of the cloud platform (300), the cloud platform (300a) is deployed as a Software-as-a-Service (SaaS) solution, which enables users to perform migration assessment operations remotely without requiring any local installations. This cloud-native deployment model centralizes all core functionalities within a secure, scalable, and managed environment. Users interact with the platform through a secure web-based interface, which is accessible via any network-enabled computing device (110). This interface serves as the primary access point for initiating and managing operations, eliminating the need to install or configure components locally. As a result, the cloud platform (300a) supports rapid onboarding, reduces infrastructure overhead, and ensures consistent performance across varied user environments.
[0052] The network-enabled computing device (110) is configured to access the cloud platform (300) remotely, enabling seamless execution of migration assessment workflows. This remote access capability allows users to leverage the full suite of platform features including metadata collection, transformation, integration, and analysis without installing the user interface (100), the cross-cloud integration module (130), the data collection engine (150), the backend processing system (180), or the metadata standardization module (170) on local machines. The modules (130, 150, 180, 170) are hosted and orchestrated within the cloud platform, ensuring centralized control, automatic updates, and simplified maintenance. The cloud platform (300a) is particularly advantageous for distributed teams, hybrid cloud environments, and organizations seeking to minimize local resource dependencies.
[0053] Furthermore, the SaaS model enhances security, scalability, and compliance. The secure web-based interface employs encryption and authentication protocols to protect data in transit and ensure authorized access. The centralized deployment also facilitates version control, audit logging, and policy enforcement, which are critical for enterprise-grade migration assessments. By abstracting the complexity of local installations and offering a fully managed cloud experience, the cloud platform (300a) empowers users to conduct assessments efficiently, collaborate across locations, and maintain alignment with evolving infrastructure and compliance requirements.
[0054] In one more embodiment (300b) (figure 8) of the cloud platform (300), the platform (300) incorporates a robust role-based access control (RBAC) system (382) configured to manage user permissions for initiating and viewing migration assessment operations. The RBAC system (382) ensures that access to sensitive functionalities and data is governed by clearly defined user roles, such as administrator, analyst, auditor, or viewer. Each role is associated with a specific set of privileges, allowing the platform to enforce operational boundaries and maintain data security. For example, while an administrator may have full access to initiate assessments, configure modules, and view all results, a viewer may be restricted to read-only access to finalized reports.
[0055] Technically, the RBAC system (382) is integrated into the platform’s (300) authentication and session management framework (303). Upon login, the cloud platform (300) identifies the user’s role and dynamically adjusts the interface and accessible features accordingly. This not only enhances security by preventing unauthorized actions but also simplifies the user experience by presenting only relevant tools and data. Additionally, the RBAC system (382) supports audit logging, ensuring that all user actions are tracked and can be reviewed for compliance and operational transparency.
[0056] In one more embodiment (300b) (Figure 1) of the cloud platform (300), the user interface (100) is configured to provide real-time visualization dashboards for assessment results, including infrastructure health metrics, migration readiness scores, and cost projections.
[0057] The user interface (100) is configured to deliver real-time visualization dashboards (not shown) that present the results of migration assessment operations in an intuitive and interactive format. The real-time visualization dashboards are designed to provide users with immediate insights into key evaluation metrics, including infrastructure health, migration readiness scores, and cost projections. By leveraging dynamic data rendering techniques, the interface ensures that users can monitor system performance, identify potential risks, and evaluate migration feasibility without delay. The real-time nature of the dashboards allows for continuous updates as new data is collected and processed, supporting agile decision-making and proactive planning.
[0058] Technically, the user interface integrates with backend services to fetch standardized metadata and assessment outputs, which are then, visualized using charts, gauges, heatmaps, and tabular views. Infrastructure health metrics may include CPU usage, memory availability, network latency, and system uptime, while migration readiness scores are derived from compatibility checks, dependency analysis, and configuration assessments. Cost projections are calculated based on resource utilization, licensing models, and cloud pricing data. These visualizations are rendered using responsive design principles, ensuring accessibility across devices and platforms. The interface also supports filtering, drill-down, and export capabilities, enabling users to customize views and share insights with stakeholders.
