Abstract: The techniques introduced here provide for efficient management of storage resources in a modern, dynamic data center through the use of virtual storage appliances. Virtual storage appliances perform storage operations and execute in or as a virtual machine on a hypervisor. A storage management system monitors a storage system to determine whether the storage system is satisfying a service level objective for an application. The storage management system then manages (e.g., instantiates, shuts down, or reconfigures) a virtual storage appliance on a physical server. The virtual storage appliance uses resources of the physical server to meet the storage related needs of the application that the storage system cannot provide. This automatic and dynamic management of virtual storage appliances by the storage management system allows storage systems to quickly react to changing storage needs of applications without requiring expensive excess storage capacity.

Abstract: Technology for operating a cache sizing system is disclosed. In various embodiments, the technology monitors input/output (IO) accesses to a storage system within a monitor period; tracks an access map for storage addresses within the storage system during the monitor period; and counts a particular access condition of the IO accesses based on the access map during the monitor period. When sizing a cache of the storage system that enables the storage system to provide a specified level of service, the counting is for computing a working set size (WSS) estimate of the storage system.

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 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 for operating a cache sizing system is disclosed. In various embodiments, the technology monitors input/output (IO) accesses to a storage system within a monitor period; tracks an access map for storage addresses within the storage system during the monitor period; and counts a particular access condition of the IO accesses based on the access map during the monitor period. When sizing a cache of the storage system that enables the storage system to provide a specified level of service, the counting is for computing a working set size (WSS) estimate of the storage system.

Abstract: The techniques introduced here provide for efficient management of storage resources in a modern, dynamic data center through the use of virtual storage appliances. Virtual storage appliances perform storage operations and execute in or as a virtual machine on a hypervisor. A storage management system monitors a storage system to determine whether the storage system is satisfying a service level objective for an application. The storage management system then manages (e.g., instantiates, shuts down, or reconfigures) a virtual storage appliance on a physical server. The virtual storage appliance uses resources of the physical server to meet the storage related needs of the application that the storage system cannot provide. This automatic and dynamic management of virtual storage appliances by the storage management system allows storage systems to quickly react to changing storage needs of applications without requiring expensive excess storage capacity.

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: Method and system for distributing a notification of an event occurring at a one entity to an affected entity. In one embodiment, entities are grouped into one or more groups so that an event occurring at one entity of the group is distributed to the other entities of the group (“affected entities”). If an event occurs at an entity managed by a local entity manager, a notification of the event can be routed to a remote entity manager that manages an affected entity. The local entity manager can use a distributed mapping to determine where to route the event notification. In one embodiment the mapping associates an entity to an entity manager.

Abstract: Graph transformations are used by a data management system to correct violations of service-level objectives (SLOs) in a data center. In one aspect, a process is provided to manage a data center by receiving an indication of a violation of a service-level objective associated with the data center from a server in the data center. A graph representation and a transformations data container are retrieved by the data management system from data storage accessible to the data management system. The transformations data container includes one or more transformations. The transformation is processed to create a mutated graph from a data center representation from the graph representation. An option for managing the data center is determined as a result of evaluating the mutated graphs.

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: 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: 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: 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: Graph transformations are used by a data management system to correct violations of service-level objectives (SLOs) in a data center. In one aspect, a process is provided to manage a data center by receiving an indication of a violation of a service-level objective associated with the data center from a server in the data center. A graph representation and a transformations data container are retrieved by the data management system from data storage accessible to the data management system. The transformations data container includes one or more transformations. The transformation is processed to create a mutated graph from a data center representation from the graph representation. An option for managing the data center is determined as a result of evaluating the mutated graphs.

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: Graph transformations are used by a data management system to correct violations of service-level objectives (SLOs) in a data center. In one aspect, a process is provided to manage a data center by receiving an indication of a violation of a service-level objective associated with the data center from a server in the data center. A graph representation and a transformations data container are retrieved by the data management system from data storage accessible to the data management system. The transformations data container includes one or more transformations. The transformation is processed to create a mutated graph from a data center representation from the graph representation. An option for managing the data center is determined as a result of evaluating the mutated graphs.

Abstract: A service level objective (SLO) violation is detected for a workload of a networked storage system, based on a performance metric not being satisfied for the workload. In response to detecting the SLO violation, a controller determines that changing a level of caching at a node of the networked storage system will improve the performance metric for the workload. The controller implements the change by adjusting an operation of a virtual cache appliance (VCA) of the networked storage system. The adjusting can be instantiating a new VCA, or adjusting the level of caching at an existing VCA. The adjusting can be for caching related to the workload itself, or it can be caching for an intertering workload.

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: 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.