International Business Machines Corporation

Telefonaktiebolaget Lm Ericsson (publ)

Ali Kanso

Maria Toeroe

Jinho Hwang

Muhammed Fatih Bulut

Alaa Salah Youssef

Shripad Nadgowda

Hai Huang

Paolo Dettori

Michael Joseph Spreitzer

Mariusz Sabath

Chen Wang

Ferhat Khendek

Chen Lin

Stefania Costache

Todd Eric Kaplinger

Malgorzata Steinder

Tamar Eilam

Yves Lemieux

Kuan Feng

Alexey Roytman

Fereydoun Farrahi Moghaddam

Pejman Salehi

Tonghoon Suk

Pietro Colombo

Hassan Hawilo

Asser Narsreldin Tantawi

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.

Machine-learning model to predict probability of success of an operator in a PaaS cloud environment

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.

Context based risk assessment of a computing resource vulnerability

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

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.

Machine learning model for micro-service compliance requirements

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

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.

Method and system to identify and prioritize re-factoring to improve micro-service identification

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.

Generation and application of meta-policies for application deployment environments

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.

Collaborative software development tool for resolving potential code-change conflicts in real time

An artificial intelligence (AI) platform to support a continuous integration and deployment (CI/CD) pipeline for software development and operations (DevOps). One or more dependency graphs are generated based on application artifacts. A machine learning (ML) model is leveraged to capture a relationship between components in the dependency graph(s) and one or more pipeline artifacts. Responsive a change of an application artifact, the captured relationship is leveraged to identify an impact of the detected change on the pipeline artifact(s). The CI/CD pipeline is selectively optimized and executed based on the identified impact to improve the efficiency of the pipeline and the deployment time.

Dynamic automation of selection of pipeline artifacts

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

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.

Quota management protocol for shared computing systems

Cloud computing is continuously growing as a business model for hosting information and communications technology applications. While the on-demand resource consumption and faster deployment time make this model appealing for the enterprise, other concerns arise regarding the quality of service offered by the cloud. The placement strategy of the virtual machines hosting the applications has a tremendous effect on the High Availability of the services provided by these applications hosted in the cloud. Systems and methods for scaling a virtualized application that take into consideration the interdependencies between the components of the applications and other constraints such as the communication delay tolerance and resource utilization are provided.

Elasticity for highly available applications

A middleware that manages high availability of a software application is automatically configured when the software application is added to a system. The method of automatic configuration includes receiving a user input that specifies availability requirements and information of the software application, where the availability requirements include at least a checkpoint service that enables the software application to save and retrieve its state. The method also includes identifying a checkpoint agent for providing the checkpoint service to the software application. The checkpoint agent includes a first interface to the software application and a second interface, different from the first interface, to the middleware. A middleware configuration is generated that satisfies the availability requirements, based on the information of the software application such that the middleware offers the checkpoint service to the software application through the checkpoint agent and directly manages lifecycles of the software application.

Providing high availability for state-aware applications

Redundancy models are compared to determine or assist in determining an Availability Management Framework (AMF) configuration of a highly available system based on quantified service availability of the system. Each redundancy model defines assignments of service-instances to service-units. An analysis model of the system is constructed to capture recovery behaviors of the system for each redundancy model. Service availability of the system is quantified based on the analysis model under one or more scenarios including failure scenarios and recovery scenarios. Based on a comparison of service availability levels provided by the redundancy models and subject to constraints of the HA system, one of the redundancy models is identified that provides a required level of service availability for the system.

Comparing redundancy models for determination of an availability management framework (AMF) configuration and runtime assignment of a high availability system

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Elmsford, NY, United States of America

Ali Kanso