DMS Cluster Snapshot Coordination for Cross-Platform Data Portability
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Solution Overview
Problem
Current data management and storage solutions face challenges in efficiently managing and storing data across different virtual machine platforms, particularly in creating and porting applications and data between various virtualization platforms like VMware, Hyper-V, and others, due to the complexity of compute infrastructure and the need for reliable backup and recovery.
Innovation Solution
A data management and storage (DMS) cluster of peer nodes that generates snapshots of data for applications by associating machines with the application, scheduling data fetch jobs, and storing them in a distributed data store, enabling seamless data management and recovery across diverse platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is stored using platform-specific formats for different virtualization platforms, then data can be natively optimized for each platform, but data portability between platforms becomes difficult
Solution Approach 1:
The patent introduces an intermediary data format that sits between platform-specific formats and the storage system. This intermediary format acts as a universal interface that can represent data from any virtualization platform (VMware, Hyper-V, KVM, etc.) in a standardized way, enabling portability while maintaining platform-specific optimizations through conversion layers.
Solution Approach 2:
The storage system is designed to handle multiple virtualization platforms through a universal data representation layer. The system can ingest data from different platforms, normalize it to a common format, and store it in a way that maintains compatibility across platforms, making the storage solution multi-functional and platform-agnostic.
2Reliability
If snapshots of multiple machines are taken simultaneously for application backup, then data consistency across machines is improved, but the time required to complete backup increases
Solution Approach 1:
The system performs preliminary actions by first identifying all machines associated with an application and preparing snapshot jobs for them before actual data capture begins. This pre-planning allows for coordinated execution that ensures consistency while optimizing timing to minimize overall backup duration.
Solution Approach 2:
The patent implements periodic snapshot execution with optimized intervals. By using periodic action with carefully chosen timing, the system can capture consistent states across multiple machines at regular intervals while balancing the need for data consistency with the desire to minimize backup time and resource consumption.
3Ease of operation
If a centralized data management system is used to manage all compute infrastructure, then ease of operation is improved, but device complexity increases
Solution Approach 1:
The centralized data management system is segmented into independent, modular components that can be deployed and managed separately. Each module handles specific functions (snapshot creation, storage, retrieval, restoration), allowing the system to provide centralized management capabilities while maintaining lower individual component complexity and enabling incremental deployment.
Data Source
AI summary
A data management and storage (DMS) cluster of peer DMS nodes manages data of an application distributed across a set of machines of a compute infrastructure. A DMS node associates a set of machines with the application, and generates data fetch jobs for the set of machines for execution by multiple peer DMS nodes. The DMS node determining whether each of the data fetch jobs for the set of machines is ready for execution by the peer DMS nodes. In response to determining that each of the data fetch jobs is ready for execution, the peer DMS nodes execute the data fetch jobs to generate snapshots of the set of machines. The snapshots may be full or incremental snapshots, and collectively form a snapshot of the application.