Abstract: Activation policies for workflows are described. Some implementations support receiving a definition of a workflow, including data defining an input set for the workflow and data defining activities and a flow of the activities for the workflow. The data includes an activation policy for at least a particular activity of the activities. An estimated operational outcome for the workflow and/or each of the activities is determined and displayed. Then data identifying edits to the activation policy are received, and an updated version of the estimated operational outcome for the workflow and/or each of the activities is determined and displayed.

Abstract: Disclosed are examples related to building a customized data collection widget. Building of the customized data collection widget includes receiving a selection of several universal modules for inclusion in the widget. Each universal module of the selected several universal modules may include programming code that causes rendering of user-fillable data fields on a display, and a summary page including data requirements of the respective universal module. Based on the data requirements in a summary page of each respective universal module of the selected several universal modules, the selected several universal modules retrieved from a module repository may be combined in a customized data collection container stored in a composite repository. The selected customized data collection container may be combined with selected other data collection containers to form a uniquely identifiable, customized data collection widget. The customized data collection widget may be delivered to the composite repository.

Abstract: A computer-implemented method includes storing usage of a resource of said computer system as time-stamped resource usage values, comparing said time-stamped resource usage values with predetermined time-stamped performance goal values, assigning a time-stamped priority value to an application that is running based on at least one of said performance goal values, identifying a future workload demand value by applying a time-series analysis algorithm to at least some of said time-stamped resource usage values and a corresponding at least some of said time-stamped performance goal values for said application resulting in workload demand time frames and related amplitudes of said workload demand time frames, and adjusting a dispatch priority value for said application by setting a minimum dispatch priority for said application based on said future workload demand value.

Abstract: The scheduling or allocation of images to nodes of a containerized computing environment is provided. Images that share layers (i.e. images that have one or more layers in common) are allocated to the same node. To determine whether different images share one or more layers, leveraging metadata that is associated with the images is provided. By analyzing metadata associated with the images, it may be determined if the images have one or more layers in common (i.e. comprise the same layer(s)). In this way, information that is already available with conventional images may be used.

Abstract: In one embodiment, a method for scheduling tasks comprises, at a task scheduler of a processing node of a plurality of processing nodes: retrieving a first task descriptor from a local memory of the task scheduler, the task descriptor corresponding to a task scheduled for execution at the current time and comprising at least a task execution time, a frequency for performing the task, and a task identifier; determining whether the task descriptor is assigned to the task scheduler for execution; if it is determined that the task descriptor is assigned to the task scheduler for execution: executing the task; updating the task execution time based on the current task execution time and the frequency for performing the task; and re-queuing the task descriptor in the local memory.

Abstract: Techniques and mechanisms for managing workloads in compute clusters comprising compute nodes by managing the workloads at the resource level of the compute clusters. For example, virtual service contexts (VSCs) may be defined where the VSCs represent service classes. Policies may be defined with respect to each service class. These service classes are dynamically constructed based on business needs. Hence there is natural requirement for a user to construct and rebalance the compute resources for these service classes dynamically. The policies may be related to resources of the compute clusters for executing workload units in the compute clusters. Resources of the compute clusters may be allocated to each service class. Each workload unit may be assigned to a one of the service classes based on the service context or type of workload unit. The workload units may then be executed by the compute clusters using the resources in accordance with the policies.

Abstract: Systems and methods are described for efficient ways to manage storage of data in virtual desktops on writable volumes contained in attachable virtual disks. Multiple writeable volumes can be attached to a user's virtual desktop and data writes on the virtual desktop can be allocated among the writeable volumes based on preset policies or criteria, allowing the storage of different types of data in different writable volumes located on different storage devices.

Abstract: The present application discloses a task status updating method including: listening for K8S events by using a K8S event listener to obtain a Pod status change event, and generating a Pod status update event based on the Pod status change event (S12); listening for the Pod status update event by using a Pod status change event listener, determining a Pod status corresponding to the Pod status update event as a Pod status of a corresponding target task in the deep learning platform when the Pod status update event is listened out, and generating a task status update event (S13); and listening for the task status update event by using a task status change event listener, and updating a current status of the target task as the Pod status of the target task when the task status update event is listened out (S14).

Abstract: A method and apparatus for autoscaling a custom resource of a containerized application handling system utilizes a metric value defined for a system object of the custom resource to scale the system object of the custom resource. An API request for the metric value is sent from an autoscaler to a control plane of the containerized application handling system to receive the metric value, which is compared to a desired metric value. A target scale metric value is then determined based on the comparison and posted in a database of the containerized application handling system. The system object of the custom resource is scaled by an operator of the containerized application handling system based on the posted target scale metric value.

Abstract: In a virtual machine (VM) performance guarantee system, a physical server includes a storage unit that divides physical resources into a plurality of groups and stores priority group setting information containing priority groups for setting different numbers of VMs capable of sharing physical resources, and a priority group changing unit that receives an instruction for changing a priority group and changes a priority group to which a VM belongs, with reference to the priority group setting information. In addition, the controller includes a priority changing determination unit that determines insufficient performance and excessive performance for a calculated performance value on the basis of the resource use amount of the VM and performs the instruction for changing a priority group of the VM.

