Red Hat, Inc.

Huamin Chen

Jay Vyas

Roland Ludwig Huss

Ricardo Noriega De Soto

Stephen James Watt

Chen Wang

Michael Bursell

Bradley Dale Childs

Jinjun Xiong

Yuval Lifshitz

Dennis Keefe

Yehuda Sadeh-Weinraub

Adam G Litke

Sage Weil

Steven Travis Nielsen

Michael Hingston McLaughlin Bursell

Noah Watkins

Daniel Francis Gryniewicz

Douglas Smith

Fabian Deutsch

Feiran Zheng

Andrew Grover

Dennis R C Keefe

Daniel J Walsh

Derek W Carr

Vivek Goyal

Mrunal Patel

Paul Morie

Parul Singh

Frank Alexander Zdarsky

Kefu Chai

Eric S Paris

Ronald Nowling

Jonathan Hal Cope

James R Curtis

Omri Zeneva

Tomas Smetana

Alexander G P Wels

David Vossel

Sebastien Han

A software container is obtained for execution on one of a plurality of compute nodes. A processing device schedules the software container on a first compute node of the plurality of compute nodes based on a comparison of a thermal state of the first compute node to a defined threshold.

Scheduler incorporating thermal awareness

One or more metrics associated with performance of a serverless function chain are received. The one or more metrics are used by a reinforcement learning (RL) model to tune the serverless function chain.

Utilizing reinforcement learning for serverless function tuning

Methods and systems are provided for assigning nodes to execute functions in a serverless computing environment. In one embodiment, a method is provided that includes receiving a function for execution in a serverless computing environment and identifying a storage pool needed during execution of the function. The serverless computing environment may include nodes for executing functions and a first set of nodes may be identified that implement the storage pool. Colocation measures may be determined between the first set of nodes and a second set of nodes. Available computing resources may be determined for the second set of nodes, such as available processing cores and available memory. The second set of nodes may be ranked according to the colocation measures and the available computing resources and a first node may be selected based on the ranking. The first node may be assigned to execute the function.

Serverless function colocation with storage pools

A request is received over a network. The request is directed to a service provided by an application of a cluster infrastructure. The cluster infrastructure includes the application and a control plane to schedule execution of the application. Responsive to receiving the request, the control plane of the cluster infrastructure is transitioned from a stopped state to an active state. The request is transferred to the cluster infrastructure.

Cluster infrastructure with on-demand activation

It is determined that data stored on a storage device associated with a computing device powered by a power source is to be transmitted to a remote computing device. A power state of the power source is determined. An encoding mechanism is selected from a plurality of different encoding mechanisms based on the power state of the power source. The data is encoded based on the encoding mechanism to generate encoded data. The encoded data is transmitted to the remote computing device.

Energy aware data replication

Computer workloads can be managed across distributed computing clusters according to some aspects of the present disclosure. In one example, a system can receive a request from a workload manager for identifying a computing cluster to which to assign a workload. The system can determine that the workload is to be assigned to a particular computing cluster among a plurality of computing clusters based on historical information about replica deployment by the particular computing cluster. The system can then transmit a response to the workload manager for causing the workload manager to assign the workload to the particular computing cluster.

Managing computer workloads across distributed computing clusters

Computer workloads can be assigned to nodes of a distributed computing environment based on energy consumption modes employed by the nodes. In one example, a system can determine a first tag assigned to a workload. The first tag can indicate a compatibility of the workload with one or more energy consumption modes employable by one or more nodes of a distributed computing environment. The system can also determine a second tag assigned to a node of the distributed computing environment. The second tag can indicate an energy consumption mode employed by the node. The system can then determine a correspondence between the first tag and the second tag indicating that the workload is compatible with the energy consumption mode employed by the node. Based on determining the correspondence between the first tag and the second tag, the system can assign the workload to the node.

Assigning computer workloads to nodes based on energy consumption modes of the nodes

One or more parameters corresponding to a targeted energy characteristic of a computing client device are received. A ratio of types of storage devices allocated to the computing client device is adjusted based on the one or more parameters. A storage volume for the computing client device is provisioned according to the ratio of the types of the storage devices.

Resource configuration based on energy expenditure

A method includes detecting, by a webhook controller, an inference serverless function invocation. The method further includes determining that the inference serverless function can be optimized. The method further includes generating an optimized version of the inference serverless function using a graph compiler, in response to the determining. The method further includes replacing, by a processing device of the webhook controller, a storage volume in an init container of the inference serverless function with a new storage volume comprising the optimized version of the inference serverless function.

Resource constraint aware deep learning model optimization for serverless-based inference systems

An example method for data auditing for object storage public clouds includes a service broker receiving a request to store data in public object storage, where the request includes user information or a container image. The service broker, based on either the user information or the container image, determines that data auditing is necessary. The service broker creates a storage unit, in public object storage, and a storage proxy. The method further includes the storage proxy storing data, and a data auditor retrieving data from the storage proxy. The data auditor determines a data qualification for the data, and notifies the storage proxy of the data qualification.

Data auditing for object storage public clouds

A system and method for a tiered cloud storage for different availability and performance requirements includes a gateway, a block store configured to cache data, and an object store configured to persistently store data. The gateway, the block store, and the object store are in a compute zone. The gateway may receive from a user application a file access call and process the file access call. The gateway may also send the file access call to the block store. Then, the gateway may determine to store data in the object store and flush the data from the block store to the object store.

Tiered cloud storage for different availability and performance requirements

A method includes initiating, by a device manager associated with a cluster manager proxy, a connection with a cluster of computing devices, wherein initiating the connection includes providing first credentials to the cluster of computing devices to access the cluster manager proxy. The method further includes receiving, at the cluster manager proxy, a first request to register the cluster of computing devices with a cluster manager, the first request including the first credentials to access the cluster manager proxy and sending, from the cluster manager proxy to the cluster manager, a second request to register the cluster of computing devices with the cluster manager, the second request including second credentials to access the cluster manager.

Scalable and secure edge cluster registration

Energy consumption associated with users of a distributed computing system can be monitored using tracing according to some examples described herein. In one such example, a system can execute tracing software configured to track usage of a software service and generate tracing information. The tracing information cant attribute the usage of the software service to a group of users. The system can analyze the tracing information to determine a portion of the usage to attribute to a given user. The system can then determine an energy consumption estimate for the user, for example based on the portion of the usage associated with the user and an energy consumption amount associated with the software service. A graphical user interface can be generated indicating the energy consumption estimate for the user.

Monitoring energy consumption associated with users of a distributed computing system using tracing

Data can be automatically anonymized in a distributed storage system. For example, a system can receive a notification indicating that an object is stored in a non-persistent bucket of a distributed storage system. The system can read the object from the non-persistent bucket in response to receiving the notification. The system can generate an anonymized version of the object by performing one or more anonymization operations with respect to the object. The system can store the anonymized version of the object in a persistent bucket of the distributed storage system. The system can then transmit a command for causing the object to be removed from the non-persistent bucket.

Automatically anonymizing data in a distributed storage system

Systems and methods for inter-cluster deployment of compute services using federated operator components are generally described. In some examples, a first request to deploy a compute service may be received by a federated operator component. In various examples, the federated operator component may send a second request to provision a first compute resource for the compute service to a first cluster of compute nodes. In various examples, the first cluster of compute nodes may be associated with a first hierarchical level of a computing network. In some examples, the federated operator component may send a third request to provision a second compute resource for the compute service to a second cluster of compute nodes. The second cluster of compute nodes may be associated with a second hierarchical level of the computing network that is different from the first hierarchical level.

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Westborough, MA, United States of America

Huamin Chen