Abstract: An exemplary embodiment of the present invention provides an improved yardage book holder for storing a yardage book as well as a variety of other golf accessories. The yardage book holder comprises a substantially slender thickness and a rectangular profile with rounded corners that allows a golfer to conveniently store the yardage book holder within a pocket in an article of clothing when folded in its fully closed position. The yardage book holder further comprises at least two layers of materials, a plurality of sections, a plurality of compartments, and a strap affixed to one of the sides of the yardage book holder. The strap allows for quick access to the item which it secures and is able to pivot such that it may quickly attach to or detach from the interior or exterior surfaces of the yardage book.

Abstract: A thermostat includes a housing having a base, a display mount cantilevered from the base, and a case defining an interior volume extending between a front surface and a rear surface, a touch-sensitive display configured to display visual media and receive user inputs, wherein the touch-sensitive display is attached to the display mount, processing electronics on a circuit board positioned at least partially within the interior volume of the case, wherein the processing electronics are configured to operate the touch-sensitive display, and a mounting bracket configured to attach to a mounting location, wherein the mounting bracket includes a frame defining an aperture and the case extends through the aperture so that the frame is located between the front and rear surfaces of the case.

Abstract: A computer storage system includes a controller, a first storage device and a second storage device including at least one fast storage device. The controller is configured to perform data operations. The first storage device stores data, and the second storage device stores data redundant to the data stored in the first storage device.

Abstract: A quality-of-service controller and related method for a data storage system. Requests for each of a plurality of storage system workloads are prioritized. The requests are selectively forwarded to a storage device queue according to their priorities so as to maintain the device queue at a target queue depth. The target queue depth is adjusted response to a latency value for the requests wherein the latency value is computed based on a difference between an arrival time and a completion time of the requests for each workload. Prioritizing the requests may be accomplished by computing a target deadline for a request based on a monitored arrival time of the request and a target latency for its workload. To reduce latencies, the target queue depth may be reduced when the target latency for a workload is less than its computed latency value. To increase throughput, the target queue depth may be increased when the target latency for each workload is greater than each computed latency value.

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 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: A technique for programmatically obtaining experimental measurements for model construction. A user provides criteria for the model, such as computational algorithms which characterize behavior of the real system, specifications of experiments to be performed on the real system for collecting experimental data from the real system, an identification of sought parameters which are to be derived from results of the experiments and desired tolerance constraints on the sought parameters. From experimental data collected from the real system and from the provided criteria, the inventive method and apparatus programmatically determines in an iterative loop which additional experiments are to be performed in order to achieve the desired tolerance constraints. After one or more iterations of the loop, the values for the sought parameters are determined within the desired tolerance constraints.

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.