Abstract: A device may receive a state chart generated via a technical computing environment. The state chart may include a state block with a single section of textual code. The single section of textual code may include a time-based portion and an output portion. The device may initiate execution of the state chart, and may process, during the execution of the state chart, the time-based portion with a graphical engine of the technical computing environment to generate a time-based method. The device may process, during the execution of the state chart, the output portion with a textual engine of the technical computing environment to generate an output method. The device may provide the time-based method and the output method in output code, where the time-based method may be separate from the output method in the output code.

Abstract: A preempt of a live migratable workload, or job, in a distributed computing environment is performed, allowing it to release its resources for use by a higher priority workload by moving to another place in the distributed computing environment without interruption. A job scheduler receives a request to schedule a higher priority job, wherein resources needed to run the higher priority job are already dedicated for use by a currently running lower priority job. A dummy job is scheduled at a highest priority that is a copy of the lower priority job. Resources required to run the dummy job are reserved. A live migration of the lower priority job to another host is initiated, and its resources are then released. Upon a successful completion of the live migration of the lower priority job, the higher priority job is then dispatched to run using the now released resources.

Abstract: Provided is a computer system including a first processor disposed in a first zone, a second processor disposed in a second zone, a prioritizing unit, and a scheduling unit. The prioritizing unit prioritizes the first processor and the second processor based on the thermal conditions of the first zone and the second zone, respectively. The scheduling unit schedules a task to one of the first processor and the second processor according to the priority provided by the prioritizing unit.

Abstract: Virtual machines are allocated among servers in a virtual environment, whereby each virtual machine has a current placement. A current fitness score is calculated for each virtual machine based on its current placement. Proposed placement plans are then generated, each plan including a proposed placement of each virtual machine. For each plan, a plan score is created. Each plan score is created by calculating a proposed fitness score for each virtual machine based on a proposed placement of that virtual machine in accordance with that plan, generating a virtual machine score for each virtual machine based on a comparison of that virtual machine's current fitness score and proposed fitness score, and then combining the virtual machine scores. The plan scores are then compared, and a target plan is selected from among the plans. The virtual machines are then reallocated among the servers in accordance with the target proposed placement plan.

Abstract: Systems and methods relate to indexing of performance and cost of workloads in a computing environment. The computing environment may include a virtualized computing environment. According to some embodiments, a performance index relating to a plurality of workloads executing in the computing environment may be generated, where the performance index is based at least in part on performance and cost of use of one or more resources in the computing environment by the plurality of workloads. The index may be normalized. If the performance or cost of a particular workload departs from an expected performance or cost determined from the average performance and/or cost in the computing environment, resources may be reallocated to the workloads such that the performance or cost of the workload is closer to its expected performance or cost based on the performance index.

Abstract: Virtual machines are allocated among servers in a virtual environment, whereby each virtual machine has a current placement. A current fitness score is calculated for each virtual machine based on its current placement. Proposed placement plans are then generated, each plan including a proposed placement of each virtual machine. For each plan, a plan score is created. Each plan score is created by calculating a proposed fitness score for each virtual machine based on a proposed placement of that virtual machine in accordance with that plan, generating a virtual machine score for each virtual machine based on a comparison of that virtual machine's current fitness score and proposed fitness score, and then combining the virtual machine scores. The plan scores are then compared, and a target plan is selected from among the plans. The virtual machines are then reallocated among the servers in accordance with the target proposed placement plan.

Abstract: An information processing apparatus includes an inclusion relation memory, a correspondence relation memory, a data type identifying unit, a software application identifying unit, and a display controller. The inclusion relation memory stores inclusion relation between multiple data types. The correspondence relation memory stores correspondence relation between the data types and software applications used in input of data. The data type identifying unit analyzes acquired information to identify a data type corresponding to an input area of the acquired information. The software application identifying unit identifies a software application corresponding to each data type included in the identified data type in accordance with the inclusion relation and the correspondence relation. The display controller displays a display part in which the identified software application is used in a display.

Abstract: A method and device for determining parallelism of tasks of a program comprises generating a task data structure to track the tasks and assigning a node of the task data structure to each executing task. Each node includes a task identification number and a wait number. The task identification number uniquely identifies the corresponding task from other currently executing tasks and the wait number corresponds to the task identification number of a node corresponding to the last descendant task of the corresponding task that was executed prior to a wait command. The parallelism of the tasks is determined by comparing the relationship between the tasks.

Abstract: A method for workload balancing among a plurality of physical machines hosting a plurality of virtual machines (VMs) is disclosed. The method comprises periodically measuring a utilization of each hardware resource in each of the plurality of physical machines; computing a resource utilization score for each hardware resource based on its respective measured utilization; computing a total physical machine utilization score for each physical machine based on the computed resource utilization scores of its respective resources; and upon reception of a client request corresponding to a software application, selecting one physical machine of the plurality of physical machines to serve the client request, wherein the selection is based on the computed total physical machine utilization.

