In one example in accordance with the present disclosure, an electronic device is described. An example electronic device includes a processor and memory storing executable instructions that when executed cause the processor to determine availability of memory resources and processing resources of multiple computing devices. The instructions also cause the processor to form a computing cluster based on the availability of the memory resources and the processing resources. The instructions further cause the processor to assign a computing task to the computing cluster to replace a cloud service.

Implementations for scheduling a sub-idle thread priority class are described. An example method may include assigning, by a scheduler of a computer system, a sub-idle execution priority class to a code optimization thread; responsive to determining that a central processing unit has been idle for at least a threshold period of time, running the code optimization thread; and generating, by the code optimization thread, an optimized code of a program executable by the computer system. The method may further execute the optimized code.

The exemplary embodiments are related to a device, a system, and a method for implementing a hardware mechanism that is configured to validate the performance of scheduling software utilized by a safety-critical system. The hardware device may receive an indication that a first frame of a frame schedule is in use. The hardware device may also monitor a time parameter corresponding to the first frame. The hardware device may also determine whether an indication that a second frame of the frame schedule is in use is received prior to the expiration of the time parameter. When the indication that the second frame of the frame scheduler is in use is not received prior to the expiration of time parameter, the hardware device may send a signal to an operating system of the safety-critical system indicating that an error in executing the frame scheduled has occurred.

Methods, computer program products, and/or systems are provided that perform the following operations: determining a pacing requirement for host requests based on one or more thresholds; setting a pacing delay level based on the one or more thresholds in response to the determination of the pacing requirement; and implementing a memory request flow for a host request based on the pacing requirement and the pacing delay level.

A time-aware application task scheduling system for a green data center (GDC) that includes a task scheduling processor coupled to one or more queue processors and an energy collecting processor connected to one or more renewable energy sources and a grid power source. The systems is capable of determining a service rate for a plurality of servers to process a plurality of application tasks in the GDC and scheduling, via processing circuitry, one or more of the application tasks to be executed in one or more of the servers at a rate according to a difference in an accumulated arriving rate for the plurality of application tasks into the one or more queues and a removal rate for the plurality of application tasks from the one or more queues. The system is further capable of removing the one or more application tasks from their associated queues for execution in the scheduled one or more servers.

Execution speed of a thread is dynamically restored while reducing influence on processing accuracy of the whole system. A thread synchronization device (10) achieves synchronization among N threads (1-1, . . . , 1-N) to perform parallel processing by dividing time-series data into a plurality of pieces of data. Environment variables which achieve a trade-off between processing accuracy and execution speed are individually set at the threads (1-1, . . . , 1-N). An execution speed calculation unit (2) calculates execution speed of each of the threads. An environment variable update unit (3) updates the environment variables in accordance with the execution speed of the threads.

Cloud-based computing systems, although claimed to have virtually unlimited resources, could get oversubscribed due to budget constraints of cloud users. The disclosed invention proposes a mechanism to identify various types of “mergeable” tasks. The system also determines when it is appropriate to aggregate tasks and how to allocate them so that the QoS of other tasks is not affected. Experimental results under real-world workload settings show that the disclosed system can improve robustness of the system in the face of oversubscription and also saves the overall time of using cloud services by more than 14%.

In one embodiment, a method for margin determination for a computing system with a real time operating system and priority preemptive scheduling comprises: scheduling a set of tasks to be executed in one or more partitions, wherein each is assigned a priority, wherein the tasks comprise periodic and/or aperiodic tasks; executing the set of tasks on the computing system within the scheduled periodic time window; introducing an overhead task executed for an execution duration controlled either by the real time operating system or by the overhead task; controlling the overhead task to converge on a point of failure at which a length of the execution duration of the overhead task causes either: 1) a periodic task to fail to execute within a deadline, or 2) time available for the aperiodic tasks to execute to fall below a threshold; and defining a partition margin corresponding to the point of failure.

A method for computation may include performing a first computation using a first system, wherein the first computation may be based, at least in part, on a first computation basis, performing a second computation using a second system, wherein the second computation may be based, at least in part, on a second computation basis, and coordinating the first computation and the second computation. The first computation basis may include a clock basis, and the second computation basis may include an event basis. The first computation may include a first operation, the second computation may include a second operation, and the coordinating the first computation and the second computation may include coordinating the first computation and the second computation based on the first operation and the second operation. The first operation may include an application computation operation, and the second operation may include a device computation operation.

A computer-implemented method according to one embodiment includes receiving data associated with a driver performing actions. At least some of the actions trigger events emitted by an event emitter. Information, from the received data, about the performed actions is logged in an action log. An event observer is instructed to log the events emitted by the event emitter that the event observer observes. The observed events are logged in an event log. The information of the action log and information of the event log is compared based on a rule, and a validity of the event emitter is determined based on results of the comparing. A computer program product according to another embodiment includes a computer readable storage medium having program instructions embodied therewith. The program instructions are readable and/or executable by a processor to cause the processor to perform the foregoing method.