Abstract: An example system may be configured to instantiate a first application container based on a plurality of application image layers; and instantiate a second application container based, at least in part, on the plurality of application image layers; wherein a private page cache stores storage information for the plurality of application image layers and is used to provide shared access to the plurality of application image layers by the first application container and the second application container.

Abstract: An intelligent method of scheduling garbage collection (GC) in a storage system. A GC scheduler obtains capacity utilization and ingest rate of the storage system and calculate therefrom a predicted capacity utilization. When the predicted capacity utilization reaches a threshold, the GC scheduler invokes GC, but otherwise skips GC until such time as predicted capacity utilization reaches the threshold. The ingest rage may be calculated by performing linear fit on past data ingest. The GC scheduler may calculate predicted capacity utilization periodically according to preset period. The GC scheduler may calculate the predicted capacity utilization to a future date beyond the next period. The future date may be at least as far as the next period plus total ingest time.

Abstract: An example system may be configured to instantiate a first application container based on a plurality of application image layers; and instantiate a second application container based, at least in part, on the plurality of application image layers; wherein a private page cache stores storage information for the plurality of application image layers and is used to provide shared access to the plurality of application image layers by the first application container and the second application container.

Abstract: An intelligent method of scheduling garbage collection (GC) in a storage system. A GC scheduler obtains capacity utilization and ingest rate of the storage system and calculate therefrom a predicted capacity utilization. When the predicted capacity utilization reaches a threshold, the GC scheduler invokes GC, but otherwise skips GC until such time as predicted capacity utilization reaches the threshold. The ingest rage may be calculated by performing linear fit on past data ingest. The GC scheduler may calculate predicted capacity utilization periodically according to preset period. The GC scheduler may calculate the predicted capacity utilization to a future date beyond the next period. The future date may be at least as far as the next period plus total ingest time.

Abstract: A custom file system in a containerized software architecture facilitates the instantiation of application containers. Each container is composed of one or more application image layers. An application container instance includes read-only application image layer data shared among application container instances associated with the same application container. An application container instance may also include read/write application container instance data that is specific to the application container instance.

Abstract: Techniques and mechanisms described herein provide for a custom file system in a containerized application system in which an application instance may be created as a container based on an application image, which itself may be composed of some number of application image layers. By being aware of these application image layers, the file system can perform image layer level operations such as image layer multisourcing and image layer garbage collection.

Abstract: A custom file system in a containerized software architecture facilitates the instantiation of application containers. Each container is composed of one or more application image layers. An application container instance includes read-only application image layer data shared among application container instances associated with the same application container. An application container instance may also include read/write application container instance data that is specific to the application container instance.

Abstract: A custom file system in a containerized software architecture facilitates the instantiation of application containers. Each container is composed of one or more application image layers. An application container instance includes read-only application image layer data shared among application container instances associated with the same application container. An application container instance may also include read/write application container instance data that is specific to the application container instance.

Abstract: A custom file system in a containerized software architecture facilitates the instantiation of application containers. Each container is composed of one or more application image layers. An application container instance includes read-only application image layer data shared among application container instances associated with the same application container. An application container instance may also include read/write application container instance data that is specific to the application container instance.

Abstract: Techniques and mechanisms described herein provide for a custom file system in a containerized application system in which an application instance may be created as a container based on an application image, which itself may be composed of some number of application image layers. By being aware of these application image layers, the graph driver can perform image layer level operations such as selectively updating image layers, storing image layers in a way that reflects class of service objectives, and responding to application level queries.

Abstract: A custom file system in a containerized software architecture facilitates the instantiation of application containers. Each container is composed of one or more application image layers. An application container instance includes read-only application image layer data shared among application container instances associated with the same application container. An application container instance may also include read/write application container instance data that is specific to the application container instance.

Abstract: A custom file system in a containerized software architecture facilitates the instantiation of application containers. Each container is composed of one or more application image layers. An application container instance includes read-only application image layer data shared among application container instances associated with the same application container. An application container instance may also include read/write application container instance data that is specific to the application container instance.

Abstract: Techniques and mechanisms described herein provide for a custom file system in a containerized application system in which an application instance may be created as a container based on an application image, which itself may be composed of some number of application image layers. By being aware of these application image layers, the graph driver can perform image layer level operations such as selectively updating image layers, storing image layers in a way that reflects class of service objectives, and responding to application level queries.

Abstract: Techniques and mechanisms described herein provide for a custom file system in a containerized application system in which an application instance may be created as a container based on an application image, which itself may be composed of sonic number of application image layers. By being aware of these application image layers, the file system can perform image layer level operations such as image layer multisourcing and image layer garbage collection.

Abstract: Implementations are provided herein for incremental backup using a change tree. A change tree is a database file for tracking file system changes of a specific dataset, such as a directory of a file system. A backup process can create and/or initiate a change tree when creating a backup snapshot of a dataset. After the change tree is created, all file system changes inside the dataset can be tracked. The next incremental backup can then take advantage of the change tree to backup changes without traversing the entire dataset. Thus, incremental backups can be more efficient and are better able to keep up with tape streaming speed.