公开(公告)号：US20190354388A1

公开(公告)日：2019-11-21

申请号：US15983390

申请日：2018-05-18

Applicant: ADOBE INC.

Inventor： SUBRATA MITRA ,  SOPAN KHOSLA ,  SANKET VAIBHAV MEHTA ,  MEKALA RAJASEKHAR REDDY ,  AASHAKA DHAVAL SHAH

IPC: G06F9/455 ,  G06F9/54 ,  G06N99/00

Abstract: Embodiments relate to tenant-side detection and mitigation of performance degradation resulting from interference generated by a noisy neighbor in a distributed computing environment. A first machine-learning model such as a k-means nearest neighbor classifier is operated by a tenant to detect an anomaly with a computer system emulator resulting from a co-located noisy neighbor. A second machine-learning model such as a multi-class classifier is operated by the tenant to identify a contended resource associated with the anomaly. A corresponding trigger signal is generated and provided to trigger various mitigation responses, including an application/framework-specific mitigation strategy (e.g., triggered approximations in application/framework performance, best-efforts paths, run-time changes, etc.), load-balancing, scaling out, updates to a scheduler to avoid impacted nodes, and the like. In this manner, a tenant can detect, classify, and mitigate performance degradation resulting from a noisy neighbor.

公开(公告)号：US20210318898A1

公开(公告)日：2021-10-14

申请号：US17355481

申请日：2021-06-23

Applicant: Adobe Inc.

Inventor： SUBRATA MITRA ,  SOPAN KHOSLA ,  SANKET VAIBHAV MEHTA ,  MEKALA RAJASEKHAR REDDY ,  AASHAKA DHAVAL SHAH

IPC: G06F9/455 ,  G06F9/54 ,  G06N20/00 ,  G06F21/56

Abstract: Embodiments relate to tenant-side detection and mitigation of performance degradation resulting from interference generated by a noisy neighbor in a distributed computing environment. A first machine-learning model such as a k-means nearest neighbor classifier is operated by a tenant to detect an anomaly with a computer system emulator resulting from a co-located noisy neighbor. A second machine-learning model such as a multi-class classifier is operated by the tenant to identify a contended resource associated with the anomaly. A corresponding trigger signal is generated and provided to trigger various mitigation responses, including an application/framework-specific mitigation strategy (e.g., triggered approximations in application/framework performance, best-efforts paths, run-time changes, etc.), load-balancing, scaling out, updates to a scheduler to avoid impacted nodes, and the like. In this manner, a tenant can detect, classify, and mitigate performance degradation resulting from a noisy neighbor.

公开(公告)号：US20230153195A1

公开(公告)日：2023-05-18

申请号：US17455364

申请日：2021-11-17

Applicant: ADOBE INC.

Inventor： SUBRATA MITRA ,  AYUSH CHAUHAN ,  SUNAV CHOUDHARY

IPC: G06F11/10

CPC classification number: G06F11/1004 ,  G06F11/1088

Abstract: A method performed by one or more processors that preserves a machine learning model comprises accessing model parameters associated with a machine learning model. The model parameters are determined responsive to training the machine learning model. The method comprises generating a plurality of model parameter sets, where each of the plurality of model parameter sets comprises a separate portion of the set of model parameters. The method comprises determining one or more parity sets comprising values calculated from the plurality of model parameter sets. The method comprises distributing the plurality of model parameter sets and the one or more parity sets among a plurality of computing devices, where each of the plurality of computing devices stores a model parameter set of the plurality of model parameter sets or a parity set of the one or more parity sets. The method comprises accessing, from the plurality of computing devices, a number of sets comprising model parameter sets and at least one parity set. The method comprises reconstructing the machine learning model from the number of sets accessed from the plurality of computing devices.