Abstract: Maximum throughput of a storage unit, and workload and latency values of the storage unit corresponding to a predefined fraction of the maximum throughput are estimated based on workloads and latencies that are monitored on the storage unit. The computed metrics are usable in a variety of different applications including admission control, storage load balancing, and enforcing quality of service in a shared storage environment.

Abstract: Methods, systems, and computer programs are presented for allocating CPU cycles in a storage system. One method includes operations for receiving requests to be processed, and for associating each request to one task. A foreground task is for processing input/output requests, and the foreground task includes one or more flows. Each flow is associated with a queue and a flow counter value, where each queue is configured to hold requests. The method further includes an operation for selecting one task for processing by the CPU based on an examination of the number of cycles processed by the CPU for each task. When the selected task is the foreground task, the flow having the lowest flow counter is selected. The CPU processes a request from the queue of the selected flow, and the flow counter of the selected flow is increased based on the data consumption of the processed task.

Abstract: Systems and methods are disclosed that allow for transparently recovering from an uncorrected multi-bit error of arbitrary length located at a memory address. Storing one or more parity pages, for a set of pages in system memory, such that a page in the set of pages may be reconstructed using one of the parity pages is disclosed. Storing an indication of one or more page'disk location such that the one or more pages may be reconstructed by refilling the page from disk is also disclosed.

Abstract: A shared input/output (IO) resource is managed in a decentralized manner. Each of multiple hosts having IO access to the shared resource, computes an average latency value that is normalized with respect to average IO request sizes, and stores the computed normalized latency value for later use. The normalized latency values thus computed and stored may be used for a variety of different applications, including enforcing a quality of service (QoS) policy that is applied to the hosts, detecting a condition known as an anomaly where a host that is not bound by a QoS policy accesses the shared resource at a rate that impacts the level of service received by the plurality of hosts that are bound by the QoS policy, and migrating workloads between storage arrays to achieve load balancing across the storage arrays.

Abstract: Methods, systems, and computer programs are presented for allocating CPU cycles in a storage system. One method includes operations for receiving requests to be processed, and for associating each request to one task. A foreground task is for processing input/output requests, and the foreground task includes one or more flows. Each flow is associated with a queue and a flow counter value, where each queue is configured to hold requests. The method further includes an operation for selecting one task for processing by the CPU based on an examination of the number of cycles processed by the CPU for each task. When the selected task is the foreground task, the flow having the lowest flow counter is selected. The CPU processes a request from the queue of the selected flow, and the flow counter of the selected flow is increased based on the data consumption of the processed task.

Abstract: Maximum throughput of a storage unit, and workload and latency values of the storage unit corresponding to a predefined fraction of the maximum throughput are estimated based on workloads and latencies that are monitored on the storage unit. The computed metrics are usable in a variety of different applications including admission control, storage load balancing, and enforcing quality of service in a shared storage environment.

Abstract: In an example, a method for performing initial placement of a data object in a distributed system that includes a plurality of hardware resources includes receiving a request to create an instance of a data object; determining, in response to the request, a list of hardware resources that satisfy one or more criteria of the data object; creating, in response to the request, a virtual cluster that includes a subset of the hardware resources included in the list of hardware resources; selecting a hardware resource from the virtual cluster into which the data object is to be placed; placing the data object into the hardware resource; and releasing the virtual cluster.

Abstract: A system and method for providing quality of service (QoS) for clients running on host computers to access a common resource uses a resource pool module and a local scheduler in at least one of the host computers. The resource pool module operates to compute an entitlement of each client for the common resource based on a current capacity for the common resource and demands of the clients for the common resource. In addition, the resource pool module operates to assign a portion of the computed current capacity for the common resource to a particular host computer using the computed entitlement of each client running on the particular host computer. The local scheduler operates to allocate the portion of the computed current capacity among the clients running on the particular host computer.

Abstract: A system and method for performing automatic remediation in a distributed computer system with multiple clusters of host computers uses the same placement selection algorithm for initial placements and for remediation placements of clients. The placement selection algorithm is executed to generate a placement solution when a remediation request in response to a remediation-requiring condition in the distributed computer system for at least one client running in one of the multiple clusters of host computers is detected and a remediation placement problem for the client is constructed. The placement solution is then implemented for the client for remediation.

Abstract: Embodiments perform centralized input/output (I/O) path selection for hosts accessing storage devices in distributed resource sharing environments. The path selection accommodates loads along the paths through the fabric and at the storage devices. Topology changes may also be identified and automatically initiated. Some embodiments contemplate the hosts executing a plurality of virtual machines (VMs) accessing logical unit numbers (LUNs) in a storage area network (SAN).