[0059] The user interface (100) is designed to present a unified, cloud-agnostic view of migration assessment data, enabling centralized planning and strategic decision-making across multiple cloud platforms. Regardless of whether the underlying infrastructure resides in AWS, Azure, Google Cloud, or private cloud environments, the interface abstracts platform-specific details and displays assessment results in a consistent format. By harmonizing data from diverse cloud sources, the interface empowers users to compare infrastructure health, readiness scores, and cost projections across platforms without being constrained by vendor-specific tools or formats.
[0060] Technically, the cloud-agnostic capability is achieved through integration with the cross-cloud integration module (130) and the metadata standardization module (170), which collectively normalizes and unifies metadata from different cloud environments. The user interface (100) then renders the standardized data using interactive dashboards, allowing users to filter, analyze, and act on insights from a single pane of glass. The centralized view supports enterprise-wide migration planning, facilitates cross-team collaboration, and ensures that decisions are based on comprehensive, platform-neutral data. It also reduces complexity and enhances transparency in multi-cloud strategies, making it easier to align technical assessments with business goals.
[0061] The processor (114) is central to executing instructions across all modules, handling tasks such as initiating user commands, managing secure connections to external environments, and performing parallel automation workflows. The memory (112) stores configurations, retrieved metadata, intermediate processing results, and schema templates, ensuring that each module has rapid access to the data it needs. The network interface of the computing device enables secure, real-time communication with external computing environments (120a–120c), allowing agentless metadata retrieval and cross-cloud integration. These hardware components collectively ensure that the platform can operate efficiently, securely, and on scale.
[0062] Each instruction sets (modules) interacts with the hardware in a distinct way. The user interface (100) relies on the processor for rendering dashboards and handling user inputs, while the CIM (130) uses the network interface to establish standardized access to APIs, SDKs, and remote protocols. The DCE (150) leverages both processor and memory to execute agentless data collection routines and temporarily store retrieved metadata. The BPS (180) performs high-throughput parallel processing tasks, requiring robust CPU performance and memory bandwidth to transform diverse metadata into a standardized format. Finally, the MSM (170) uses memory to manage dynamic schema templates (341) and versioning data (342), and the processor (114) to apply rule-based or intelligent transformations. Together, these interactions between hardware and software components ensure the platform’s ability to deliver unified, scalable, and non-intrusive migration assessments.
[0063] Each module of the cloud platform (300) interacts with external computing environments (hybrid/on-premises, public cloud) through specific hardware components that enable secure, scalable, and efficient operations. The network interface of the computing device (110) is essential for establishing remote connections to these environments using protocols like SSH, REST APIs, SNMP, and SDKs. This interface supports the CIM (130) in standardizing access mechanisms and allows the DCE (150) to perform agentless metadata retrieval. The processor (114) executes protocol-specific commands and handles encryption/decryption tasks to maintain secure communication with cloud and on-prem systems. Additionally, the memory (112) temporarily stores retrieved metadata and access credentials, ensuring smooth data flow between the platform and external environments.
[0064] For modules like the BPS (180) and MSM (170), hardware support is critical when processing and transforming metadata from diverse environments. The processor (114) must handle parallel automation tasks that interact with multiple systems simultaneously, such as virtual machines in AWS or legacy servers in on-premises setups. The memory supports caching and buffering large datasets during transformation and schema standardization. These modules rely on high-throughput data exchange with external environments, which demands robust network bandwidth and low-latency connectivity. In essence, the hardware elements of the computing device (110) act as the operational backbone, enabling seamless interaction between the platform’s software modules and the heterogeneous computing environments they assess.
[0065] In one more embodiment of the present invention, a method (1000) (Figure 9) for performing migration assessment operations using the cloud platform (300, 300a, 300b) is provided.
[0066] The method (1000) starts at step 1000a.
[0067] At step 1001, the user interface (UI 100), the cross-cloud integration module (CIM 130), the data collection engine (DCE 150), the backend processing system (BPS 180), and the metadata standardization module (MSM 170) are configured within the network-enabled computing device (110).
[0068] At step 1002, the user initiates and manages one or more migration assessment operations across various computing environments (120a, 120b, 120c) using the UI (100). The UI (100) serves as the control center for launching assessments, monitoring progress, and reviewing results.
[0069] At step 1003, the CIM (130) which is communicatively coupled to the UI (100) interfaces with the computing environments (120a, 120b, 120c) through. The CIM (130) standardizes access mechanisms (210) to ensure consistent and secure retrieval of infrastructure and application metadata (140) from the diverse environments.
