Abstract: A search space for allocating a virtual machine is pruned. An allocation request for allocating a virtual machine to a plurality of clusters is received. A valid set of clusters is generated. The valid set of clusters includes clusters of the plurality of clusters that satisfy the allocation request. An attribute associated with the allocation request is identified. A truncation parameter is determined, by a trained search space classification model, based on the identified attribute. The valid set of clusters is filtered based on the truncation parameter. A server is selected from the filtered valid set of clusters. The virtual machine is allocated to the selected server. In an aspect of the disclosure, a search space pruner generates an analysis summary based on an analysis of received telemetry data. The search space pruner trains the search space classification model to determine truncation parameters based on the analysis summary.

Abstract: The present application relates to a network, apparatus, and method for resource allocation in a distributed wide area network including a plurality of computing resources configured to instantiate virtual machines. A workload manager for the network predicts, using one or more machine-learning algorithms, based on network utilization and eviction information, a set of stable interruptible virtual machine (IVM) resources having an IVM capacity. The network manager receives a plurality of requests for interruptible workloads. The network manager allocates the interruptible workloads to the stable IVM resources up to the IVM capacity. The network manager may predict a rate of requests for priority uninterruptible workloads and a capacity during a future time period, determine to protect at least one of the interruptible workloads based on the rate of requests and the capacity, and guarantee resources to the at least one interruptible workload as a protected workload for the future time period.

Abstract: Described are examples for recommending increase in worker instance count for an availability zone in a cloud-based computing platform. A machine learning (ML) model can be used to predict a time series forecast of a workload for the availability zone in a future time period. A predicted number of worker instances to handle the predicted workload can be computed, and if the number of worker instances in the availability zone is less than the predicted number of worker instances, a recommendation to increase the number of worker instances in the availability zone can be generated.

Abstract: Systems and methods are taught for providing customers of a cloud computing service to control when updates affect the services provided to the customers. Because multiple customers share the cloud's infrastructure, each customer may have conflicting preferences for when an update and associated downtime occurs. Preventing and resolving conflicts between the preferences of multiple customers while providing them with input for scheduling a planned update may reduce the inconvenience posed by updates. Additionally, the schedule for the update may be transmitted to customers so that they can prepare for the downtime of services associated with the update.

Abstract: The present disclosure relates to systems, methods, and computer readable media for predicting surplus capacity on a set of server nodes and determining a quantity of deferrable virtual machines (VMs) that may be scheduled over an upcoming period of time. This determination of VM quantity may be determined while minimizing risks associated with allocation failures on the set of server nodes. This disclosure described systems that facilitate features and functionality related to improving utilization of surplus resource capacity on a plurality of server nodes by implementing VMs having some flexibility in timing of deployment while also avoiding significant risk caused as a result of over-allocated storage and computing resources. In one or more embodiments, the quantity of deferrable VMs is determined and scheduled in accordance with rules of a scheduling policy.

Abstract: Embodiments of the subject matter described herein relate to a wireless programmable media processing system. In the media processing system, a processing unit in a computing device generates a frame to be displayed based on a graphics content for an application running on the computing device. The frame to be displayed is then divided into a plurality of block groups which are compressed. The plurality of compressed block groups are sent to a graphics display device over a wireless link. In this manner, both the generation and the compression of the frame to be displayed may be completed at the same processing unit in the computing device, which avoids data copying and simplifies processing operations. Thereby, the data processing speed and efficiency is improved significantly.

Abstract: Interactive analytics are provided for resource allocation failure incidents, which may be tracked, diagnosed, summarized, and presented in near real-time for users and/or platform/service providers to understand the root cause(s) of failure incidents and actual and hypothetical, failed and successful, allocation scenarios. A capacity analyzer simulates an allocation process implemented by a resource allocation platform. The capacity analyzer may determine which resources were and/or were not eligible for allocation for a request, based on information about the resource allocation failure, resources in the region of interest, and constraints associated with the incident, and the resource allocation rules associated with the resource allocation platform. Users may quickly learn whether a request constraint, a requesting entity constraint, a capacity constraint, and/or a resource platform constraint caused a resource allocation incident.