Abstract: Methods, systems, and computer programs are presented for allocating CPU cycles in a storage system. One method includes operations for receiving requests to be processed, and for associating each request to one task. A foreground task is for processing input/output requests, and the foreground task includes one or more flows. Each flow is associated with a queue and a flow counter value, where each queue is configured to hold requests. The method further includes an operation for selecting one task for processing by the CPU based on an examination of the number of cycles processed by the CPU for each task. When the selected task is the foreground task, the flow having the lowest flow counter is selected. The CPU processes a request from the queue of the selected flow, and the flow counter of the selected flow is increased based on the data consumption of the processed task.

Abstract: Methods, systems, and computer programs are presented for allocating CPU cycles and disk Input/Output's (IOs) to resource-creating processes based on dynamic weights that change according to the current percentage of resource utilization in the storage device. One method includes operations for assigning a first weight to a processing task that increases resource utilization of a resource for processing incoming input/output (IO) requests, and for assigning a second weight to a generating task that decreases the resource utilization of the resource. Further, the method includes an operation for dynamically adjusting the second weight based on the current resource utilization in the storage system. Additionally, the method includes an operation for allocating the CPU cycles and disk IOs to the processing task and to the generating task based on their respective first weight and second weight.

Abstract: A system and method for placing a client in a computer network system uses continuously variable weights to resource utilization metrics for each candidate device, e.g., a host computer. The weighted resource utilization metrics are used to compute selection scores for various candidate devices to select a target candidate device for placement of the client.

Abstract: A system and method for allocating a resource among clients running on host computers using capacity distribution uses lower and upper bounds with respect to a capacity to be distributed to each of the clients. Each client is allocated a portion of the capacity that corresponds to the lower bound for that client. Any excess amount of the capacity is then allocated to the clients based at least partly on the lower bound and the upper bound of each of the clients.

Abstract: Systems and techniques are described for allocating data store queues to virtual machines. A described technique includes allocating a respective queue to each of a plurality of threads, wherein the queue is configured to queue data requests from the respective thread and for a first data store, determining, for each of a plurality of threads, a respective maximum quantity of pending requests for the thread, wherein a quantity of pending requests sent from the respective queue to the first data store is equal to the maximum quantity of pending requests determined for the thread, determining, for each of the threads, a respective current quantity of operations per second, determining, for each of one or more first threads in the plurality of threads, a respective updated quantity of pending requests, and adjusting, for each first thread, the quantity of pending requests of the first thread sent to the first data store.

Abstract: A system and method for providing quality of service (QoS) for clients running on host computers to access a common resource uses a resource pool module and a local scheduler in at least one of the host computers. The resource pool module operates to compute an entitlement of each client for the common resource based on a current capacity for the common resource and demands of the clients for the common resource. In addition, the resource pool module operates to assign a portion of the computed current capacity for the common resource to a particular host computer using the computed entitlement of each client running on the particular host computer. The local scheduler operates to allocate the portion of the computed current capacity among the clients running on the particular host computer.

Abstract: Embodiments perform centralized input/output (I/O) path selection for hosts accessing storage devices in distributed resource sharing environments. The path selection accommodates loads along the paths through the fabric and at the storage devices. Topology changes may also be identified and automatically initiated. Some embodiments contemplate the hosts executing a plurality of virtual machines (VMs) accessing logical unit numbers (LUNs) in a storage area network (SAN).

Abstract: In an example, a method for performing initial placement of a data object in a distributed system that includes a plurality of hardware resources includes receiving a request to create an instance of a data object; determining, in response to the request, a list of hardware resources that satisfy one or more criteria of the data object; creating, in response to the request, a virtual cluster that includes a subset of the hardware resources included in the list of hardware resources; selecting a hardware resource from the virtual cluster into which the data object is to be placed; placing the data object into the hardware resource; and releasing the virtual cluster.

Abstract: A management server and method for performing resource management operations in a distributed computer system uses at least one sampling parameter to estimate demand of a client for a resource. The sampling parameter has a correlation with at least one target performance goal of an application that the client is running. The demand estimation can then be used to make at least one decision in a resource management operation.

Abstract: Systems and techniques are described for allocating data store queues to virtual machines. A described technique includes allocating a respective queue to each of a plurality of threads, wherein the queue is configured to queue data requests from the respective thread and for a first data store, determining, for each of a plurality of threads, a respective maximum quantity of pending requests for the thread, wherein a quantity of pending requests sent from the respective queue to the first data store is equal to the maximum quantity of pending requests determined for the thread, determining, for each of the threads, a respective current quantity of operations per second, determining, for each of one or more first threads in the plurality of threads, a respective updated quantity of pending requests, and adjusting, for each first thread, the quantity of pending requests of the first thread sent to the first data store.