Abstract:
An embodiment of the invention provides an apparatus and method for classifying a workload of a computing entity. In an embodiment, the computing entity samples a plurality of values for a plurality of parameters of the workload. Based on the plurality of values of each parameter, the computing entity determines a parameter from the plurality of parameters that the computing entity's response time is dependent on. Here, the computing entity's response time is indicative of a time required by the computing entity to respond to a service request from the workload. Further, based on the identified significant parameter, the computing entity classifies the workload of the computing entity by selecting a workload classification from a plurality of predefined workload classifications.
Abstract:
Technology is disclosed for managing network storage services by service level objectives (SLOs). The method receives multiple service level capability (SLC) templates; creates at least one storage service level (SSL) instance using at least one of the SLC templates; provisions a storage object located in a network storage infrastructure based on the SSL instance; and services storage requests using the storage object.
Abstract:
A distributed object store in a network storage system uses location-independent global object identifiers (IDs) for stored data objects. The global object ID enables a data object to be seamlessly moved from one location to another without affecting clients of the storage system, i.e., “transparent migration”. The global object ID can be part of a multilevel object handle, which also can include a location ID indicating the specific location at which the data object is stored, and a policy ID identifying a set of data management policies associated with the data object. The policy ID may be associated with the data object by a client of the storage system, for example when the client creates the object, thus allowing “inline” policy management. An object location subsystem (OLS) can be used to locate an object when a client request does not contain a valid location ID for the object.
Abstract:
A change in workload characteristics detected at one tier of a multi-tiered cache is communicated to another tier of the multi-tiered cache. Multiple caching elements exist at different tiers, and at least one tier includes a cache element that is dynamically resizable. The communicated change in workload characteristics causes the receiving tier to adjust at least one aspect of cache performance in the multi-tiered cache. In one aspect, at least one dynamically resizable element in the multi-tiered cache is resized responsive to the change in workload characteristics.
Abstract:
Collaborative management of shared resources is implemented by a storage server receiving, from a first resource manager, notification of a violation for a service provided by the storage server or device coupled to the storage server. The storage server further receives, from each of a plurality of resource managers, an estimated cost of taking a corrective action to mitigate the violation and selects a corrective action proposed by one of the plurality of resource managers based upon the estimated cost. The storage server directs the resource manager that proposed the selected corrective action to perform the selected corrective action.
Abstract:
One or more techniques and/or systems are provided for migrating a dataset from a file storage system to an object storage system. That is, a snapshot of a file system may be received from the file storage system. The snapshot may comprise file data associated with a file of the file system. The file may be converted into an object using the file data. The object may be stored within a data constituent volume of the object storage system. A namespace volume, used to track objects, may be populated with a redirector that maps a front-end data path (e.g., a path used by clients to reference the object) to a back-end data path that specifies a path to the object within the data constituent volume. In this way, a dataset of one or more files may be migrated from the file storage system to the object storage system.
Abstract:
One or more techniques and/or systems are provided for migrating a dataset from a file storage system to an object storage system. That is, a snapshot of a file system may be received from the file storage system. The snapshot may comprise file data associated with a file of the file system. The file may be converted into an object using the file data. The object may be stored within a data constituent volume of the object storage system. A namespace volume, used to track objects, may be populated with a redirector that maps a front-end data path (e.g., a path used by clients to reference the object) to a back-end data path that specifies a path to the object within the data constituent volume. In this way, a dataset of one or more files may be migrated from the file storage system to the object storage system.
Abstract:
Embodiments of the systems and techniques described here can leverage several insights into the nature of workload access patterns and the working-set behavior to reduce the memory overheads. As a result, various embodiments make it feasible to maintain running estimates of a workload's cacheability in current storage systems with limited resources. For example, some embodiments provide for a method comprising estimating cacheability of a workload based on a first working-set size estimate generated from the workload over a first monitoring interval. Then, based on the cacheability of the workload, a workload cache size can be determined. A cache then can be dynamically allocated (e.g., change, possibly frequently, the cache allocation for the workload when the current allocation and the desired workload cache size differ), within a storage system for example, in accordance with the workload cache size.
Abstract:
Described herein is a system and method for dynamically managing service-level objectives (SLOs) for workloads of a cluster storage system. Proposed states/solutions of the cluster may be produced and evaluated to select one that achieves the SLOs for each workload. A planner engine may produce a state tree comprising nodes, each node representing a proposed state/solution. New nodes may be added to the state tree based on new solution types that are permitted, or nodes may be removed based on a received time constraint for executing a proposed solution or a client certification of a solution. The planner engine may call an evaluation engine to evaluate proposed states, the evaluation engine using an evaluation function that considers SLO, cost, and optimization goal characteristics to produce a single evaluation value for each proposed state. The planner engine may call a modeler engine that is trained using machine learning techniques.
Abstract:
An embodiment of the invention provides an apparatus and method for classifying a workload of a computing entity. In an embodiment, the computing entity samples a plurality of values for a plurality of parameters of the workload. Based on the plurality of values of each parameter, the computing entity determines a parameter from the plurality of parameters that the computing entity's response time is dependent on. Here, the computing entity's response time is indicative of a time required by the computing entity to respond to a service request from the workload. Further, based on the identified significant parameter, the computing entity classifies the workload of the computing entity by selecting a workload classification from a plurality of predefined workload classifications.