Abstract: Efficient data transfer is disclosed. A server provides an endpoint for a client. The client can communicate with a cloud storage system through the server rather than connect directly to the cloud storage system. The server is configured to perform requests in a manner that reduces the amount of data transferred to and from the cloud storage system.

Abstract: Systems and methods enabling garbage collection operations and normal system operations concurrently. Concurrent operations are performed by configuring a similarity group to permit garbage collection and normal operations. This may include creating a new subgroup in a similarity group for write and deduplication purposes such that an impacted subgroup can be cleaned.

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 estimating the number of workers needed to perform a garbage collection operation are disclosed. Similarity groups are used to identify segments associated with objects in a computing system. Using deletion records that identify objects to be deleted, the similarity groups impacted by the deletion records can be identified. The number of workers can be determined based on the impacted similarity groups. More specifically, the number of impacted similarity groups and/or workers can be evaluated in terms of memory requirements, input/output constraints and/or time requirements to estimate the number or workers needed to clean similarity groups impacted by a garbage collection operation.

Abstract: Systems and methods for marking similarity groups impacted by a garbage collection operation are disclosed. Similarity groups are used to identify segments associated with objects in a computing system. Using deletion records that identify objects to be deleted, the similarity groups impacted by the deletion records can be identified. The live segments associated with the impacted similarity groups are also identified. This allows segments that are associated with the deleted objects and that are not associated with any live objects to be removed.

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: One example method includes identifying objects that each include one or more segments to be transferred from a source storage tier to a target storage tier, determining a total amount of data to be transferred, using a tiering controller to create worker nodes operable to transfer the segments to the target storage tier, where a number of worker nodes created is based on the amount of data, transferring, from the source storage tier to the target storage tier, only those segments of the objects not already present in the target storage tier, and the transferring of the segments is performed by the worker nodes, and for each of the objects, placing metadata associated with that object in a bucket.

Abstract: Efficient data transfer is disclosed. A server provides an endpoint for a client. The client can communicate with a cloud storage system through the server rather than connect directly to the cloud storage system. The server is configured to perform requests in a manner that reduces the amount of data transferred to and from the cloud storage system.

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: Efficient data transfer is disclosed. A server provides an endpoint for a client. The client can communicate with a cloud storage system through the server rather than connect directly to the cloud storage system. The server is configured to perform requests in a manner that reduces the amount of data transferred to and from the cloud storage system.

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 estimating the number of workers needed to perform a garbage collection operation are disclosed. Similarity groups are used to identify segments associated with objects in a computing system. Using deletion records that identify objects to be deleted, the similarity groups impacted by the deletion records can be identified. The number of workers can be determined based on the impacted similarity groups. More specifically, the number of impacted similarity groups and/or workers can be evaluated in terms of memory requirements, input/output constraints and/or time requirements to estimate the number or workers needed to clean similarity groups impacted by a garbage collection operation.

Abstract: Systems and methods for estimating the number of workers needed to perform a garbage collection operation are disclosed. Similarity groups are used to identify segments associated with objects in a computing system. Using deletion records that identify objects to be deleted, the similarity groups impacted by the deletion records can be identified. The number of workers can be determined based on the impacted similarity groups. More specifically, the number of impacted similarity groups and/or workers can be evaluated in terms of memory requirements, input/output constraints and/or time requirements to estimate the number or workers needed to clean similarity groups impacted by a garbage collection operation.

Abstract: Systems and methods for estimating the number of workers needed to perform a garbage collection operation are disclosed. Similarity groups are used to identify segments associated with objects in a computing system. Using deletion records that identify objects to be deleted, the similarity groups impacted by the deletion records can be identified. The number of workers can be determined based on the impacted similarity groups. More specifically, the number of impacted similarity groups and/or workers can be evaluated in terms of memory requirements, input/output constraints and/or time requirements to estimate the number or workers needed to clean similarity groups impacted by a garbage collection operation.

Abstract: Systems and methods enabling garbage collection operations and normal system operations concurrently. Concurrent operations are performed by configuring a similarity group to permit garbage collection and normal operations. This may include creating a new subgroup in a similarity group for write and deduplication purposes such that an impacted subgroup can be cleaned.

Abstract: Systems and methods enabling garbage collection operations and normal system operations concurrently. Concurrent operations are performed by configuring a similarity group to permit garbage collection and normal operations. This may include creating a new subgroup in a similarity group for write and deduplication purposes such that an impacted subgroup can be cleaned.

Abstract: Systems and methods for marking similarity groups impacted by a garbage collection operation are disclosed. Similarity groups are used to identify segments associated with objects in a computing system. Using deletion records that identify objects to be deleted, the similarity groups impacted by the deletion records can be identified. The live segments associated with the impacted similarity groups are also identified.

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.