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131.发明公开公开(公告)号：US20230342220A1
公开(公告)日：2023-10-26
申请号：US18342998
申请日：2023-06-28
IPC分类号： G06F9/50
CPC分类号： G06F9/5077 , G06F2209/505
摘要： Example implementations relate to a role-based autoscaling approach for scaling of nodes of a stateful application in a large scale virtual data processing (LSVDP) environment. Information is received regarding a role performed by the nodes of a virtual cluster of an LSVDP environment on which a stateful application is or will be deployed. Role-based autoscaling policies are maintained defining conditions under which the roles are to be scaled. A policy for a first role upon which a second role is dependent specifies a condition for scaling out the first role by a first step and a second step by which the second role is to be scaled out in tandem. When load information for the first role meets the condition, nodes in the virtual cluster that perform the first role are increased by the first step and nodes that perform the second role are increased by the second step.
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公开(公告)号：US20230305869A1
公开(公告)日：2023-09-28
申请号：US17703038
申请日：2022-03-24
申请人： Red Hat, Inc.
CPC分类号： G06F9/45541 , G06F9/5044 , G06F2209/505
摘要： Systems and methods for dynamically allocating host devices in distributed computing environments are provided. In one embodiment, a method is provided that includes receiving a request to execute multiple instances of a software application within a distributed computing environment. The distributed computing environment may be a bare metal computing environment in which application code is executed directly by computing hardware. At least one computing resource requirement, including at least one minimum resource requirement, may be identified and computing resource information may be received from a first plurality of host devices. Based on the computing resource information, a second plurality of host devices may be identified from among the first plurality of host devices that fulfill the minimum resource requirement. At least a subset of the second plurality of host devices may be assigned to a cluster used to execute the multiple instances of the software application.
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公开(公告)号：US11768713B2
公开(公告)日：2023-09-26
申请号：US17234711
申请日：2021-04-19
发明人： Vidush Vishwanath , Kendall Stratton , Rohit Raina
CPC分类号： G06F9/5083 , G06F9/505 , G06F9/5077 , G06F11/3433 , G06F2209/503 , G06F2209/505
摘要： Systems and methods for dynamically relocating pods to optimize inter-pod networking efficiency are provided. The method comprises receiving and storing inter-pod traffic data for a plurality of pods. The plurality of pods includes a first pod, a second pod, and a third pod. The method further includes receiving and storing node resource availability data for each node of a plurality of nodes, generating a queue that sorts the plurality of pods by an amount of inter-pod traffic indicated by the inter-pod traffic data, generating a hash that maps one or more parameters to the plurality of nodes, selecting, based on the generated hash, a node of the plurality of nodes, and dynamically relocating a highest ranked pod of the plurality of pods from the generated queue to the selected node.
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公开(公告)号：US20230289240A1
公开(公告)日：2023-09-14
申请号：US17691570
申请日：2022-03-10
申请人： Google LLC
发明人： Alan Pearson , Yaou Wei
CPC分类号： G06F9/5088 , G06F9/5077 , G06F9/4875 , G06F2209/505
摘要： A system and method of balancing data storage among a plurality of groups of computing devices, each group comprising one or more respective computing devices. The method may involve determining a compute utilization disparity between the group having a highest level of compute utilization and the group having a lowest level of compute utilization, determining a transfer of one or more projects between the plurality of groups of computing devices that reduces the compute utilization disparity, and directing the plurality of groups of computing devices to execute the determined transfer
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公开(公告)号：US11698820B2
公开(公告)日：2023-07-11
申请号：US16800024
申请日：2020-02-25
IPC分类号： G06F9/50
CPC分类号： G06F9/5077 , G06F2209/505
摘要： Example implementations relate to a role-based autoscaling approach for scaling of nodes of a stateful application in a large scale virtual data processing (LSVDP) environment. Information is received regarding a role performed by the nodes of a virtual cluster of an LSVDP environment on which a stateful application is or will be deployed. Role-based autoscaling policies are maintained defining conditions under which the roles are to be scaled. A policy for a first role upon which a second role is dependent specifies a condition for scaling out the first role by a first step and a second step by which the second role is to be scaled out in tandem. When load information for the first role meets the condition, nodes in the virtual cluster that perform the first role are increased by the first step and nodes that perform the second role are increased by the second step.