Abstract: Disclosed are non-limiting examples of systems, apparatus, methods and computer program products for dynamically configuring a process associated with an application based on environmental characteristics monitored by a mobile device. In some implementations, the process includes stages and decision nodes. There is a configurable flow along a path defined by a subset of the stages controlled by a subset of the decision nodes. Contextual data is provided by the mobile device on which the application is usable. The contextual data indicates one or more environmental characteristics associated with a physical environment in which the mobile device is or has been located. The contextual data can be provided as a control input to a decision node associated with a current stage of the process. The process can be configured accordingly.

Abstract: An illustrative embodiment disclosed herein is an apparatus including a processor having programmed instructions to receive a request for a computing device associated with a user to remotely access a session on an operating system of a non-domain joined instance (non-DJI) virtual machine (VM). The processor includes programmed instructions to, automatically and responsive to the request, cause a server to attach, to the VM, a disk for storing a profile of the user, assign an account of the operating system to the user, reset a password for the account, login the user to the session on the VM using the account and the password, mount, on the VM, the profile, and cause a connection to be established between the computing device and the VM.

Abstract: A customer having a deployment in a resource provider environment can request one or more changes to the deployment using one or more application programming interface (API) requests. Along with the one or more changes, the customer can specify one or more metrics or behaviors, or a function thereof, to be monitored for the deployment for at least a period of time after the change is implemented. The customer can also specify acceptable or unacceptable values or ranges for the metrics. If the value of a specified metric is determined during the monitoring to have an unacceptable value, the change can be automatically rolled back or undone. The roll back in some embodiments takes the form of a change in state to yet another state that will cause the deployment to operate similar to a state before the change was implemented.

Abstract: Methods, systems, and computer-readable media for automated management of machine images are disclosed. A machine image management system determines that a trigger for a machine image build process has occurred. The machine image management system performs the machine image build process responsive to the trigger. The machine image build process generates a machine image, and the machine image comprises a plurality of operating system components associated with an application. The machine image is validated by the machine image management system for compliance with one or more policies. The machine image management system provides the machine image to one or more recipients. One or more compute resources are launched using the machine image, and the application is executed on the compute resource(s) launched using the machine image.

Abstract: Some embodiments of the invention provide a method for connecting deployed machines in a set of one or more software-defined datacenters (SDDCs) to a virtual private cloud (VPC) in an availability zone (AZ). The method deploys network plugin agents (e.g. listening agents) on multiple host computers and configures the network plugin agents to receive notifications of events related to the deployment of network elements from a set of compute deployment agents executing on the particular deployed network plugin agent's host computer. The method, in some embodiments, is performed by a network manager that receives notifications from the deployed network plugin agents regarding events relating to the deployed machines and, in response to the received notifications, configures network elements to connect one or more sets of the deployed machines.

Abstract: A virtualization partition (VP) is executed by a virtualization layer. The VP contains guest software that executes in isolation within the VP. The guest software has a background task (BT) that needs to be performed in the future. The BT is virtualized by a BT service that executes outside of the VP. The guest software registers the BT, through the virtualization layer, with a BT virtualization service. An event occurs outside of the VP that triggers the BT. The BT virtualization service responds to the event by assuring that the VP is available (executing), and optionally triggers (possibly indirectly) the execution of code in the application.

Abstract: Described embodiments provide systems and methods for a management service using virtual delivery agent measurement metrics to determine the probability of the virtual delivery agent successfully launching a connection to a virtual application and desktop service. A probability mass function is implemented to determine the correlation between the measurement metrics over time, and the probability mass function distribution is mapped to a states in a linear Markov chain such that the probability of the virtual delivery agent successfully launching a connection to a virtual application and desktop service is based on the current state of the Markov chain.

Abstract: A system and method for deploying a virtual network function (VNF) are disclosed. Deploying a VNF includes receiving a request to instantiate a VNF in a network virtualization infrastructure, obtaining input from a user providing parameters needed for performing the instantiation of the VNF, determining a type of deployment for the VNF, and adding parameters inferred from the type of deployment to the user data to complete the parameters needed for deployment of the VNF, wherein the added parameters are inferred based on stored data regarding previous instantiations of the VNF. Determining the type of deployment for the VNF includes determining a number of instances of the VNFs to be deployed and a number of virtual infrastructure managers that will be instructed to deploy resources needed by the VNF.

Abstract: Disclosed herein are techniques for identifying sources of software-based malfunctions. Techniques include identifying a potential software malfunction in a system, the system having multiple code sets associated with a plurality of different software sources; accessing a line-of-code behavior and relation model representing execution of functions of the code sets; identifying, based on the line-of-code behavior and relation model, a code set determined to have the potential to cause, a least in part, the potential software malfunction; and determining a source identifier of the identified code set.

Abstract: Executing and managing flow plans by performing at least the following: receiving an indication to initiate a task flow including a plurality of discrete but related operations at a customer instance environment of a cloud-based computing platform; obtaining a definition of the task flow identifying run-time requirements for each of the plurality of operations; determining a first execution environment for the first of the plurality of operations; initiating execution of the first operation in the first execution environment; and determining the proper execution environment for subsequent operations of the task flow until all operations of the task flow are complete. Factors, such as look-ahead optimization, environmental operational capabilities, access and security requirements, current load, future load, etc. may be considered when determining the proper execution environment for a given operation.