Abstract: The present disclosure relates to systems, methods, and computer readable media for reducing placement conflicts between allocation agents tasked with allocating computing resources on a computing zone. For example, systems disclosed herein may identify placement conflicts between multiple allocation agents processing incoming placement requests on a computing zone. The systems disclosed herein may further modify a placement policy based on the number of placement conflicts by reducing one or more restrictions on rules for allocating computing resources. For example, the systems disclosed herein may enlarge a placement zone of server nodes when allocating computing resources to prevent or otherwise reduce conflicts between multiple allocation agents implementing the same placement policies.

Abstract: A method for evaluating at least one potential policy for an IaaS system may include determining a predicted workload for the IaaS system based on at least one generative model corresponding to the IaaS system. The at least one potential policy for the IaaS system may be simulated based on the predicted workload, thereby producing one or more simulation metrics that indicate effects of the at least one potential policy. The performance of the IaaS system may be optimized based on the one or more simulation metrics.

Abstract: Techniques for scheduling processing tasks in a device having multiple computing elements are disclosed. A network interface controller of the device receives processing tasks, for execution on the computing elements, from a network that is external to the device. The network interface controller schedules the tasks for execution on the computing devices based on policy data available to the network interface controller. A scheduler within the network interface controller, which can be implemented as a standalone processing unit (such as a microcontroller, a programmable processing core, or an application specific integrated circuit), performs such scheduling, thereby freeing the central processing unit of the device from the burden of performing scheduling operations. The scheduler schedules the tasks according to any technically feasible scheduling technique.

Abstract: A method for evaluating at least one potential policy for an IaaS system may include determining a predicted workload for the IaaS system based on at least one generative model corresponding to the IaaS system. The at least one potential policy for the IaaS system may be simulated based on the predicted workload, thereby producing one or more simulation metrics that indicate effects of the at least one potential policy. The performance of the IaaS system may be optimized based on the one or more simulation metrics.

Abstract: Techniques for scheduling processing tasks in a device having multiple computing elements are disclosed. A network interface controller of the device receives processing tasks, for execution on the computing elements, from a network that is external to the device. The network interface controller schedules the tasks for execution on the computing devices based on policy data available to the network interface controller. A scheduler within the network interface controller, which can be implemented as a standalone processing unit (such as a microcontroller, a programmable processing core, or an application specific integrated circuit), performs such scheduling, thereby freeing the central processing unit of the device from the burden of performing scheduling operations. The scheduler schedules the tasks according to any technically feasible scheduling technique.