Abstract: Various examples are disclosed for autonomously reproducing and destructing virtual machines. Various virtual machines can be provisioned in a computing environment having at least one computing device. Each of the virtual machines provisioned can process a workload assigned to the at least one computing device. Further, each of the virtual machines can independently determine whether to self-replicate or self-destroy based at least on a state of processing of the workload. To self-replicate, each of the virtual machines can cause a new virtual machine to be created, where a processing of the workload is divided between a virtual machine and the new virtual machine created.

Abstract: An information processing device according to one aspect includes a memory, a processor coupled to the memory, and one or more physical storage devices. The processor is configured to execute a configuration process of configuring a plurality of storage areas by dividing the one or more physical storage devices, a first synchronization process of initializing at least one of the storage areas and of synchronizing data stored in the initialized storage area with another information processing device, and a second synchronization process of writing file data used for generating a virtual machine to the storage area initialized and synchronized by the first synchronization process, and of synchronizing the file data written to the storage area with the another information processing device.

Abstract: In response to a request for launching a program, a list of one or more application frameworks to be accessed by the program during execution of the program is determined. Zero or more entitlements representing one or more resources entitled by the program during the execution are determined. A set of one or more rules based on the entitlements of the program is obtained from at least one of the application frameworks. The set of one or more rules specifies one or more constraints of resources associated with the at least one application framework. A security profile is dynamically compiled for the program based on the set of one or more rules associated with the at least one application framework. The compiled security profile is used to restrict the program from accessing at least one resource of the at least one application frameworks during the execution of the program.

Abstract: An apparatus may include first and second processors. A first user may be bound to the first processor such that processes of the first user execute on the first processor and do not execute on the second processor. A second user may be bound to the second processor such that processes of the second user execute on the second processor and do not execute on the first processor.

Abstract: There are provided an information processing system that operates virtual machines and storage controllers on a processor, and an information processing method executed by the information processing system. A storage controller group capable of taking over processing between the storage controllers arranged in different nodes is provided. The virtual machine is movable between the different nodes by deploy. The virtual machine and the storage controller that processes data input and output by the virtual machine are arranged in the same node. A combination of the virtual machines that cannot be arranged in the same node is defined by a restriction. A management unit arranges one of the virtual machines that cannot be arranged in the same node in the node in which the storage controller included in the storage controller group to which the storage controller used by the other virtual machine belongs is not arranged.

Abstract: A cloud management system receives a request initiated by a requestor to provide an execution platform to execute one or more applications, identifies a set of computing resources comprising at least one cloud-controlled computing resource and at least one internally-controlled computing resource, and creates the execution platform comprising at least one cloud-controlled computing resource and at least one internally-controlled computing resource. Further, the cloud management system instantiates the one or more applications on the execution platform provides, to the requestor, access to the one or more applications, and responsive to determining that the request has expired, de-allocates the set of computing resources.

Abstract: A plurality of processing entities are maintained. A plurality of task control block (TCB) groups are generated, wherein each of the plurality of TCB groups are restricted to one or more different processing entities of the plurality of processing entities. A TCB is assigned to one of the plurality of TCB groups, at TCB creation time.

Abstract: An HTM-assisted Combining Framework (HCF) may enable multiple (combiner and non-combiner) threads to access a shared data structure concurrently using hardware transactional memory (HTM). As long as a combiner executes in a hardware transaction and ensures that the lock associated with the data structure is available, it may execute concurrently with other threads operating on the data structure. HCF may include attempting to apply operations to a concurrent data structure utilizing HTM and if the HTM attempt fails, utilizing flat combining within HTM transactions. Publication lists may be used to announce operations to be applied to a concurrent data structure. A combiner thread may select a subset of the operations in the publication list and attempt to apply the selected operations using HTM. If the thread fails in these HTM attempts, it may acquire a lock associated with the data structure and apply the selected operations without HTM.

Abstract: Devices and methods of providing performance measurements (PMs) for Network Function Virtualization are generally described. A Virtual Network Function (VNF) PM job is scheduled at a VNF and VNF PM data received in response. From the VNF PM data, it is determined that virtualized resource (VR) management may be a cause of poor VNF performance. A VR PM job is scheduled and results in VR PM data. The VR PM and VNF PM data are analyzed to determine whether to increase the VR at the VNF. If an increase is determined, a request for the increase is transmitted from an element manager to a VNF manager or the VNF PM and/or VR PM data are provided to a Network Manager (NM) for the NM to request the increase by a Network Function Virtualization Orchestrator (NFVO).

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for implementing blockchain contracts are provided. One of the methods includes: obtaining bytecode of a blockchain contract, wherein the bytecode comprises an indicator indicating a type of the blockchain contract; determining a virtual machine corresponding to the type based at least on the indicator; and triggering the determined virtual machine to execute the blockchain contract.

