Abstract:
A method for analyzing a read error event is provided comprising reading a page of data stored in memory, determining a read error event for the page of data, and identifying a scope of the read error event in the memory. In another embodiment, a method for performing a preliminary read error recovery is provided comprising reading a first data unit from memory and identifying a bit error rate for a first data unit with a correction engine, determining that the bit error rate is above a threshold, accessing a data structure including entries identifying data units and read error event information associated with the data units, identifying a second data unit in an entry that matches the first data unit, and performing a preliminary read error recovery process on the first data unit using the information in the entry to reduce the bit error rate below the threshold.
Abstract:
Embodiments provide workload processing for clustered systems. In an illustrative, non-limiting embodiment, a computer-implemented method may include identifying a server as an active node of a cluster; assigning a workload to the server in response to the identification; determining, after the assignment, that the server is no longer an active node of the cluster; calculating, in response to the determination, a probability that the server is capable of continuing to execute the workload; and deciding, based upon the probability, whether to allow the workload to remain assigned to the server.
Abstract:
Provided are a computer program product, system, and method for determining server write activity levels to use to adjust write cache size. Server write activity information on server write activity to the cache is gathered. The server write activity information is processed to determine a server write activity level comprising one of multiple write activity levels indicating a level of write activity. The determined server write activity level is transmitted to a storage server having a write cache, wherein the storage server uses the determined server write activity level to determine whether to adjust a size of the storage server write cache.
Abstract:
Idle detection techniques are disclosed. A set of idle conditions that includes one or more conditions not comprising or triggered by an absence of user input is monitored. The device is determined to be idle based at least in part on results of the monitoring. The device may be determined not to be idle even in the absence of recent user input.
Abstract:
An apparatus and method for a user configurable reliability control loop. For example, one embodiment of a processor comprises: a reliability meter to track accumulated stress on components of the processor based on measured processor operating conditions; and a controller to receive stress rate limit information from a user or manufacturer and to responsively specify a set of N operating limits on the processor in accordance with the accumulated stress and the stress rate limit information; and performance selection logic to output one or more actual operating conditions for the processor based on the N operating limits specified by the controller.
Abstract:
Apparatuses, systems, methods, and computer program products for auto-commit memory are presented. A monitor module determines that a triggering event for an auto-commit memory has occurred. An identification module identifies a triggered commit action for an auto-commit memory. An auto-commit memory module performs a triggered commit action for an auto-commit memory in response to a triggering event occurring.
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:
Methods, apparatus, systems and articles of manufacture are disclosed to validate translated guest code in a dynamic binary translator. An example apparatus disclosed herein includes a translator to generate a first translation of code to execute on a host machine, the first translation of the guest code to facilitate creating a first translated guest code, and the translator to generate a second translation of the translated guest code to execute on the host machine. The example apparatus also includes a translation versions manager to identify a first host machine state based on executing a portion of the first translation, and the translation versions manager to identify a second host machine state based on executing a portion of the second translation. The example system also includes a validator to determine a state divergence status of the second translation based on a comparison between the first host machine state and the second host machine state.
Abstract:
A processor monitors, directly or indirectly, the amount of time it takes for the memory controller to respond to one or more memory access requests. When this memory access latency indicates that a memory latency tolerance of a program thread has been exceeded, the processor can apportion additional power to the memory controller, thereby increasing the speed with which the memory controller can process memory access requests.
Abstract:
A computer determines that a utilization level of a resource has satisfied a threshold. The computer scales the allocation of the resource to the furthest of the current allocation of the resource plus a parameter and of a historical limit. The computer determines if the scaled allocation of the resource is outside the historical limit and if so, sets the historical limit equal to the scaled allocation of the resource. The computer determines whether the scaling of the allocation of the resource will result in an allocation oscillation. The computer determines if the scaled allocation of the resource is outside a boundary parameter and if so, sets the allocation of the resource equal to the boundary parameter.