Abstract: A computer storage system includes a controller and a storage device array. The storage device array may include a first sub-array and a fast storage device sub-array. The first sub-array includes one or more first storage devices storing data. The fast storage device sub-array includes one or more fast storage devices storing a copy of the data stored in the first sub-array.

Abstract: A technique of allocating shared resources in a computer network-based storage system comprises taking periodic performance samples on a running storage system; evaluating an objective function that takes as input the performance samples to quantify how aligned a current state of the storage system is with organizational objectives; building and maintaining models of behavior and capabilities of the storage system by using the performance samples as input; determining how resources of the storage system should be allocated among client computers in the storage system by selecting one among many possible allocations based on predictions generated by the models in order to maximize a value of the objective function; calculating a confidence statistic value for a chosen resource allocation based on an accuracy of the models; and enforcing the chosen resource allocation on the running storage system when the confidence statistic value is at or above a predetermined threshold value.

Abstract: Embodiments herein present a method, system, computer program product, etc. for automated management using a hybrid of prediction models and feedback-based systems. The method begins by calculating confidence values of models. Next, the method selects a first model based on the confidence values and processes the first model through a constraint solver to produce first workload throttling values. Following this, workloads are repeatedly processed through a feedback-based execution engine, wherein the feedback-based execution engine is controlled by the first workload throttling values. The first workload throttling values are applied incrementally to the feedback-based execution engine, during repetitions of the processing of the workloads, with a step-size that is proportional to the confidence values. The processing of the workloads is repeated until an objective function is maximized, wherein the objective function specifies performance goals of the workloads.

Abstract: Data is migrated by moving a set of the data in a data storage system of a computer system. The method includes monitoring a performance of at least one executing application while moving the data in the data storage system. A change in the rate of moving is calculated in response to the monitored performance of the executing application. The rate of moving is modified in accordance with the calculated change.

Abstract: A computer storage system includes a controller and a storage device array. The storage device array includes a first sub-array and a fast storage device sub-array. The first sub-array includes one or more log-structured storage devices storing data. The fast storage device sub-array includes one or more fast storage devices storing a copy of the data stored in the first sub-array.

Abstract: A method predicts performance of a system that includes a plurality of interconnected components defining at least one data flow path. The method references a workload specification for the system. The method models the system using one or more component models. Each component model represents selected one or more of the components. Each component model is arranged in like relationship to the data flow path as the selected one or more of the components represented by the component model. Each component model is (a) a constraint upon the workload specification input to that component model or (b) a transformer of the workload specification input to that component model so as to result in one or more output workload specifications that are input workload specifications to subsequent component models along the data flow path or (c) both a constraint and a transformer. At least one of the component models is a constraint.

Abstract: A method of and apparatus for determining whether a multi-component target system meets a given multi-part performability requirement is provided. A description of the target system, failure probabilities for components of the target system and a multi-part performability requirement for the target system are obtained. The multi-part performability requirement indicates desired performance levels and corresponding fractions of time. One or more failure-scenarios are successively computed that represent one or more states of the target system having zero or more components failed and a corresponding probability of occurrence of the one or more of the states of the target system. Performance of the target system is modeled under the failure scenarios using a performance predictor module for generating a multi-part performability function.

Abstract: A computer storage system includes a controller and a storage device array. The storage device array may include a first sub-array and a fast storage device sub-array. The first sub-array includes one or more first storage devices storing data. The fast storage device sub-array includes one or more fast storage devices storing a copy of the data stored in the first sub-array.

Abstract: A computer storage system includes a controller and a storage device array. The storage device array may include a first sub-array and a fast storage device sub-array. The first sub-array includes one or more first storage devices storing data. The fast storage device sub-array includes one or more fast storage devices storing a copy of the data stored in the first sub-array.

Abstract: A technique for emulation of a data storage system. The invention allows the level of services to be provided by a data storage system to be specified in terms of the level of services provided by another storage system. In one aspect, a performance characterization of a data storage device to be emulated is obtained (e.g., by experimental techniques). A specification of a workload is also obtained that includes a specification of a plurality of data stores for the workload. The data stores are assigned to an emulation data storage device according to the performance characterization and according to the specification of the workload such that sufficient resources of the emulation data storage device are allocated to the workload to meet the performance characterization of the data storage device to be emulated. The emulation data storage device is then operated under the workload. Quality-of-service (QoS) control may be performed so as to provide a degree of performance isolation among the workloads.

Abstract: A computer storage system includes a controller and a storage device array. The storage device array includes a first sub-array and a fast storage device sub-array. The first sub-array includes one or more log-structured storage devices storing data. The fast storage device sub-array includes one or more fast storage devices storing a copy of the data stored in the first sub-array.

Abstract: The present invention is referred as a formula or combination of two compounds, one of them being a non-steroidal anti-inflammatory such as ketorolac, and the other an opiate analgesic known as tramadol, which are formulated in capsules.

Abstract: RAID levels are assigned to data prior to loading the data on a disk array or other data storage device. The RAID levels are determined by applying an algorithm (e.g., a rule-based criteria, a utilization model) to data workload specifications and device specifications.

Abstract: A computer storage system includes a controller and a storage device array. The storage device array may include a first sub-array and a fast storage device sub-array. The first sub-array includes one or more first storage devices storing data. The fast storage device sub-array includes one or more fast storage devices storing a copy of the data stored in the first sub-array.

Abstract: RAID levels are assigned to data prior to loading the data on a disk array or other data storage device. The RAID levels are determined by applying an algorithm (e.g., a rule-based criteria, a utilization model) to data workload specifications and device specifications.

Abstract: Given a multi-component system and a multi-part performability requirement, a method and apparatus for determining whether the system will satisfy the multi-part performability requirement is described. The method uses a failure-scenario generator that analyzes the most-likely failure scenarios first and operates only until a PASS-FAIL determination can be rendered.

Abstract: A method and system for a migration executor of an on-line data migration engine is utilized to provide data migration of a user's data in a data storage system while maintaining application performance guarantees. In particular, the migration executor includes a monitor module configured to monitor performance metrics of a computer system executing foreground applications and the on-line data migration, where quality-of-service parameters computed from the performance metrics are forwarded to a controller module. The controller module of the migration executor is configured to compare the quality-of-service parameters of the computer system with performance goals for the foreground applications. The controller module may be further configured to issue commands to an actuator module that is configured to increase or decrease the rate of the data migration, where the actuator module is further configured to perform the data migration by executing a migration plan.

Abstract: A method predicts performance of a system that includes a plurality of interconnected components defining at least one data flow path. The method references a workload specification for the system. The method models the system using one or more component models. Each component model represents selected one or more of the components. Each component model is arranged in like relationship to the data flow path as the selected one or more of the components represented by the component model. Each component model is (a) a constraint upon the workload specification input to that component model or (b) a transformer of the workload specification input to that component model so as to result in one or more output workload specifications that are input workload specifications to subsequent component models along the data flow path or (c) both a constraint and a transformer. At least one of the component models is a constraint.