Abstract:
It is detected that a metric associated with a first workload has breached a first threshold. It is determined that the first workload and a second workload access the same storage resources, wherein the storage resources are associated with a storage server. It is determined that the metric is impacted by the first workload and the second workload accessing the same storage resources. A candidate solution is identifier. An estimated impact of a residual workload is determined based, at least in part, on the candidate solution. A level of caching of at least one of the first workload or the second workload is adjusted based, at least in part, on the estimated impact of the residual workload.
Abstract:
Technology is described for a profile-based lifecycle management for data storage servers. The technology can receive a profile, monitor events emitted by devices of the data storage system, determine based on the monitored events that a device of the storage system matches the indicated condition, and perform the action corresponding to the indicated condition, wherein the action includes managing data stored by the data storage system. The received profile can indicate a condition and an action corresponding to the condition.
Abstract:
A dynamic caching technique adaptively controls copies of data blocks stored within caches (“cached copies”) of a caching layer distributed among servers of a distributed data processing system. A cache coordinator of the distributed system implements the dynamic caching technique to increase the cached copies of the data blocks to improve processing performance of the servers. Alternatively, the technique may decrease the cached copies to reduce storage capacity of the servers. The technique may increase the cached copies when it detects local and/or remote cache bottleneck conditions at the servers, a data popularity condition at the servers, or a shared storage bottleneck condition at the storage system. Otherwise, the technique may decrease the cached copies at the servers.
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:
Technology is described for actively responding to data storage traffic. The technology can provide an application program interface; receive, via the application program interface, from an application, a command to query a data storage attribute associated with a virtual data storage component; query the associated virtual data storage component; and return to the application a value for the data storage attribute.
Abstract:
Technology is described for a profile-based lifecycle management for data storage servers. The technology can receive a profile, monitor events emitted by devices of the data storage system, determine based on the monitored events that a device of the storage system matches the indicated condition, and perform the action corresponding to the indicated condition, wherein the action includes managing data stored by the data storage system. The received profile can indicate a condition and an action corresponding to the condition.
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:
Technology is described for a profile-based lifecycle management for data storage servers. The technology can receive a profile, monitor events emitted by devices of the data storage system, determine based on the monitored events that a device of the storage system matches the indicated condition, and perform the action corresponding to the indicated condition, wherein the action includes managing data stored by the data storage system. The received profile can indicate a condition and an action corresponding to the condition.
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.