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公开(公告)号：US11847496B2
公开(公告)日：2023-12-19
申请号：US17082413
申请日：2020-10-28
Applicant: Adobe Inc.
Inventor： Nikhil Sheoran , Subrata Mitra
CPC classification number: G06F9/5011 , G06F9/5083 , G06N20/00
Abstract: A digital environment includes multiple computing nodes and a scheduling system that assigns workloads to computing nodes. The scheduling system includes an equivalence-class-based resource usage prediction system that receives a workload request and predicts an equivalence class for that workload request based on resource usage over time by the workload request or metadata associated with the workload request. The scheduling system also includes a workload assignment system that assigns the workload request to one or more of the computing nodes based on the predicted equivalence class. The number of equivalence classes is small relative to the total number of workloads that are scheduled (as an example, 10 to 15 equivalence classes for a total number of workloads in the tens or hundreds of thousands).
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公开(公告)号：US20200257968A1
公开(公告)日：2020-08-13
申请号：US16271642
申请日：2019-02-08
Applicant: Adobe Inc.
Inventor： Subrata Mitra , Nikhil Sheoran , Ramanuja Narasimha Simha , Shanka Subhra Mondal , Neeraj Jagdish Dhake , Ravinder Nehra
Abstract: The technology described herein is directed to a self-learning application scheduler for improved scheduling distribution of resource requests, e.g., job and service scheduling requests or tasks derived therefrom, initiated by applications on a shared compute infrastructure. More specifically, the self-learning application scheduler includes a reinforcement learning agent that iteratively learns a scheduling policy to improve scheduling distribution of the resource requests on the shared compute infrastructure. In some implementations, the reinforcement learning agent learns inherent characteristics and patterns of the resource requests initiated by the applications and orchestrates placement or scheduling of the resource requests on the shared compute infrastructure to minimize resource contention and thereby improve application performance for better overall user-experience.
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公开(公告)号：US20240394407A1
公开(公告)日：2024-11-28
申请号：US18324484
申请日：2023-05-26
Applicant: Adobe Inc.
Inventor： Sunav Choudhary , Subrata Mitra , Sanjay Sukumaran , Priyanshu Yadav , Munish Gupta , Jashn Arora , Iftikhar Ahamath Burhanuddin , Gautam Choudhary , Atharv Tyagi
IPC: G06F21/62
Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that implements a secure distributed data collaboration architecture for generating synthetic datasets. For example, the disclosed system sends a request to perform a data collaboration with a first dataset of a first local node and a second dataset of a second local node. The disclosed system receives intermediate feature maps from the local nodes that correspond with the datasets and generates a combined feature map. Further, the disclosed system generates a synthetic dataset from the combined feature map by utilizing a central generative model. Moreover, the synthetic dataset generated by the disclosed system is statistically representative of the first dataset and the second dataset.
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公开(公告)号：US12014217B2
公开(公告)日：2024-06-18
申请号：US17538663
申请日：2021-11-30
Applicant: Adobe Inc.
Inventor： Subrata Mitra , Sunav Choudhary , Shaddy Garg , Anuj Jitendra Diwan , Piyush Kumar Maurya , Arpit Aggarwal , Prateek Jain
IPC: G06F9/44 , G06F9/50 , G06F18/214 , G06N20/00
CPC classification number: G06F9/5038 , G06F9/5044 , G06F9/5055 , G06F9/5088 , G06F18/214 , G06N20/00
Abstract: A resource control system is described that is configured to control scheduling of executable jobs by compute instances of a service provider system. In one example, the resource control system outputs a deployment user interface to obtain job information. Upon receipt of the job information, the resource control system communicates with a service provider system to obtain logs from compute instances implemented by the service provider system for the respective executable jobs. The resource control system uses data obtained from the logs to estimate utility indicating status of respective executable jobs and an amount of time to complete the executable jobs by respective compute instances. The resource control system then employs a machine-learning module to generate an action to be performed by compute instances for respective executable jobs.
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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
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.
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Patent Agency Ranking
公开(公告)号：US11915054B2
公开(公告)日：2024-02-27
申请号：US17324692
申请日：2021-05-19
Applicant: Adobe Inc.
