Abstract: In the embodiment a determination is made, for one or more applications being executed by the computing system, of an amount of the first or second memory being used by the one or more applications. Based on the determination, a portion of the memory resources of the third memory are configured to function with the first or second memory when it is determined that the amount of the first or second memory being used by the one or more applications is not sufficient for the memory needs of the one or more applications and a portion of the memory resources of the third memory are removed from functioning with the first or second memory when it is determined that the amount of the first or second memory being used by the one or more applications is more than is needed for the memory needs of the one or more applications.

Abstract: In the embodiment a determination is made, for one or more applications being executed by the computing system, of an amount of the first or second memory being used by the one or more applications. Based on the determination, a portion of the memory resources of the third memory are configured to function with the first or second memory when it is determined that the amount of the first or second memory being used by the one or more applications is not sufficient for the memory needs of the one or more applications and a portion of the memory resources of the third memory are removed from functioning with the first or second memory when it is determined that the amount of the first or second memory being used by the one or more applications is more than is needed for the memory needs of the one or more applications.

Abstract: Aspects extend to methods, systems, and computer program products for predicting solid state drive reliability. Aspects of the invention can be used to predict and/or to configure a data center to minimize one or more of: SSD capacity degradation (how much storage an SSD has left), SSD performance degradation (reduced read/write latency/throughput), and SSD failure. Models and data center considerations can be based on device level SSD related operations, such as, for example, read, write, erase. Operations decisions can be made for a data center based on SSD specific features, such as, for example, remaining capacity, write amplification factor, etc. Dependence and/or causality of various different data center factors can be leveraged. The impact of the various data center factors on different SSD failure modes and capacity/performance degradation can be quantified to drive SSD design, SSD provisioning, and SSD operations.

Abstract: Operations of a variety of components of a storage system stack are redefined to make the system more efficient when the underlying media has a “multi-log” type interface such as the case with NAND flash SSD memory or shingled magnetic recording media. The responsibilities of components of the storage system stack are modified such that each responsibility is performed at the most efficient component (level of abstraction) of the storage stack.

Abstract: Operations of a variety of components of a storage system stack are redefined to make the system more efficient when the underlying media has a “multi-log” type interface such as the case with NAND flash SSD memory or shingled magnetic recording media. The responsibilities of components of the storage system stack are modified such that each responsibility is performed at the most efficient component (level of abstraction) of the storage stack.

Abstract: Embodiments of battery-based data persistence management in computing devices are disclosed therein. In one embodiment, a method includes receiving a storage request to persistently store data in the computing device. In response to receiving the storage request, the method includes allocating a number of memory blocks of the main memory to store the data associated with the storage request and incrementing an accumulated number of memory blocks in the main memory that contain data stored in response to received storage requests. The method further includes maintaining the accumulated number of memory blocks in the main memory below a threshold corresponding to an energy capacity of the auxiliary power source and copying all of the stored data in the memory blocks of the main memory to the persistent storage using power from only the auxiliary power source when the main power supply suffers an unexpected power failure.

Abstract: Various techniques of managing storage devices in a computing system are described in this application. In one embodiment, a method includes receiving an input containing consumption data representing consumption of a storage device in one of the processing units and determining if the storage device in one of the processing units is consumed excessively. In response to determining that the storage device is consumed excessively, an indicator may be generated to indicate a potential program migration from the one of the processing units to another one of the processing units in the computing system.

Abstract: Technology relating to tuning for operating memory devices is disclosed. The technology includes a computing device that selectively configures operating parameters for at least one operating memory device based at least in part of performance characteristics for an application or other workload that the computing device has been requested to execute. This technology may be implemented, at least in part, in the firmware via a Basic Input/Output System (BIOS) or Unified Extensible Firmware Interface (UEFI) of the computing device. Further, this technology may be employed by a computing device that is executing workloads on behalf of a distributed computing system, e.g., in a data center. Such data centers may include, for example, thousands of computing devices and even more operating memory devices.

Abstract: Aspects extend to methods, systems, and computer program products for predicting solid state drive reliability. Aspects of the invention can be used to predict and/or to configure a data center to minimize one or more of: SSD capacity degradation (how much storage an SSD has left), SSD performance degradation (reduced read/write latency/throughput), and SSD failure. Models and data center considerations can be based on device level SSD related operations, such as, for example, read, write, erase. Operations decisions can be made for a data center based on SSD specific features, such as, for example, remaining capacity, write amplification factor, etc. Dependence and/or causality of various different data center factors can be leveraged. The impact of the various data center factors on different SSD failure modes and capacity/performance degradation can be quantified to drive SSD design, SSD provisioning, and SSD operations.

Abstract: Operations of a variety of components of a storage system stack are redefined to make the system more efficient when the underlying media has a “multi-log” type interface such as the case with NAND flash SSD memory or shingled magnetic recording media. The responsibilities of components of the storage system stack are modified such that each responsibility is performed at the most efficient component (level of abstraction) of the storage stack.

Abstract: Various techniques of managing storage devices in a computing system are described in this application. In one embodiment, a method includes receiving an input containing consumption data representing consumption of a storage device in one of the processing units and determining if the storage device in one of the processing units is consumed excessively. In response to determining that the storage device is consumed excessively, an indicator may be generated to indicate a potential program migration from the one of the processing units to another one of the processing units in the computing system.

Abstract: Techniques for problem determination and diagnosis in a shared dynamic cloud environment. A method includes monitoring each virtual machine and physical server in the shared dynamic cloud environment for at least one metric, identifying a symptom of a problem and generating an event based on said monitoring, analyzing the event to determine a deviation from normal behavior, and classifying the event as a cloud-based anomaly or an application fault based on existing knowledge.

Abstract: An apparatus and an article of manufacture for problem determination and diagnosis in a shared dynamic cloud environment include monitoring each virtual machine and physical server in the shared dynamic cloud environment for at least one metric, identifying a symptom of a problem and generating an event based on said monitoring, analyzing the event to determine a deviation from normal behavior, and classifying the event as a cloud-based anomaly or an application fault based on existing knowledge.

Abstract: Techniques for problem determination and diagnosis in a shared dynamic cloud environment. A method includes monitoring each virtual machine and physical server in the shared dynamic cloud environment for at least one metric, identifying a symptom of a problem and generating an event based on said monitoring, analyzing the event to determine a deviation from normal behavior, and classifying the event as a cloud-based anomaly or an application fault based on existing knowledge.

Abstract: A method, an apparatus and an article of manufacture for problem determination and diagnosis in a shared dynamic cloud environment. The method includes monitoring each virtual machine and physical server in the shared dynamic cloud environment for at least one metric, identifying a symptom of a problem and generating an event based on said monitoring, analyzing the event to determine a deviation from normal behavior, and classifying the event as a cloud-based anomaly or an application fault based on existing knowledge.