[0070] At step 1004, the DCE (150) retrieves metadata (140) from one or more target systems (127) within the computing environments (120a, 120b (120)), without requiring the installation of software agents on the source environments (125). This agentless approach simplifies deployment and reduces operational overhead.
[0071] At step 1005, the backend processing system (180) receives assessment requests (193) from the UI (100) and initiates parallel automation tasks (195) to collect metadata from the target systems. This parallelism enhances performance and scalability, especially in large or distributed environments.
[0072] At step 1006, the BPS (180) transforms the retrieved metadata (140) into the standardized format (190), enabling consistent analysis and presentation. This transformation ensures that metadata from various sources can be interpreted uniformly.
[0073] At step 1007, the MSM (170) converts the standardized metadata into a unified schema (185). The unified schema (185) supports consistent visualization and analysis through the UI, allowing users to make informed decisions based on harmonized data across multiple platforms.
[0074] The method (100) ends at step (1000b).
[0075] The present invention (300,300a, 300b,1000) enables users in enterprise IT, cloud strategy, and infrastructure management roles to utilize a cloud platform (300) for migration assessment operations across diverse computing environments such as hybrid infrastructure (121), public cloud (122), and private cloud (123). Through a secure, web-based user interface (100) configured in a network-enabled computing device (110), users can initiate and manage assessments without requiring local installations. The platform (300) supports centralized planning and decision-making by providing real-time visualization dashboards for assessment results, including infrastructure health metrics, migration readiness scores, and cost projections. Role-based access control ensures that different stakeholders—such as architects, analysts, and auditors—can securely access relevant data and insights based on their responsibilities.
[0076] Using the method (1000) defined in the invention, users can execute structured workflows that include initiating assessment operations via the UI (100), interfacing with multiple environments through the cross-cloud integration module (130) and retrieving metadata (140) using the data collection engine (150) without deploying agents. The backend processing system (180) and metadata standardization module (170) work together to transform and unify metadata into the unified schema (185), enabling clear and actionable insights. These capabilities support practical applications such as infrastructure readiness evaluation, dependency mapping, cost-benefit analysis, and compliance tracking, making the invention a valuable tool for organizations planning cloud migration or modernization initiatives.
[0077] The invention delivers significant technical advantages by enabling non-intrusive, agentless metadata collection from diverse computing environments, including legacy systems and regulated infrastructures. The integration of remote access mechanisms such as API-based access (212), SSH (213), and SDKs (215) allows the data collection engine (150) to retrieve detailed infrastructure and application metadata (140) without installing software agents. This reduces deployment complexity and avoids disruptions to existing systems, while still enabling access to hardware-level metrics necessary for accurate migration assessments. The backend processing system (180) enhances said capability by executing parallel automation tasks (195) and transforming the collected metadata into the standardized format (190), ensuring consistency and reliability in analysis.
[0078] The platform (300) further introduces a unified operational framework that supports metadata collection across heterogeneous cloud environments, including public, private, hybrid, and on-premises infrastructures. The cross-cloud integration module (130) standardizes access mechanisms (210), while the metadata standardization module (170) converts diverse metadata into the unified schema (185) using the dynamic schema templates (341) that adapts to changes in the source environments (125). This enables scalable and consistent assessments across platforms, reducing fragmentation and manual configuration efforts. The user interface (100) presents the results through real-time dashboards while role-based access control ensures secure and permission-based usage. The CIM (130), the DCE (150), the BPS (180) and the MSM (170) collectively improve automation, interoperability, and operational efficiency in cloud migration planning. , Claims:We Claim:
1) A cloud platform (300) for migration assessment operations, the cloud platform (300) comprises:
- a user interface (UI) (100) configured in a network-enabled computing device (110), the computing device (110) having a memory (112) and a processor (114) and a network interface configured to enable secure communication with one or more external computing environments (120a, 120b, 120) , wherein the user interface (100) by utilizing the processor (114) and the memory (112) is configured to initiate and manage one or more assessment operations across the computing environments (120a, 120b, 120) connected therewith;
- a cross-cloud integration module (CIM) (130) configured in the computing device (110), the CIM (130) being communicatively coupled to the user interface (100), the CIM (130) by utilizing the processor (114) and memory (112) configured to interface with the computing environments (120a, 120b, 120c), and to standardize an access mechanisms (210) for retrieving infrastructure and application metadata (140) from the computing environments (120a, 120b, 120);
- a data collection engine (DCE) (150) configured in the computing device (110), the DCE (150) by leveraging the processor (114), the memory (112) and network interface is configured to retrieve metadata (140) from one or more target systems (127) within the computing environments (120) without requiring installation of agents on one or more source environments (125) of the computing environments (120);
- a backend processing system (BPS) (180) configured in the network-enabled computing device (110), the BPS (180) by utilizing the processor (114) for parallel execution and the memory (112) for intermediate data handling is configured to receive assessment requests (193) from the user interface (100), initiate parallel automation tasks (195) according to the assessment requests (193) that retrieve metadata (140) from the target systems (127), and transform the retrieved metadata (140) into a standardized format (190) suitable for consistent analysis and presentation; and
- a metadata standardization module (MSM) (170) configured within the network-enabled computing device (110), the MSM (170) by using the memory (112) to manage schema templates and the processor (114) to apply transformations configured to convert the retrieved metadata (140) by the DCE (150) from the target systems (127) residing in the source environments (125) into a unified schema (185) for consistent presentation and analysis through the user interface (100).
