Abstract: Implementations are presented for utilizing probabilistic predicates (PPs) to speed up searches requiring machine learning inferences. One method includes receiving a search query comprising a predicate for filtering blobs in a database utilizing a user-defined-function (UDF). The filtering requiring analysis of the blobs by the UDF to determine blobs that pass the filtering. Further, the method includes determining a PP sequence of PPs based on the predicate. Each PP is a classifier that calculates a PP-blob probability of satisfying a PP clause. The PP sequence defines an expression to combine the PPs. Further, the method includes operations for performing the PP sequence to determine a blob probability that the blob satisfies the expression, determining which blobs meet an accuracy threshold, discarding the blobs with the blob probability less than the accuracy threshold, and executing the database query over the blobs that have not been discarded. The results are then presented.

Abstract: One or more approximations of query output in a data analytics platform are controlled. The one or more approximations are controlled by generating values of error metrics associated with placements of samplers in one or more query execution plans associated with the query, and injecting a plurality of samplers into the query execution plans, using the determined values of the error metrics, in lieu of storing samples of input to the query prior to execution of the query.

Abstract: The ensuring of predictable and quantifiable networking performance includes adaptively throttling the rate of VM-to-VM traffic flow. A receiving hypervisor can detect congestion and communicate messages for throttling traffic flow to reduce congestion at the receiving hypervisor.

Abstract: Latency in responding to queries directed to geographically distributed data can be reduced by allocating individual steps, of a multi-step compute operation requested by the query, among the geographically distributed computing devices so as to reduce the duration of shuffling of intermediate data among such devices, and, additionally, by pre-moving, prior to the receipt of the query, portions of the distributed data that are input to a first step of the multistep compute operation, to, again, reduce the duration of the exchange of intermediate data. The pre-moving of input data occurring, and the adaptive allocation of intermediate steps, are prioritized for high-value data sets. Additionally, a threshold increase in a quantity of data exchanged across network communications can be established to avoid incurring network communication usage without an attendant gain in latency reduction.

Abstract: Various technologies pertaining to scheduling network traffic in a network are described. A request to transfer data from a first computing device to a second computing device includes data that identifies a volume of the data to be transferred and a deadline, where the data is to be transferred prior to the deadline. A long-term schedule is computed based upon the request, wherein the long-term schedule defines flow of traffic through the network over a relatively long time horizon. A short-term schedule is computed based upon the long-term schedule, where devices in the network are configured based upon the short-term schedule.

Abstract: Latency in responding to queries directed to geographically distributed data can be reduced by allocating individual steps, of a multi-step compute operation requested by the query, among the geographically distributed computing devices so as to reduce the duration of shuffling of intermediate data among such devices, and, additionally, by pre-moving, prior to the receipt of the query, portions of the distributed data that are input to a first step of the multistep compute operation, to, again, reduce the duration of the exchange of intermediate data. The pre-moving of input data occurring, and the adaptive allocation of intermediate steps, are prioritized for high-value data sets. Additionally, a threshold increase in a quantity of data exchanged across network communications can be established to avoid incurring network communication usage without an attendant gain in latency reduction.

Abstract: Various technologies pertaining to scheduling network traffic in a network are described. A request to transfer data from a first computing device to a second computing device includes data that identifies a volume of the data to be transferred and a deadline, where the data is to be transferred prior to the deadline. A long-term schedule is computed based upon the request, wherein the long-term schedule defines flow of traffic through the network over a relatively long time horizon. A short-term schedule is computed based upon the long-term schedule, where devices in the network are configured based upon the short-term schedule.

Abstract: Embodiments for efficient queue management for cluster scheduling and managing task queues for tasks which are to be executed in a distributed computing environment. Both centralized and distributed scheduling is provided. Task queues may be bound by length-based bounding or delay-based bounding. Tasks may be prioritized and task queues may be dynamically reordered based on task priorities. Job completion times and cluster resource utilization may both be improved.

Abstract: The techniques and/or systems described herein are configured to determine a set of update operations to transition a network from an observed network state to a target network state and to generate an update dependency graph used to dynamically schedule the set of update operations based on constraint(s) defined to ensure reliability of the network during the transition. The techniques and/or systems dynamically schedule the set of update operations based on feedback. For example, the feedback may include an indication that a previously scheduled update operation has been delayed, has failed, or has been successfully completed.

Abstract: The techniques and/or systems described herein are configured to determine a set of update operations to transition a network from an observed network state to a target network state and to generate an update dependency graph used to dynamically schedule the set of update operations based on constraint(s) defined to ensure reliability of the network during the transition. The techniques and/or systems dynamically schedule the set of update operations based on feedback. For example, the feedback may include an indication that a previously scheduled update operation has been delayed, has failed, or has been successfully completed.

