Cloud Micro-Service Architecture for Storage Analytics
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Solution Overview
Problem
Traditional storage management systems face challenges in rapidly introducing new services and applications due to monolithic architectures, which are difficult to modify without affecting the entire system, and require expensive on-premise hardware and software for processing telemetry data.
Innovation Solution
A cloud-based system that provides micro-services with a modular, lightweight architecture, allowing independent development and deployment of services, using a cloud manager to collect and process telemetry data, and a data loader to store and access data via a network connection, enabling rapid provisioning of analytics services and user interfaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If monolithic architecture is used for storage management systems, then system stability is maintained, but adaptability and ease of modification deteriorate
Solution Approach 1:
The patent divides the monolithic storage management system into independent micro-services that can be deployed and modified separately. Each micro-service handles a specific function (data collection, processing, storage, analytics), allowing the system to adapt to new requirements without affecting the entire system. This segmentation enables flexible addition of new services while maintaining overall system stability.
Solution Approach 2:
The system implements dynamic service registration and discovery mechanisms, allowing micro-services to be added, removed, or updated at runtime without system reconfiguration. The service registry automatically manages service lifecycles, enabling the architecture to adapt dynamically to changing requirements while maintaining operational stability.
2Productivity
If on-premise hardware and software systems are deployed for processing telemetry data, then data processing capability is ensured, but cost and deployment time increase
Solution Approach 1:
The patent utilizes cloud-based infrastructure as a copyable, scalable alternative to on-premise systems. Micro-services are deployed as containerized applications that can be rapidly instantiated and scaled in cloud environments, eliminating the need for physical hardware deployment while maintaining full data processing capabilities. This approach reduces deployment time from weeks to minutes.
Solution Approach 2:
The cloud-based micro-service architecture provides universal data processing capabilities that can handle multiple types of telemetry data through a unified platform. The system can process various data formats and analytics workloads using the same infrastructure, eliminating the need for specialized on-premise systems for each function.
3Adaptability or versatility
If monolithic middleware layer is modified to add new services, then new functionality is achieved, but system reliability deteriorates due to re-testing requirements
Solution Approach 1:
By segmenting the system into independent micro-services, the patent eliminates the need to modify the entire middleware layer when adding new functionality. Each service is self-contained with its own codebase and deployment unit, allowing new services to be added without touching existing stable components. This segmentation isolates changes and prevents cascading failures.
Solution Approach 2:
The patent introduces a service registry and API gateway as intermediary components that manage service communication and coordination. These intermediaries handle service discovery, load balancing, and request routing, allowing new services to integrate with the system through standardized interfaces without modifying core middleware logic. This preserves system reliability while enabling service extensibility.
4Ease of operation
If traditional web services architecture with relational databases is used, then data storage and access is established, but ease of introducing new services deteriorates
Solution Approach 1:
The patent adopts modern cloud-native data storage solutions (such as NoSQL databases, data lakes, or managed database services) as replacements for traditional relational databases. These copied, proven architectures offer flexible schemas and simplified data access patterns that align with micro-service requirements, enabling rapid service introduction without the rigidity of traditional database architectures.
Solution Approach 2:
The system implements configurable data storage parameters and access patterns that can be adjusted per micro-service requirements. Different services can use different storage backends, data models, and access methods based on their specific needs, all managed through a unified service framework. This parameter flexibility enables easy introduction of new services with diverse data requirements.
Data Source
AI summary
Methods and systems for a networked computing system are provided. One method includes deploying a micro-service associated with data stored by a networked storage system at a storage device, the micro-service deployed as a virtual machine of a cloud-based system having a data store, and an application programming interface (API) for providing analytic information associated with the data and resources of the networked storage system, where the cloud based system is accessible to a client system via a first network connection; processing performance data associated with the micro-service by a first computing system; storing the processed performance data for the micro-service at a storage volume accessible via a second network connection; copying the processed performance data at the data store by a data loader; and providing access to the processed performance data to the client system from the data store via the API and the first network connection.