公开(公告)号：US11947986B2

公开(公告)日：2024-04-02

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 ,  G06F21/56 ,  G06N20/00 ,  G06F21/57 ,  H04L9/40

CPC classification number: G06F9/45504 ,  G06F9/45533 ,  G06F9/45558 ,  G06F9/547 ,  G06F21/566 ,  G06N20/00 ,  G06F2009/45587 ,  G06F2009/45595 ,  G06F21/577 ,  H04L63/1425 ,  H04L63/145

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：US11086646B2

公开(公告)日：2021-08-10

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: G06F21/56 ,  G06F9/455 ,  G06F9/54 ,  G06N20/00 ,  H04L29/06 ,  G06F21/57

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.