Emc IP Holding Company LLC

Venkata Ravi Chandra Bandlamudi

Philip N Shilane

Atul Avinash Karmarkar

Joseph S Brandt

Kimberly R Lu

One example method includes receiving a write request that includes a data structure version to be written, wherein the data structure version is associated with a unique identifier, storing the data structure version in association with the unique identifier, receiving a read request for a most recent version of the data structure and, when the stored data structure version is not the most recent version of the data structure, examining respective unique identifiers of each of a group of other stored data structure versions to determine which stored data structure version is the most recent. Finally, the example method includes returning the most recent data structure version, notwithstanding that one or more other data structure versions existed at the time that the read request was received.

Eventual consistency in a deduplicated cloud storage system

A deduplicated microservices-based storage system and method running in a cloud includes frontend and backend microservice nodes that process requests from a plurality of clients and interface with a master node and one or more worker nodes connected as a network for deduplicating, storing and retrieving data from object storage. A custom metric compares demand on system resources to thresholds and dynamically scales resources to add or eliminate resources according to demand. The master node partitions the range of values identifying data similarity groups across the worker nodes, reassigns ranges of values using consistent hashing as worker nodes are scaled, and maintains a mapping of ranges of values to worker IP addresses.

Architecture for scalable metadata microservices orchestration

In a deduplicated microservices-based storage system and method running in a cloud includes frontend and backend microservice nodes that process requests from a plurality of clients and interface with a master node and one or more worker nodes connected as a network for deduplicating, storing and retrieving data from object storage, a worker node receives queries and fingerprints for data segments of a data similarity group to be deduplicated, obtains and returns the similarity group from its cache or from object storage using a key. The system compares the received fingerprints to stored fingerprints to determine non-duplicates for deduplication. The worker node updates similarity groups in its cache and in a write-ahead log with a reference to a compression region of non-duplicate segments, and periodically writes modified objects from its cache to object storage.

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San Jose, CA, United States of America

Venkata Ravi Chandra Bandlamudi