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公开(公告)号：US11423327B2
公开(公告)日：2022-08-23
申请号：US16156925
申请日：2018-10-10
Applicant: Oracle International Corporation
Inventor： Onur Kocberber , Felix Schmidt , Craig Schelp , Andrew Brownsword , Nipun Agarwal
Abstract: Techniques are described herein for estimating CPU, memory, and I/O utilization for a workload via out-of-band sensor readings using a machine learning model. The framework involves receiving sensor data associated with executing benchmark applications, obtaining ground truth utilization values for the benchmarks, preprocessing the training data to select a set of enhanced sequences, and using the enhanced sequences to train a random forest model to estimate CPU, memory, and I/O utilization given sensor monitoring data. Prior to the training phase, a machine learning model is trained using a set of predefined hyper-parameters. The trained models are used to generate estimations for CPU, memory, and I/O utilizations values. The utilization values are used with workload context information to assess the deployment and generate one or more recommendations for machine types that will best serve the workload in terms of system utilization.
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公开(公告)号：US10917203B2
公开(公告)日：2021-02-09
申请号：US16414954
申请日：2019-05-17
Applicant: Oracle International Corporation
Inventor： Stuart Wray , Felix Schmidt , Craig Schelp , Pravin Shinde , Akhilesh Singhania , Nipun Agarwal
Abstract: Embodiments use Bayesian techniques to efficiently estimate the bit error rates (BERs) of cables in a computer network at a customizable level of confidence. Specifically, a plurality of probability records are maintained for a given cable in a computer system, where each probability record is associated with a hypothetical BER for the cable, and reflects a probability that the cable has the associated hypothetical BER. At configurable time intervals, the probability records are updated using statistics gathered from a switch port connected to the cable. In order to estimate the BER of the cable at a given confidence level, embodiments determine which probability record is associated with a probability mass that indicates the confidence level. The estimate for the cable BER is the hypothetical BER that is associated with the indicated probability mass. Embodiments store the estimate in memory and utilize the estimate to aid in maintaining the computer system.
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公开(公告)号：US10795690B2
公开(公告)日：2020-10-06
申请号：US16174582
申请日：2018-10-30
Applicant: Oracle International Corporation
Inventor： Pravin Shinde , Felix Schmidt , Craig Schelp
IPC: G06F9/44 , G06F9/445 , G06F16/901
Abstract: Herein are computerized techniques for generation, costing/scoring, optimal selection, and reporting of intermediate configurations for a datacenter change plan. In an embodiment, a computer receives a current configuration of a datacenter and a target configuration. New configurations are generated based on the current configuration. A cost function is applied to calculate a cost of each new configuration based on measuring a logical difference between the new configuration and the target configuration. A particular new configuration is selected that has a least cost. When the particular configuration satisfies the target configuration, the datacenter is reconfigured based on the particular configuration. Otherwise, this process is (e.g. iteratively) repeated with the particular configuration instead used as the current configuration. In embodiments, new configurations are randomly, greedily, and/or manually generated. In an embodiment, new configurations obey design invariants that constrain which changes and/or configurations are attainable.
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