Inventor： Subrata Mitra , Sunav Choudhary , Sheng Yang , Kanak Vivek Mahadik , Samir Khuller
CPC classification number: G06F9/5038 , G06F9/4818 , G06F9/4856 , G06F9/4881
Abstract: Techniques are provided for scheduling multiple jobs on one or more cloud computing instances, which provide the ability to select a job for execution from among a plurality of jobs, and to further select a designated instance from among a plurality of cloud computing instances for executing the selected job. The job and the designated instance are each selected based on a probability distribution that a cost of executing the job on the designated instance does not exceed the budget. The probability distribution is based on several factors including a cost of prior executions of other jobs on the designated instance and a utility function that represents a value associated with a progress of each job. By scheduling select jobs on discounted cloud computing instances, the aggregate utility of the jobs can be maximized or otherwise improved for a given budget.
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公开(公告)号：US20230222005A1
公开(公告)日：2023-07-13
申请号：US17573221
申请日：2022-01-11
Applicant: Adobe Inc.
Inventor： Subrata Mitra , Pradeep Dogga
CPC classification number: G06F9/5077 , G06F9/5016 , G06F9/5022 , G06N20/00
Abstract: Shared resource interference detection techniques are described. In an example, a resource detection module supports techniques to quantify levels of interference through use of working set sizes. The resource detection module selects working set sizes. The resource detection module then initiates execution of code that utilizes the shared resource based on the first working set size. The resource detection module detects a resource consumption amount based on the execution of the code. The resource detection module then determines whether the detected resource consumption amount corresponds to the defined resource consumption amount for the selected working set size.
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公开(公告)号：US20230168941A1
公开(公告)日：2023-06-01
申请号：US17538663
申请日：2021-11-30
Applicant: Adobe Inc.
Inventor： Subrata Mitra , Sunav Choudhary , Shaddy Garg , Anuj Jitendra Diwan , Piyush Kumar Maurya , Arpit Aggarwal , Prateek Jain
CPC classification number: G06F9/5038 , G06F9/5044 , G06F9/5055 , G06F9/5088 , G06K9/6256 , G06N20/00
Abstract: A resource control system is described that is configured to control scheduling of executable jobs by compute instances of a service provider system. In one example, the resource control system outputs a deployment user interface to obtain job information. Upon receipt of the job information, the resource control system communicates with a service provider system to obtain logs from compute instances implemented by the service provider system for the respective executable jobs. The resource control system uses data obtained from the logs to estimate utility indicating status of respective executable jobs and an amount of time to complete the executable jobs by respective compute instances. The resource control system then employs a machine-learning module to generate an action to be performed by compute instances for respective executable jobs.
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公开(公告)号：US12229604B2
公开(公告)日：2025-02-18
申请号：US17573221
申请日：2022-01-11
Applicant: Adobe Inc.
Inventor： Subrata Mitra , Pradeep Dogga
Abstract: Shared resource interference detection techniques are described. In an example, a resource detection module supports techniques to quantify levels of interference through use of working set sizes. The resource detection module selects working set sizes. The resource detection module then initiates execution of code that utilizes the shared resource based on the first working set size. The resource detection module detects a resource consumption amount based on the execution of the code. The resource detection module then determines whether the detected resource consumption amount corresponds to the defined resource consumption amount for the selected working set size.
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公开(公告)号：US12164517B2
公开(公告)日：2024-12-10
申请号：US18092779
申请日：2023-01-03
Applicant: Adobe Inc.
Inventor： Vibhor Porwal , Yeuk-Yin Chan , Vidit Bhatia , Subrata Mitra , Shaddy Garg , Sergey N Kazarin , Sameeksha Arora , Himanshu Panday , Gautam Pratap Kowshik , Fan Du , Anup Bandigadi Rao , Anil Malkani
IPC: G06F16/245 , G06F16/2453 , G06F16/2458
Abstract: To retrieve information derived from a plurality of separately stored datasets, join structures are identified within the plurality of separately stored datasets. Join structures can include datasets joined by a central dataset, datasets joined by a single key, and datasets joined across a plurality of keys. Each of the join structures corresponds to a query processing schema that defines a sampling technique. When a join query is received as a SQL query, the join query identifies a portion of the plurality of separately stored datasets, from which a join structure is selected and a corresponding query processing schema is identified. The join query is reconstructed to form a reconstructed join query that comprises query processing schema instructions to derive the requested information using the sampling technique defined by the identified query processing schema.
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