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公开(公告)号：US20230179650A1
公开(公告)日：2023-06-08
申请号：US17652467
申请日：2022-02-24
IPC分类号： H04L67/1001 , H04L43/0852 , H04L43/0876 , G06K9/62 , G06N3/02 , G06F9/455
CPC分类号： H04L67/1002 , H04L43/0852 , H04L43/0876 , G06K9/6256 , G06K9/6251 , G06N3/02 , G06F9/45558 , G06F2009/45595 , G06F2009/4557 , G06F2209/505
摘要： A remote server computing system is configured to deploy a cloud-service-managed control plane and a cloud service data plane spanning the remote server computing system, a local edge computing device, and a local on-premises computing device connected in a hybrid cloud environment. Energy-related training data is received including a plurality of energy-related training data pairs. A machine learning function is trained using the plurality of training data pairs to predict a classified label for restricted energy-related data that is not accessible to the remote server computing system. The trained machine learning function is deployed to the one or more of the local edge computing device and the local on-premises computing device via the cloud service data plane. The remote server computing system is further configured to receive, via the cloud service data plane, classified output of the trained machine learning function.
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公开(公告)号：US20230153170A1
公开(公告)日：2023-05-18
申请号：US17983630
申请日：2022-11-09
发明人： Jae Hoon AN , Young Hwan KIM
IPC分类号： G06F9/50
CPC分类号： G06F9/5077 , G06F9/5072 , G06F2209/505 , G06F2209/501 , G06F2209/503 , G06F2209/5022
摘要： There are provided a method and an apparatus for hybrid cloud management, which configure, reconfigure, and manage service resources in order to rapidly deploy a service in a hybrid cloud environment. According to embodiments of the disclosure, when there is a request for resources of a service operating in an existing cloud environment (Kubernetes), problems of a method of simply expanding replicas may be solved, and rapid processing (deployment) may be performed in response to a continuous resource request. In addition, an available space for using resources may be guaranteed by applying a method of HPA (increasing the number of resource replicas), VPA (increasing allocated resources), migration (transferring resources), rather than simply expanding the number of replicas.
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公开(公告)号：US11650847B2
公开(公告)日：2023-05-16
申请号：US17201225
申请日：2021-03-15
申请人： SAP SE
CPC分类号： G06F9/4887 , G06F9/5072 , G06F11/0715 , G06F11/3051 , G06F2209/486 , G06F2209/505
摘要： The present disclosure relates to computer-implemented methods, software, and systems for an automatic recovery job execution through a scheduling framework in a cloud environment. One or more recovery jobs are scheduled to be performed periodically for one or more registered service components included in a service instance running on a cluster node of a cloud platform. Each recovery job is associated with a corresponding service component of the service instance. A health check operation is invoked at a service component based on executing a recovery job at the scheduling framework corresponding to the service component. In response to determining that the service component needs a recovery measure based on a result from the health check operation, a recovery operation is invoked as part of executing a set of scheduled routines of the recovery job. Implemented logic for the recovery operation is stored and executed at the service component.
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公开(公告)号：US20190146839A1
公开(公告)日：2019-05-16
申请号：US15812911
申请日：2017-11-14
发明人： Todd Lowney , Velmurugan Vinayakam , Pradeepa Shanmugam , Jerome M. Zott , Gopi Krishna Dogiparti , Rakesh K. Joshi , Sai Karthik Nanduri , Vigneshvaran Ramalingam , Arun Prasath Govindarajulu
IPC分类号： G06F9/50
CPC分类号： G06F9/50 , G06F9/5016 , G06F9/5027 , G06F2209/505
摘要： A method for identifying applications operating in contravention to historic operation standards is provided. The method may include retrieving operation system log data from a plurality of operating system logs. The method may include retrieving job scheduling log data from a plurality of job scheduling logs on the first level. The method may include retrieving platform data from a distributed data platform on a second level. The method may include combining the operating system log data, the job scheduling log data and the platform data. The method may identify an application operating in contravention to historic operation standards. The method may also identify at least one user identifier or service identifier associate with the application. The method may terminate and/or flag the application for remediation.
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公开(公告)号：US20190042309A1
公开(公告)日：2019-02-07
申请号：US16150163
申请日：2018-10-02
发明人： Chong Chen , Fang Liu , Qi Wang , Shutao Yuan
CPC分类号： G06F9/4881 , G06F9/5038 , G06F9/5044 , G06F9/505 , G06F9/5061 , G06F9/5072 , G06F2209/5012 , G06F2209/505 , H04L67/1002
摘要： According to one aspect of the present disclosure, a technique for job distribution within a grid environment includes receiving a job at a submission cluster for distribution of the job to at least one of a plurality of execution clusters where each execution cluster includes one or more execution hosts. Resource attributes are determined corresponding to each execution host of the execution clusters. For each execution cluster, execution hosts are grouped based on the resource attributes of the respective execution hosts. For each grouping of execution hosts, a mega-host is defined for the respective execution cluster where the mega-host for a respective execution cluster defines resource attributes based on the resource attributes of the respective grouped execution hosts. An optimum execution cluster is selected for receiving the job based on a weighting factor applied to select resources of the respective execution clusters.
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