Abstract: Described are examples for recommending increase in worker instance count for an availability zone in a cloud-based computing platform. A machine learning (ML) model can be used to predict a time series forecast of a workload for the availability zone in a future time period. A predicted number of worker instances to handle the predicted workload can be computed, and if the number of worker instances in the availability zone is less than the predicted number of worker instances, a recommendation to increase the number of worker instances in the availability zone can be generated.

Abstract: The present application is directed to stranded resource recovery in a cloud computing environment. A resource utilization signal at each of a plurality of nodes that each hosts corresponding virtual machines (VMs) is measured. Based on each resource utilization signal, a set of candidate nodes is identified. Each candidate node comprises a stranded resource that is unutilized due to utilization of a bottleneck resource. The identification includes calculating an amount of the stranded resource at each candidate node. From a plurality of VMs hosted at the set of candidate nodes, a set of candidate VMs is identified for migration for stranded resource recovery. The identification includes calculating a score for each candidate VM based on a degree of imbalance between the stranded resource and the bottleneck resource at a candidate node hosting the candidate VM. Migration of at least one candidate VM in the set of candidate VMs is initiated.

Abstract: According to implementations of the subject matter described herein, there is proposed a solution for supporting communications for an FPGA device. In an implementation, the FPGA device includes an application module and protocol stack modules. The protocol stack modules are operable to access target devices based on different communication protocols via a physical interface. The FPGA device further includes a universal access module operable to receive, from the application module, first data and a first identity of a first target device, the first target device acting as a destination of the first data, and transmit, based on the first identity and predetermined first routing information, the first data to a first protocol stack module accessible to the first target device via the physical interface. By introducing the universal access module, it is possible to provide unified and direct communications for the application module.

Abstract: A search space for allocating a virtual machine is pruned. An allocation request for allocating a virtual machine to a plurality of clusters is received. A valid set of clusters is generated. The valid set of clusters includes clusters of the plurality of clusters that satisfy the allocation request. An attribute associated with the allocation request is identified. A truncation parameter is determined, by a trained search space classification model, based on the identified attribute. The valid set of clusters is filtered based on the truncation parameter. A server is selected from the filtered valid set of clusters. The virtual machine is allocated to the selected server. In an aspect of the disclosure, a search space pruner generates an analysis summary based on an analysis of received telemetry data. The search space pruner trains the search space classification model to determine truncation parameters based on the analysis summary.

Abstract: Updates are managed across partitions in a distributed cloud allocation system. Updates are managed in a variety of dimensions, e.g., by partition, time, or upgrade domain, to maintain a sufficient number of allocator instances to maintain service. An update service may receive, organize, schedule, and deliver updates to VM allocator instances to limit service disruptions. Updates may be aggregated based on partition scope. Updates to one or more partitions may be batched in a single update. Delivery and timing of updates may be configurable on a per partition basis. Allocator instances may receive batched updates at the same or different times. An update service may dynamically adapt to prevailing service conditions if an essential update is in progress and/or request demand is above a threshold.

Abstract: Systems and methods are provided for scheduling a virtual machine (VM) to host a workload in a cloud system. In particular, the disclosed technology schedules an evicted VM for redeploying an interruptible workload. The scheduling is based on capacity prediction and inference data associated with a type of the evicted VM. Capacity signal predictor generates training data for training a machine learning model using capacity signal history data of the cloud system. The machine-learning model, once trained, predicts capacity including a rate of evictions for the types of the evicted VM. The predicted data is based on at least the current status of available computing resources. Upon receiving a notice associated with a workload interruption, the intelligent scheduler prioritizes the evicted VM for scheduling and determines whether to defer redeploying the evicted VM based on the rate of eviction for the type of the evicted VM.