Abstract: Optimizing operational costs in a computing environment includes identifying high-cost jobs that are executed to generate one or more reports in the computing environment, identifying one or more reports the generation of which is dependent on the execution of the high-cost jobs, and culling at least a first job from among the high-cost jobs, in response to determining that a benefit achieved from the reports that depend on the first job does not justify costs associated with generating the reports.

Abstract: Systems and methods may provide a set of cores capable of parallel execution of threads. Each of the cores may run code that is provided with a progress meter that calculates the amount of work remaining to be performed on threads as they run on their respective cores. The data may be collected continuously, and may be used to alter the frequency, speed or other operating characteristic of the cores as well as groups of cores. The progress meters may be annotated into existing code.

Abstract: Embodiments relate to a method for encapsulating a hardware application for virtualization. The method surrounds the hardware application with a service layer controller and ring interfaces. The ring interfaces dictates a virtual function that the hardware application is running. The method controls the hardware application so that the hardware application is reset in between each of a plurality of running jobs. The method tags, by the ring interfaces, each of a plurality of requests with an identifier signifying a virtual function that the respective request belongs to. The method ensures that there are not any outstanding requests following a quiesce of the hardware application.

Abstract: Systems and methods are disclosed for scheduling a plurality of tasks for execution on one or more processors. An example method includes obtaining a counter value of a counter. The method also includes for each work queue of a plurality of work queues, identifying an execution period of the respective work queue and comparing a counter value to an execution period of the respective work queue. Each work queue includes a set of tasks and is defined by an execution period at which to run the respective set of queued tasks. The method further includes selecting, based on the comparing, a subset of the plurality of work queues. The method also includes scheduling a set of tasks of slower frequency queued in a selected work queue for execution on one or more processors before a set of tasks queued in a non-selected work queue. The work items may be scheduled in O(1) because the design inherently prioritizes the tasks based on the urgency of their completion, and may do so by resetting a work queue pointer.

Abstract: A method comprises, receiving, at each of a plurality of computing devices, a task execution estimation request message from a central server, the task execution estimation request message comprising a worst-case execution time (WCET) corresponding to the computing device. The method further comprises, computing, by each of the plurality of computing devices, an estimate task execution time for the task based on the WCET and a state transition model corresponding to the computing device, wherein the state transition model indicates available processing resources corresponding to the computing device. Further, the method comprises transmitting, by each of the plurality of computing devices, the estimate task execution time to the central server for allocation of the task to a computing device from amongst the plurality of computing devices based on the estimate task execution time corresponding to the computing device.

Abstract: A storage unit stores information indicting the past execution records of a plurality of related tasks. An operating unit obtains information indicating the execution status of a plurality of tasks. After obtaining the information, the operating unit obtains the execution status of an unexecuted task included in the plurality of related tasks, which was not yet executed, at an estimated time by which the unexecuted task is completed and which is estimated based on the information stored in the storage unit.

Abstract: In one implementation, a secure operation system initiates a secure operation associated with a virtual machine hosted at a secured host, and determines when the secure operation is complete. In response to determining that the secure operation is complete, the secure operation system migrates the virtual machine to a host other than the secured host.

Abstract: In one embodiment, a method includes empirically analyzing a set of active reservations and a current set of consumable resources belonging to a class of consumable resources. Each active reservation is of a managed task type and includes a group of one or more tasks requiring access to a consumable resource of the class. The method further includes, based on the empirically analyzing, clocking the set of active reservations each clocking cycle. In addition, the method includes, responsive to the clocking, sorting a priority queue of the set of active reservations.

Abstract: Described embodiments provide for controlling a plurality of virtual machines in a cloud computing system. At least one virtual storage allocated to the plurality of virtual machines may be monitored. Based on the monitoring result, a virtual storage in a service unable state may be detected among the monitored at least one virtual storage. A virtual machine associated with the detected virtual storage may be temporarily interrupted.

Abstract: A method and apparatus for managing backlogged tasks are disclosed. In the method and apparatus, upon receiving a task pertaining to a requestor group, a number of outstanding tasks associated with the requestor group is determined and the task is submitted for processing if the number of outstanding tasks is within an allowable range. If the number of outstanding tasks is outside of the allowable range, take one or more actions may be taken, which may include rejecting the request.

Abstract: A system optimizes a number of shared server processes executing on a processor. The system creates, in a memory, a data array for storing a plurality of performance metric values, each associated with a number of shared server processes. The system selects a value for an optimized number of shared server processes according to a first procedure based on the performance metric, observes a performance metric associated with the selected optimized number, and stores, in the data array, the observed performance metric. The system repeats the selecting, observing and storing until at least a predetermined number of contiguous data values are stored in the data array. The system selects the value for the optimized number according to a second procedure based on a slope of the performance metric. The system observes the performance metric associated with the selected optimized number, and stores, in the data array, the observed performance metric.