Abstract: Embodiments include computing devices, apparatus, and methods implemented by a computing device for task scheduling in the presence of task conflict edges on a computing device. The computing device may determine whether a first task and a second task are related by a task conflict edge. In response to determining that the first task and the second task are related by the task conflict edge, the computing device may determine whether the second task acquires a resource required for execution of the first task and the second task. In response to determining that the second task fails to acquire the resource, the computing device may assign a dynamic task dependency edge from the first task to the second task.

Abstract: In some implementations, a user device can schedule tasks based on user behavior. For example, the user device can receive a task request that includes a time window and user/device context parameters for performing the task. The user device can predict a time when the user/device context is optimal for performing the task during the time window based on historical context data. For example, the user device can generate an optimal context score for the task based on the context parameters and the historical context data. The user device can execute the requested task at a current time within the time window when a context score for the current context exceeds a threshold determined based on the optimal context score.

Abstract: Computing environments are scalably generated and provisioned based on metadata associated with separately defined containers of groups of sandboxes. Each container of a group of sandboxes is its own digital construct and is wrapped with metadata that defines the nature and aspects of the grouping, including enumerating individual members of a team, factors or attributes of assignments, and the like. The metadata, therefore, identifies the sandboxes to generate and, within those sandboxes, the computing environments to generate and how such computing environments are to be provisioned. In variable team settings quantities and identities of individuals can be unknown and can dynamically change. Administrators provide team definitions without rosters, or with rosters that comprise only previously registered individuals.

Abstract: Computing environments can be scalably generated and provisioned based on one or more team definitions and definitions of one or more projects assigned to a particular team. Digital constructs can be generated to represent the teams and the projects assigned to such teams, and such digital constructs can comprise metadata identifying sandboxes to generate and, within those sandboxes, computing environments to generate and how such computing environments are to be provisioned. The sandboxes can be generated in parallel and the computing environments can be provisioned in parallel, or staggered based on individual user access. Assignment of consumption limitations can be individually adjusted from default values, and monitoring of such consumption limitations can be performed on an aggregated basis.

Abstract: Devices and methods of providing performance measurements (PMs) for Network Function Virtualization are generally described. A Virtual Network Function (VNF) PM job is scheduled at a VNF and VNF PM data received in response. From the VNF PM data, it is determined that virtualized resource (VR) management may be a cause of poor VNF performance. A VR PM job is scheduled and results in VR PM data. The VR PM and VNF PM data are analyzed to determine whether to increase the VR at the VNF. If an increase is determined, a request for the increase is transmitted from an element manager to a VNF manager or the VNF PM and/or VR PM data are provided to a Network Manager (NM) for the NM to request the increase by a Network Function Virtualization Orchestrator (NFVO).

Abstract: A method performed by a physical computing system includes, with a hypervisor, presenting a virtualized Remote Direct Memory Access (RDMA) device to a guest, with the hypervisor, allocating a portion of total guest memory to the guest, with the hypervisor, determining a memory threshold for the guest, the memory threshold being based on a number of virtual machines managed by the hypervisor and a size of total guest memory, with the hypervisor, receiving from the guest, a first request to register a first size sub-portion of the portion of total guest memory to the virtualized RDMA device, and with the hypervisor, in response to determining that the first size sub-portion exceeds the memory threshold, returning a notification to the guest, the notification indicating that the first request failed. The first size sub-portion is less than the portion of total guest memory.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for implementing blockchain contracts are provided. One of the methods includes: obtaining bytecode of a blockchain contract, wherein the bytecode comprises an indicator indicating a type of the blockchain contract; determining a virtual machine corresponding to the type based at least on the indicator; and triggering the determined virtual machine to execute the blockchain contract.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for executing blockchain contracts are provided. One of the methods includes: obtaining a bytecode of a blockchain contract, wherein the bytecode comprises one or more indicators, and the one or more indicators comprise a first indicator indicating a virtual machine type for executing the blockchain contract; and executing the blockchain contract using a virtual machine of the virtual machine type associated with the first indicator.

Abstract: This disclosure describes techniques implemented by an event scheduling system for using an ensemble of event schedulers to achieve higher precision when generating a tick at a desired time at which an event is scheduled to occur. Event scheduling systems that schedule events at scale may utilize event schedulers that have relatively low precision in order to provide the scheduling services at scale. By utilizing an ensemble, or group, of event schedulers to generate a tick at a particular time for a scheduled event, the ticks generated by the ensemble of event schedulers may be distributed throughout jitter delay such that likelihood that one of the ticks is close to the scheduled time for the event is increased. Thus, the techniques described herein may provide higher precision scheduling techniques for use by event scheduling systems.

Abstract: The disclosed embodiments relate to a system for monitoring a virtual-machine environment. During operation, the system identifies a parent and a set of two or more child components that are related to the parent component in the virtual-machine environment. Next, the system determines a performance metric for each child component in the set of two or more child components. The system then determines a child-component performance state for each child component in the set of two or more child components based on the performance metric for the child component and a child-component state criterion. Finally, the system determines a parent state for the parent component based on the child-component performance state for each child component in the set of two or more child components and a parent-component state criterion, wherein the parent-component state criterion includes a threshold percentage or number of child components that have a specified state.