Abstract: Methods and systems for enabling sizing of storage array resources are provided. Resources of a storage array can include, for example, cache, memory, SSD cache, central processing unit (CPU), storage capacity, number of hard disk drives (HDD), etc. Generally, methods and systems are provided that enable efficient predictability of sizing needs for said storage resources using historical storage array use and configuration metadata, which is gathered over time from an install base of storage arrays. This metadata is processed to produce models that are used to predict resource sizing needs to be implemented in storage arrays with certainty that takes into account customer-to-customer needs and variability. The efficiency in which the sizing assessment is made further provides significant value because it enables streamlining and acceleration of the provisioning process for storage arrays.

Abstract: A flaw classifier model classifies a computing system as one which contains or does not contain one or more flaws that affect a performance of the computing system. Inputs to the flaw classifier model may include an actual utilization of a resource of the computing system and an expected utilization of the resource of the computing system. The expected utilization of the resource may be determined by an expected resource utilization model. Inputs to the expected resource utilization model may include workload description parameter values and hardware description parameter values.

Abstract: Methods and systems for enabling sizing of storage array resources are provided. Resources of a storage array can include, for example, cache, memory, SSD cache, central processing unit (CPU), storage capacity, number of hard disk drives (HDD), etc. Generally, methods and systems are provided that enable efficient predictability of sizing needs for said storage resources using historical storage array use and configuration metadata, which is gathered over time from an install base of storage arrays. This metadata is processed to produce models that are used to predict resource sizing needs to be implemented in storage arrays with certainty that takes into account customer-to-customer needs and variability.

Abstract: A flaw classifier model classifies a computing system as one which contains or does not contain one or more flaws that affect a performance of the computing system. Inputs to the flaw classifier model may include an actual utilization of a resource of the computing system and an expected utilization of the resource of the computing system. The expected utilization of the resource may be determined by an expected resource utilization model. Inputs to the expected resource utilization model may include workload description parameter values and hardware description parameter values.

Abstract: Methods and systems for enabling sizing of storage array resources are provided. Resources of a storage array can include, for example, cache, memory, SSD cache, central processing unit (CPU), storage capacity, number of hard disk drives (HDD), etc. Generally, methods and systems are provided that enable efficient predictability of sizing needs for said storage resources using historical storage array use and configuration metadata, which is gathered over time from an install base of storage arrays. This metadata is processed to produce models that are used to predict resource sizing needs to be implemented in storage arrays with certainty that takes into account customer-to-customer needs and variability. The efficiency in which the sizing assessment is made further provides significant value because it enables streamlining and acceleration of the provisioning process for storage arrays.

Abstract: Methods and systems for enabling sizing of storage array resources are provided. Resources of a storage array can include, for example, cache, memory, SSD cache, central processing unit (CPU), storage capacity, number of hard disk drives (HDD), etc. Generally, methods and systems are provided that enable efficient predictability of sizing needs for said storage resources using historical storage array use and configuration metadata, which is gathered over time from an install base of storage arrays. This metadata is processed to produce models that are used to predict resource sizing needs to be implemented in storage arrays with certainty that takes into account customer-to-customer needs and variability. The efficiency in which the sizing assessment is made further provides significant value because it enables streamlining and acceleration of the provisioning process for storage arrays.

Abstract: Embodiments of the invention provide a hanger system and method. The hanger system can include a first member and a second member. The first member and the second member can be capable of being moved with respect to each other in order to adjust a first length of the hanger system. In some embodiments, the first member can include at least one aperture and a fastener can be substantially positioned through the second member with the fastener capable of at least partially fixing a position of the first member with respect to the second member. A slot can be included through a portion of the second member and the slot can be capable of enabling a support structure to be coupled to the at least one aperture.

Abstract: Electrochemical cells and electrochemical cell systems are disclosed.

Abstract: A battery, such as an alkaline battery, that can provide a high energy output at a high rate and that has a relatively high ratio of manganese dioxide to cathode volume is disclosed. The battery can provide high energy output at a high rate when the battery is intermittently subjected to a high energy load.