Extensible Data Management Services Engine for Storage Adaptability
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Solution Overview
Problem
Existing storage systems struggle to adapt to varying data management demands, such as shifts from document to multimedia data, due to limited computational resources in the storage front end and high storage density in the storage back end.
Innovation Solution
A data management services architecture that redirects storage requests from the storage domain to the compute domain, manages resources, ensures compliance with resource consumption policies, deploys data management services, and monitors service instances, allowing for adaptive and flexible resource allocation based on demand.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If computational resources are concentrated in the storage front end, then data management services can be processed locally, but the system loses flexibility and adaptability to varying data management demands
Solution Approach 1:
The system divides data management services into two segments: storage front end (for basic operations) and compute domain (for complex processing). This segmentation allows each component to specialize, improving adaptability while managing complexity through clear functional separation.
Solution Approach 2:
A services engine acts as an intermediary between the storage front end and compute domain. It redirects storage requests to appropriate services in the compute domain, enabling flexible resource allocation without increasing front-end complexity.
2Quantity of substance
If storage density is increased in the storage back end, then storage capacity is improved, but computational resources become insufficient for data management
Solution Approach 1:
The system moves computational resources from the traditional storage front end to a separate compute domain, adding a new dimensional perspective to resource allocation. This allows the storage back end to focus purely on capacity while the compute domain provides necessary processing power.
3Reliability
If data management services are deployed in the storage domain, then service latency is reduced, but service isolation and security are compromised
Solution Approach 1:
The services engine serves as a mediator that maintains service isolation by running services in the compute domain while still enabling low-latency operation through efficient request redirection and response handling mechanisms.
Data Source
AI summary
A data management services architecture includes architectural components that run in both a storage and compute domains. The architectural components redirect storage requests from the storage domain to the compute domain, manage resources allocated from the compute domain, ensure compliance with a policy that governs resource consumption, deploy program code for data management services, dispatch service requests to deployed services, and monitor deployed services. The architectural components also include a service map to locate program code for data management services, and service instance information for monitoring deployed services and dispatching requests to deployed services. Since deployed services can be stateless or stateful, the services architecture also includes state data for the stateful services, with supporting resources that can expand or contract based on policy and/or service demand. The architectural components also include containers for the deployed services.