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 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: 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: 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 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 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 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: Write performance is accelerated in a microservices deduplication system having a master node and a worker node, where the worker node receives and writes updates to data similarity groups to a cache and to a primary write-ahead log. Periodically, the worker writes updated similarity groups from cache to object storage, and flushes the contents of the primary write-ahead log. During flushing of the primary write-ahead log, the worker writes similarity group updates to a secondary write-ahead log which is swapped for the primary write-ahead log and which becomes a new primary write-ahead log for updates during flushing and during a subsequent time period. The previous primary write-ahead log becomes the new secondary write-ahead log, and the log swapping is repeated during subsequent time periods.