2) The cloud platform (300) as claimed in claim 1, wherein the assessment operations include infrastructure readiness assessment, application dependency mapping, cost estimation, performance benchmarking and compliance evaluation.
3) The cloud platform (300) as claimed in claim 1, wherein the external computing environments (120a, 120b, 120c) comprise a hybrid computing infrastructure (121), a public cloud (122), a private cloud (123), on-premises infrastructure (124) or combinations thereof.
4) The cloud platform (300) as claimed in claim 1, wherein the access mechanisms (210) including API-based access (212), secure shell (SSH) (213), agentless protocols (214), and SDK integrations (215).
5) The cloud platform (300) as claimed in claim 1, wherein the backend processing system (180) transforms the retrieved metadata (140) into the standardized format (190) using rule-based mapping (331) or a machine learning-based classification (332) or template-driven formatting (333) or schema mapping (325) or data normalization (326).
6) The cloud platform (300) as claimed in claim 1, wherein the metadata standardization module (170) is configured to generate the unified schema (185) based on dynamic schema templates (341) that adapt to changes in the source environments (125), and support versioning (342) for audit and compliance tracking.
7) The cloud platform (300a) as claimed in claim 1, wherein the cloud platform (300a) is deployed as a Software-as-a-Service (SaaS) solution, and the network-enabled computing device (110) is configured to access the platform (300a) remotely via a secure web-based interface, enabling execution of migration assessment operations without requiring local installation of the user interface (100), the cross-cloud integration module (130), the data collection engine (150), the backend processing system (180), or the metadata standardization module (170).
8) The cloud platform (300b) as claimed in claim 1, wherein the platform (300) includes a role-based access control system (382) to manage user permissions for initiating and viewing assessment operations.
9) The cloud platform as claimed in claim 1, wherein the user interface (100) is configured to provide real-time visualization dashboards for assessment results, including infrastructure health metrics, migration readiness scores, and cost projections.
10) A method (1000) for performing migration assessment operations using a cloud platform (300), the method (1000) comprising:
configuring a user interface (UI) (110), the CIM (130), the DCE (150), the BPS (180), the MSM (170) in a network enabled computing device (110), the computing device (110) comprising a memory (112) and a processor (114);
initiating and managing one or more assessment operations across one or more computing environments (120a, 120b, 120c) via the user interface (UI) (100);
interfacing (establishing) with the computing environments (120a, 120b, 120c) through a cross-cloud integration module (CIM) (130) communicatively coupled to the UI (100), the CIM (130) being configured to standardize access mechanisms (210) for retrieving infrastructure and application metadata (140) from the computing environments (120a, 120b, 120c);
retrieving metadata (140) from one or more target systems (127) of the computing environments (120) using the data collection engine (DCE) (150) wherein the retrieval is performed without requiring installation of software agents on one or more source environments (125) of the computing environments (120);
receiving assessment requests (193) from the UI (100) at a backend processing system (BPS) (180) and initiating parallel automation tasks (195) according to the assessment requests (193) to retrieve metadata (140) from the target systems (127);
transforming the retrieved metadata (140) into a standardized format (190) for consistent analysis and presentation using the BPS (180); and
converting the retrieved metadata (140) into a unified schema (185) using the metadata standardization module (MSM) (170), wherein the unified schema (185) enables consistent presentation and analysis through the UI (100).