The present disclosure describes a technique for honoring virtual machine placement constraints established on a first host implemented on a virtualized computing environment by receiving a request to migrate one or more virtual machines from the first host to a second host and without violating the virtual machine placement constraints, identifying an architecture of the first host, provisioning a second host with an architecture compatible with that of the first host, adding the second host to the cluster of hosts, and migrating the one or more virtual machines from the first host to the second host.

The present disclosure provides a method and system for selecting an optimal edge computing node in an Internet of vehicle (IoV) environment. The method includes: acquiring and analyzing properties of computing tasks of a vehicle in the IoV environment; acquiring and analyzing properties of different edge computing nodes; computing matching degrees between the properties of the computing tasks and the properties of the nodes; analyzing computing demands of different tasks, and assigning weights to different types of matching degrees; and selecting a node having an optimal sum for products of the matching degrees and the weights as an optimal edge computing node to compute each of the computing tasks of the vehicle.

The present disclosure relates to resource scheduling methods and apparatuses. In one example method, a scheduling node receives a task. The scheduling node obtains a target execution duration level to which the task belongs, where the target execution duration level represents a time length, and the target execution duration level indicates to use a target compute module of a target compute node in multiple compute nodes to execute the task. The scheduling node sends the task to the target compute node.

Systems and methods take into account the criticality of workloads, the warranty needs of workloads, the warranty available time, and the lifetime of a workload to provide an optimal solution that ensures servers are used to highest extent. The warranty health of servers is computed and categorized as critical, warning, or healthy based on the number of days remaining in warranty. Workloads are tagged as short-term or long-term workloads. Workloads are also classified based on criticality. The quarantine mode for proactive high availability of servers is divided into multiple modes, including a long-time, critical-workload quarantine mode, a critical-workload quarantine mode, and a standard quarantine mode. Servers that are in quarantine mode are assigned new workloads based upon the warranty health, workload term, and workload criticality.

Information representative of a graph-based program specification has a plurality of components, each of which corresponds to a task, and directed links between ports of said components. A program corresponding to said graph-based program specification is executed. A first component includes a first data port, a first control port, and a second control port. Said first data port is configured to receive data to be processed by a first task corresponding to said first component, or configured to provide data that was processed by said first task corresponding to said first component. Executing a program corresponding to said graph-based program specification includes: receiving said first control information at said first control port, in response to receiving said first control information, determining whether or not to invoke said first task, and after receiving said first control information, providing said second control information from said second control port.

In one embodiment, an apparatus comprises a memory and a processor. The memory is to store visual data associated with a visual representation captured by one or more sensors. The processor is to: obtain the visual data associated with the visual representation captured by the one or more sensors, wherein the visual data comprises uncompressed visual data or compressed visual data; process the visual data using a convolutional neural network (CNN), wherein the CNN comprises a plurality of layers, wherein the plurality of layers comprises a plurality of filters, and wherein the plurality of filters comprises one or more pixel-domain filters to perform processing associated with uncompressed data and one or more compressed-domain filters to perform processing associated with compressed data; and classify the visual data based on an output of the CNN.

The present disclosure relates to scheduling compute nodes to satisfy a multidimensional request using vectorized representations. One method includes receiving a request to allocate resources of a distributed virtual environment for a workload, wherein the distributed virtual environment includes a plurality of compute nodes, receiving, for each compute node, node matrix and a utilization vector, determining a mask vector, wherein the mask vector represents constraints associated with the workload, concatenating the plurality of node matrices to form a concatenated matrix, determining a utilization matrix based on the plurality of utilization vectors, and selecting a particular compute node for the workload based on the mask vector, a portion of the concatenated matrix, and the utilization matrix.

A computer-implemented method, a computer program product, and a computer system for placements of workloads in a system of multiple platforms as a service. A computer detects a triggering event for modifying a matrix that pairs respective workloads on respective platforms and includes attributes of running respective workloads on respective platforms. The computer recalculates the attributes in the matrix, in response to the triggering event being detected. The computer determines optimal placements of the respective workloads on the respective platforms, based on information in the matrix. The computer places the respective workloads on the respective platforms, based on the optimal placements.

Methods and systems disclosed herein relate generally to evaluating resource loads to determine when to transform queues and to specific techniques for transforming at least part of queues so as to correspond to alternative resources

A computer-implemented method, a computer program product, and a computer system for optimizing workload placements in a system of multiple platforms as a service. A computer first places respective workloads on respective platforms that yield lowest costs for the respective workloads. The computer determines whether mandatory constraints are satisfied. The computer checks best effort constraints, in response to the mandatory constraints being satisfied. The computer determines a set of workloads for which the best effort constraints are not satisfied and determines a set of candidate platforms that yield the lowest costs and enable the best effort constraints to be satisfied. From the set of workloads, the computer selects a workload that has a lowest upgraded cost and updates the workload by setting an upgraded platform index.