Abstract: One or more approximations of query output in a data analytics platform are controlled. The one or more approximations are controlled by generating values of error metrics associated with placements of samplers in one or more query execution plans associated with the query, and injecting a plurality of samplers into the query execution plans, using the determined values of the error metrics, in lieu of storing samples of input to the query prior to execution of the query.

Abstract: Latency in responding to queries directed to geographically distributed data can be reduced by allocating individual steps, of a multi-step compute operation requested by the query, among the geographically distributed computing devices so as to reduce the duration of shuffling of intermediate data among such devices, and, additionally, by pre-moving, prior to the receipt of the query, portions of the distributed data that are input to a first step of the multistep compute operation, to, again, reduce the duration of the exchange of intermediate data. The pre-moving of input data occurring, and the adaptive allocation of intermediate steps, are prioritized for high-value data sets. Additionally, a threshold increase in a quantity of data exchanged across network communications can be established to avoid incurring network communication usage without an attendant gain in latency reduction.

Abstract: The ensuring of predictable and quantifiable networking performance includes adaptively throttling the rate of VM-to-VM traffic flow. A receiving hypervisor can detect congestion and communicate messages for throttling traffic flow to reduce congestion at the receiving hypervisor.

Abstract: The techniques and/or systems described herein implement a fault handling service that is able to ensure that at least part of a network can avoid congestion (e.g., a link exceeding capacity) as long as a predetermined maximum number of faults is not exceeded. The fault handling service models different combinations of possible faults based on network topology and then computes an amount of traffic to be communicated via individual paths such that congestion is avoided as long as a number of actual faults that occur is less than or equal to the predetermined maximum number of faults.

Abstract: The ensuring of predictable and quantifiable networking performance. Embodiments of the invention combine a congestion free network core with a hypervisor based (i.e., edge-based) throttling design to help insure quantitative and invariable subscription bandwidth rates. A lightweight shim layer in a hypervisor can adaptively throttle the rate of VM-to-VM traffic flow. A receiving hypervisor can detect congestion and communicate back to sending hypervisors that rates are to be regulated. In response, sending hypervisors can reduce transmission rate to mitigate congestion at the receiving hypervisor. In some embodiments, the principles are extended to any message processors communicating over a congestion free network.

Abstract: The provisioning of a host computing system by a controller located over a wide area network. The host computing system has power-on code that automatically executes upon powering up, and causes the host to notify the controller of the host address. In a first level of bootstrapping, the controller instructs the host to download a maintenance operating system. The host responds by downloading and installing a maintenance operating system, enabling further bootstrapping. The persistent memory may further have security data, such as a public key, that allows the host computing system to securely identify the source of the download instructions (and subsequent instructions) as originating from the controller. A second level of bootstrapping may accomplish the configuring of the host with a hypervisor and a host agent. A third level of bootstrapping may accomplish the provisioning of virtual machines on the host.

Abstract: Traffic in a cloud is controlled by the nodes participating in the cloud. Tenants of the cloud each have a ratio. On any given node, a current transmission rate of the node is allocated among the tenants of the node, or more specifically, their execution units (e.g., virtual machines) on the node. Thus each tenant receives a predefined portion of the transmission capacity of the node. The transmission capacity can vary as conditions on the network change. For example, if congestion occurs, the transmission capacity may be decreased. Nonetheless, each tenant receives, according to its ratio, a same relative portion of the overall transmission capacity.

Abstract: The ensuring of predictable and quantifiable networking performance. Embodiments of the invention combine a congestion free network core with a hypervisor based (i.e., edge-based) throttling design to help insure quantitative and invariable subscription bandwidth rates. A lightweight shim layer in a hypervisor can adaptively throttle the rate of VM-to-VM traffic flow. A receiving hypervisor can detect congestion and communicate back to sending hypervisors that rates are to be regulated. In response, sending hypervisors can reduce transmission rate to mitigate congestion at the receiving hypervisor. In some embodiments, the principles are extended to any message processors communicating over a congestion free network.

Abstract: Examples are disclosed herein that relate to estimating and predicting vehicular fuel use. One example estimates fuel usage by a vehicle during a trip by obtaining sensor measurements from one or more sensors of a mobile computing device during the trip, determining a plurality of trip features from the sensor measurements, each trip feature representing an aspect of one or more of energy produced and energy consumed during the trip, obtaining vehicle-specific parameters of the vehicle, and determining an estimated fuel usage from the vehicle-specific parameters and the plurality of trip features for output by the computing device.

Abstract: The described implementations relate to distributed computing. One implementation provides a system that can include an outlier detection component that is configured to identify an outlier task from a plurality of tasks based on runtimes of the plurality of tasks. The system can also include a cause evaluation component that is configured to evaluate a cause of the outlier task. For example, the cause of the outlier task can be an amount of data processed by the outlier task, contention for resources used to execute the outlier task, or a communication link with congested bandwidth that is used by the outlier task to input or output data. The system can also include one or more processing devices configured to execute one or more of the components.