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公开(公告)号：US11956266B2
公开(公告)日：2024-04-09
申请号：US17078603
申请日：2020-10-23
发明人： Ali Kanso , Muhammed Fatih Bulut , Jinho Hwang , Shripad Nadgowda
CPC分类号： H04L63/1433 , H04L63/1416 , H04L63/20
摘要： According to an embodiment, a computer-implemented method can comprise: inspecting, using a processor, a set of container images respectively associated with pods; identifying, using the processor, a first subset of the pods that contain a vulnerability; classifying, using the processor, the first subset of the pods as primary-infected pods; generating, using the processor, a first list of namespaces in which the primary-infected pods are deployed within a network; checking, using the processor, network policies in connection with the first list of namespaces to determine secondary-suspect pods that have ability to communicate with the primary-infected pods; generating, using the processor, a list of secondary-suspect namespaces in which the secondary-suspect pods are deployed within the network; identifying, using the processor, one or more secondary-suspect pods that communicated with one or more primary-infected pods; and generating, using the processor, a list of secondary-infected pods.
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公开(公告)号：US20220188192A1
公开(公告)日：2022-06-16
申请号：US17117183
申请日：2020-12-10
发明人： Chen Wang , Ali Kanso , Alaa S. Youssef
摘要： Methods, computer program products, and/or systems are provided that perform the following operations: obtaining data indicative of a node failure; obtaining data associated with nodes and pods started on each node; generating a causation score for each pod associated with a failed node, wherein each pod associated with the failed node is designated as a candidate pod for the node failure; determining pod rescheduling for each candidate pod associated with the failed node based, at least in part, on a pod ranking of the causation score for each pod; and providing the pod rescheduling to a node cluster to restart each pod associated with the failed node.
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公开(公告)号：US20210304063A1
公开(公告)日：2021-09-30
申请号：US16834463
申请日：2020-03-30
发明人： Muhammed Fatih Bulut , Jinho Hwang , Ali Kanso , Shripad Nadgowda
摘要： Embodiments relate to a computer system, computer program product, and computer-implemented method to train a machine learning (ML) model using artificial intelligence to learn an association between (regulatory) compliance requirements and features of micro-service training datasets. The trained ML model is leveraged to determine the compliance requirements of a micro-service requiring classification. In an exemplary embodiment, once the micro-service has been classified with respect to applicable compliance requirements, the classified micro-service may be used as an additional micro-service training dataset to further train the ML model and thereby improve its performance.
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公开(公告)号：US11513842B2
公开(公告)日：2022-11-29
申请号：US16592078
申请日：2019-10-03
发明人： Chen Wang , Stefania V. Costache , Alaa S. Youssef , Ali Kanso , Tonghoon Suk , Asser Narsreldin Tantawi
IPC分类号： G06F9/48
摘要： Systems, computer-implemented methods, and computer program products that can facilitate performance biased resource scheduling based on runtime performance of a certain workload type on one or more nodes are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a performance component that assigns performance points to different nodes based on execution of one or more workload types. The computer executable components can further comprise a scheduler extender component that modifies a scheduling decision to run a workload type on a node based on the performance points.
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公开(公告)号：US20200304599A1
公开(公告)日：2020-09-24
申请号：US16360361
申请日：2019-03-21
发明人： Ali Kanso , Paolo Dettori , Alexey Roytman , Kuan Feng , Todd Eric Kaplinger , Tamar Eilam
摘要： Systems, computer-implemented methods, and computer program products that can facilitate generating and applying meta-policies for application deployment environments are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a state analyzer that can analyze a first application deployment environment to identify a first configuration of the first application deployment environment. The computer executable components can further comprise a policy generator that generates a meta-policy based on the identified first configuration.
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6.发明授权公开(公告)号：US12124924B2
公开(公告)日：2024-10-22
申请号：US16950228
申请日：2020-11-17
发明人： Ali Kanso , Jinho Hwang , Muhammed Fatih Bulut , Shripad Nadgowda , Chen Lin
摘要： Systems and methods are provided that integrate a machine-learning model, and more specifically, utilizing a platform as a service (PaaS) cloud to predict probability of success for an operator in an environment. An embodiment comprises a system having: a processor that executes computer executable components stored in memory, trained machine-learning model that predicts probability of success for deployment of an operator in an environment with a namespace of a platform as a service (PaaS) cloud, and a deployment component that receives a first operator and a first namespace and employs the trained machine-learning model to predict success of deployment of the first operator in a first environment.
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7.发明授权公开(公告)号：US11269625B1
公开(公告)日：2022-03-08
申请号：US17075432
申请日：2020-10-20
发明人： Chen Lin , Jinho Hwang , Muhammed Fatih Bulut , Ali Kanso , Shripad Nadgowda
摘要： A computer system, computer program product, and computer-implemented method to identify one or more re-factoring operations directed at micro-service identification for source code. A genetic algorithm is leveraged to produce an offspring population of re-factoring operations from a parent set. The offspring population is subject to an assessment utilizing one or more objective measures. Responsive to the assessment, one or more identified re-factoring operations are selectively applied to the source code to produce one or more corresponding micro-service candidates.
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8.发明授权公开(公告)号：US11150893B2
公开(公告)日：2021-10-19
申请号：US16296727
申请日：2019-03-08
发明人： Mariusz Sabath , Ali Kanso , Michael Joseph Spreitzer , Hai Huang
摘要： According to one or more embodiments of the present invention, a computer-implemented method includes uploading, by a first instance of an integrated development environment (IDE), a first source-code change to a change log of a version control system. A second instance of the IDE is used to upload a second source-code change to the change log of the version control system. A determination is made that the second source-code change conflicts with the first source-code change. Based on the determination that the second source-code change conflicts with the first source-code change, generating a notification of the second source-code change is generated in the first instance of the IDE.
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公开(公告)号：US10805237B2
公开(公告)日：2020-10-13
申请号：US16522795
申请日：2019-07-26
发明人： Paolo Dettori , Hai Huang , Ali Kanso , Mariusz Sabath , Michael Joseph Spreitzer , Alaa Salah Youssef
IPC分类号： H04L12/911
摘要： Techniques are provided for automated employment of respective quota managers for framework instances, where the respective quota managers can negotiate amongst themselves to manage usage of a resource of a shared computing system in relation to a quota for the resource for a tenant of the shared computing system. This can allow tenants to share their quota among multiple frameworks, enable quota exchange between multiple frameworks, and choose a quota with a minimum costs, and thus maximize savings.
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公开(公告)号：US11488064B2
公开(公告)日：2022-11-01
申请号：US16834463
申请日：2020-03-30
发明人： Muhammed Fatih Bulut , Jinho Hwang , Ali Kanso , Shripad Nadgowda
摘要： Embodiments relate to a computer system, computer program product, and computer-implemented method to train a machine learning (ML) model using artificial intelligence to learn an association between (regulatory) compliance requirements and features of micro-service training datasets. The trained ML model is leveraged to determine the compliance requirements of a micro-service requiring classification. In an exemplary embodiment, once the micro-service has been classified with respect to applicable compliance requirements, the classified micro-service may be used as an additional micro-service training dataset to further train the ML model and thereby improve its performance.
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