Abstract: Disclosed is a system for storage management comprising: receiving capacity information within a predetermined time period from a plurality of storage in a first appliance pool and a second appliance pool, each having a plurality of storage systems; tracking a first capacity limit of the first storage system in the first appliance pool based on the capacity information; performing a first capacity prediction of the first storage system based on the tacked result of the first storage system, wherein the first capacity prediction indicating when the first storage system will be out of space based on the capacity information and the tracked result; generating a first recommendation indicating when an extra capacity of the first storage system should be added based on one or more attributes including the first capacity prediction; and presenting the first recommendation with one or more options.

Abstract: Disclosed is a system for storage management comprising: receiving capacity information within a predetermined time period from a plurality of storage in a first appliance pool and a second appliance pool, each having a plurality of storage systems; tracking a first capacity limit of the first storage system in the first compliance pool based on the capacity information; performing a first capacity prediction of the first storage system based on the tacked result of the first storage system, wherein the first capacity prediction indicating when the first storage system will be out of space based on the capacity information and the tracked result; generating a first recommendation indicating when an extra capacity of the first storage system should be added based on one or more attributes including the first capacity prediction; and presenting the first recommendation with one or more options.

Abstract: Described is a system for a providing a service (or microservice) for performing deduplication for an object storage. The service (or microservice) may be source-agnostic in that it may receive data from multiple types of source systems by providing a uniform set of functions for deduplicating and writing the data to a destination object storage. The set of functions encapsulate a previously dispersed set of functionality provided by various components. Accordingly, the service provides a single scalable and stateless component for performing deduplication. For example, the service (e.g. deduplication service) may receive object related information and perform a filtering to accelerate network transfers. Accordingly, the service provides the ability to only transfer and write data that does not already exist on the object storage.

Abstract: Described is a system for a providing a service (or microservice) for performing deduplication for an object storage. The service (or microservice) may be source-agnostic in that it may receive data from multiple types of source systems by providing a uniform set of functions for deduplicating and writing the data to a destination object storage. The set of functions encapsulate a previously dispersed set of functionality provided by various components. Accordingly, the service provides a single scalable and stateless component for performing deduplication. For example, the service (e.g. deduplication service) may receive object related information and perform a filtering to accelerate network transfers. Accordingly, the service provides the ability to only transfer and write data that does not already exist on the object storage.

Abstract: Systems and methods for cleaning a storage system. A deduplicated storage system is cleaned by identifying structures that include dead or unreferenced segments. This includes processing recipes to identify the segments that are no longer part of a live object recipe. Then, the dead segments are removed. This is accomplished by copying forward the live segments and then deleting, as a whole, the structure that included the dead segments.

Abstract: Systems and methods for cleaning a storage system. A deduplicated storage system is cleaned by identifying structures that include dead or unreferenced segments. This includes processing recipes to identify the segments that are no longer part of a live object recipe. Then, the dead segments are removed. This is accomplished by copying forward the live segments and then deleting, as a whole, the structure that included the dead segments.

Abstract: 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.

Abstract: Systems and methods for cleaning a storage system. A deduplicated storage system is cleaned by identifying structures that include dead or unreferenced segments. This includes processing recipes to identify the segments that are no longer part of a live object recipe. Then, the dead segments are removed. This is accomplished by copying forward the live segments and then deleting, as a whole, the structure that included the dead segments.

Abstract: 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.