Abstract: The present disclosure relates to systems, methods, and computer readable media for predicting expansion failures and implementing defragmentation instructions based on the predicted expansion failures and other signals. For example, systems disclosed herein may apply a failure prediction model to determine an expansion failure prediction associated with an estimated likelihood that deployment failures will occur on a node cluster. The systems disclosed herein may further generate defragmentation instructions indicating a severity level that a defragmentation engine may execute on a cluster level to prevent expansion failures while minimizing negative customer impacts. By uniquely generating defragmentation instructions for each node cluster, a cloud computing system can minimize expansion failures, increase resource capacity, reduce costs, and provide access to reliable services to customers.

Abstract: Systems and methods are provided for scheduling a virtual machine (VM) to host a workload in a cloud system. In particular, the disclosed technology schedules an evicted VM for redeploying an interruptible workload. The scheduling is based on capacity prediction and inference data associated with a type of the evicted VM. Capacity signal predictor generates training data for training a machine learning model using capacity signal history data of the cloud system. The machine-learning model, once trained, predicts capacity including a rate of evictions for the types of the evicted VM. The predicted data is based on at least the current status of available computing resources. Upon receiving a notice associated with a workload interruption, the intelligent scheduler prioritizes the evicted VM for scheduling and determines whether to defer redeploying the evicted VM based on the rate of eviction for the type of the evicted VM.

Abstract: The present disclosure relates to systems, methods, and computer readable media for predicting surplus capacity on a set of server nodes and determining a quantity of deferrable virtual machines (VMs) that may be scheduled over an upcoming period of time. This determination of VM quantity may be determined while minimizing risks associated with allocation failures on the set of server nodes. This disclosure described systems that facilitate features and functionality related to improving utilization of surplus resource capacity on a plurality of server nodes by implementing VMs having some flexibility in timing of deployment while also avoiding significant risk caused as a result of over-allocated storage and computing resources. In one or more embodiments, the quantity of deferrable VMs is determined and scheduled in accordance with rules of a scheduling policy.

Abstract: A computer implemented method includes receiving telemetry data corresponding to capacity health of nodes in a cloud based computing system. The received telemetry data is processed via a prediction engine to provide predictions of capacity health at multiple dimensions of the cloud based computing system. Node recoverability information is received and node recovery execution is initiated as a function of the representations of capacity health and node recoverability information.

Abstract: Embodiments of the subject matter described herein relate to a wireless programmable media processing system. In the media processing system, a processing unit in a computing device generates a frame to be displayed based on a graphics content for an application running on the computing device. The frame to be displayed is then divided into a plurality of block groups which are compressed. The plurality of compressed block groups are sent to a graphics display device over a wireless link. In this manner, both the generation and the compression of the frame to be displayed may be completed at the same processing unit in the computing device, which avoids data copying and simplifies processing operations. Thereby, the data processing speed and efficiency is improved significantly.

Abstract: The present disclosure describes a type of virtual machine, which the present disclosure may refer to as a harvest virtual machine, that may allow improved utilization of physical computing resources on a cloud-computing system. First, the harvest virtual machine may be evictable. In other words, higher priority virtual machines may preempt the harvest virtual machine's access to physical computing resources. Second, the harvest virtual machine may receive access to a dynamic amount of physical computing resources during the course of its operating life. Third, the harvest virtual machine may have a minimum size (in terms of an amount of physical computing resources) and may terminate whenever the harvest virtual machine has access to an amount of physical computing resources less than the minimum size.

Abstract: The present disclosure relates to systems, methods, and computer readable media for predicting surplus capacity on a set of server nodes and determining a quantity of deferrable virtual machines (VMs) that may be scheduled over an upcoming period of time. This determination of VM quantity may be determined while minimizing risks associated with allocation failures on the set of server nodes. This disclosure described systems that facilitate features and functionality related to improving utilization of surplus resource capacity on a plurality of server nodes by implementing VMs having some flexibility in timing of deployment while also avoiding significant risk caused as a result of over-allocated storage and computing resources. In one or more embodiments, the quantity of deferrable VMs is determined and scheduled in accordance with rules of a scheduling policy.