International Business Machines Corporation

Alaa Salah Youssef

Giovanni Pacifici

Jonathan Paul Munson

Jia Jun Brandon Lum

Ali Kanso

Magda Mahmoud Mourad

Nader M Nassar

Tamer M Nassar

Mariusz Sabath

Chen Wang

Diana Jeanne Arroyo

Marco Pistoia

Asser Nasreldin Tantawi

Hai Huang

Michael Joseph Spreitzer

Ahmed N Tantawy

Malgorzata Steinder

Lawrence H Koved

Paolo Dettori

Abhishek Malvankar

Tamer A Mahfouz

Stefania Costache

Marquita May Ellis

Hesham Soultan

Li Feng Zhang

Maja Vukovic

Lior Aronovich

Jinho Hwang

Dimitrios Pendarakis

Arun Kumar

Jim Alain Laredo

Neeran Mohan Karnik

Girish Bhimrao Chafle

Wolfgang Segmuller

Franco Marconcini

Tonghoon Suk

Tamer Nadeem

Asser Narsreldin Tantawi

Ruby Arora

Ahmed Fouad

Hesham E M Soultan

Systems, computer-implemented methods, and computer program products to facilitate optimization of resource usage based on quota trees are provided. According to an embodiment, a system can comprise a processor that executes computer executable components stored in memory. The computer executable components comprise a determination component that determines one or more quota trees that classify a job request as inadmissible. The computer executable components further comprise an optimization component that optimizes resource usage to enable admissibility of the job request based on the one or more quota trees.

Optimizing resource utilization based on quota trees in resource scheduling

A computer hardware system includes a context controller configured to manage recovery for a workload within a cloud environment. A plurality of compute nodes are initially allocated to the workload, as a current allocation, within a context of the cloud environment. A failure within the current allocation is identified. Responsive to the failure being identified, a plurality of costs respectively associated with each of a plurality of recovery options for the workload are evaluated. A lowest-cost option, from the plurality of recovery options being evaluated, is executed. The recovery options being evaluated includes both recovery options that include performing the workload within the current context and recovery options that include performing at least a portion of the workload in at least one secondary context of the cloud environment. A resource controller node associated with the context controller is communicated with to identify the at least one secondary context.

Resource-aware workload reallocation across cloud environments

A method and system allocating computing resources according to a trained machine learning model that includes receiving a request to accommodate a particular workload that involves a first threshold amount of computing resources implemented by at least one computing cluster. An identified a set of computing clusters, that have computing resources available for allocating from the identified set of computing clusters to the at least one computing cluster to satisfy the first threshold amount of computing resources, is received from a database. A trained deep-reinforcement learning model is applied to generate a policy for allocating the available computing resources from the identified set of computing clusters to the at least one computing cluster. It is verified that the generated policy satisfies a threshold according to one or more predetermined criteria. The computing resources are allocated from the set of computing clusters to the at least one computing cluster.

Allocating computing resources using a machine learning model

A processor may generate one or more encrypted policies associated with a policy creator. A processor may generate token metadata associated with a user utilizing the one or more encrypted policies. A processor may encrypt the token metadata to form encrypted token metadata. A processor may send the one or more encrypted policies and the encrypted token metadata to a policy evaluator. The policy evaluator may evaluate the one or more encrypted policies and the encrypted token metadata. The processor may return a response. The response may be based on the evaluation by the policy evaluator.

Maintaining confidentiality in decentralized policies

A shift-left analysis system receives information regarding an application implemented by one or more microservices. The system determines a microservice performance metric based on a performance prediction model for each microservice of the application. The system outputs an application performance metric for the application based on the microservice performance metrics determined for the one or more microservices of application.

Estimating performance and required resources from shift-left analysis

One or more systems, computer-implemented methods, and computer program products to facilitate a process for consensus regarding proceeding with a request of a transaction are provided. 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 services component that operates a service of a transaction, where the services component approaches consensus regarding a request of the transaction by communicating with one or more other services components that operate one or more other services immediately upstream or downstream in the transaction to the service. The services component can approach the consensus by communicating only with the one or more other services components. To approach the consensus, the services component can communicate one or more messages that include one or more requests, votes or final decisions.

Request facilitation for approaching consensus for a service transaction

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.

Performance biased resource scheduling based on runtime performance

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.

Identifying harmful containers

An example operation may include one or more one or more of receiving two or more authorization decisions from two or more authorization entities into a blockchain system, recording the two or more authorization decisions into one or more blocks of a blockchain of the blockchain system, determining, by the blockchain system, whether the two or more authorization decisions satisfy a policy to authorize access to at least one of a device or identifiable content on the device, and when the two or more authorization decisions satisfy the policy, authorizing access to a public key that can be used to gain access to the device.

Multi-subject device access authorization

Verifying authenticity of software updates is provided. An update executable and an update behavior profile corresponding to a software update are hashed using a cryptographic hash function. A hash of the update executable and the update behavior profile is signed using a private key to form a hashed update digital signature.

Verifying updates based on update behavior-based profiles

Aspects of the invention include creating and starting fast-start container images. A preview image of a container is received at a host computer. The preview image includes a subset of an original image of the container. The preview image of the container is executed, at the host computer, for a workload. Based at least in part on detecting a fault during the executing of the preview image of the container, one of the original image of the container and a portion of the original image not included in the preview image of the container is accessed for continuing execution of the workload.

Creating and starting fast-start container images

An apparatus receives a signal to perform secure erasure of a storage medium. The apparatus, responsive to reception of the signal, erases the storage medium by performing at least the following operations. An encryption key is erased. The encryption key is stored on the storage medium and is used to encrypt data on the storage medium. The apparatus generates a fake encryption key that is different from the encryption key and stores storing the fake encryption key on the storage medium. The encryption key and/or fake encryption key may be stored on the medium in multiple parts. The encryption key may be generated using random data from the medium. The apparatus may be the storage medium or a computer system that access the storage medium. The erasure can be performed in response to a request by a user. The medium may be an erasure-resistant storage medium.

Obfuscation of keys on a storage medium to enable storage erasure

Embodiments for intelligent communication message format automatic correction in a computing system by a processor. An appropriateness of the communication message formats is learned based on a plurality of factors for receiving communication messages from a communication system. A communication message, having one or more errors of a received communication message, may be automatically corrected according to the learned appropriateness of the communication messages.

Intelligent communication message format automatic correction

Technology for selecting job characteristics to determine the similarity among jobs in terms of performance. Technology based on similarity among jobs calculated by selected characteristics for determining jobs that are likely to lead to successful performance of a requested new job by a cloud. Also, technology based on similarity among jobs calculated by selected characteristics for determining jobs that are likely to lead to failure when performing a requested new job by the cloud. When the new job request is accepted, because its characteristics of the new job matches job characteristics characterized by success and/or fails to match job characteristics characterized by failure, then the new job is said to lead to a 'reward' or an 'expected reward' because the new job will be rewarded by being allowed to use, by an admission controller of a cloud management system, use of cloud computing resources of the cloud.

Reward-based admission controller for resource requests in the cloud

Aspects of the invention include selecting a node for an infrastructure update. The selected node is included in a cluster of nodes executing workloads that include containers. A future workload is prevented from being scheduled on the selected node. A workload currently executing on the selected node is migrated to another node included in the cluster of nodes. Infrastructure code on the selected node is updated, and in response to the updating, the ability to schedule a future workload on the selected node is enabled.

Growing community of inventors

Valhalla, NY, United States of America

Alaa Salah Youssef