Abstract: Devices and techniques are generally described for per-query prediction of shard relevance for search. In some examples, a search system may receive a first search query. A first score may be determined for a first database partition, the first score indicating a relevancy of the first search query to first data stored by the first database partition. Similarly, a second score may be determined for a second database partition, the second score indicating a relevancy of the first search query to second data stored by the second database partition. A determination may be made that the first search query is related to the first data stored by the first database partition. A determination may be made, based at least in part on the second score, that the first search query is unrelated to the second data stored by the second database partition.

Abstract: Systems and methods are disclosed for operating a software-defined network (SDN) by slicing the SDN into differentiated queues according to different priorities to prioritizes the queries based on the user's request; reserving necessary bandwidth for specific queries to ensure specific performance levels based on the user's request; providing information to a query plan executor; and managing performance of analytical queries in distributed relational databases.

Abstract: Systems and methods are disclosed for selecting a query plan in a database by monitoring network state information and flow information; and selecting an adaptive plan for execution with a query manager that receives the network state information and flow information, including: receiving a query, parsing the query, generating and optimizing a global query plan; dividing the global query plan into local plans; sending the local plans to corresponding data store sites for execution with separate threads; and orchestrating data flows among the data store sites and forwarding a final result to a user.

Abstract: An admission control system for a cloud database includes a machine learning prediction module to estimate a predicted probability for a newly arrived query with a deadline, if admitted into the cloud database, to finish its execution before said deadline, wherein the prediction considers query characteristics and current system conditions. The system also includes a decision module applying the predicted probability to admit a query into the cloud database with a target of profit maximization with an expected profit determined using one or more service level agreements (SLAs).

Abstract: Systems and methods are disclosed to manage resources in a cloud-based computing system by generating a model of a relationship between cloud database resources and an expected profit based on cloud-server system parameters and service level agreements (SLAs) that indicates profits for different system performances, wherein the model comprises a two level optimization/control problem, wherein model receives system metrics, number of replicas, and arrival rate as the multiple input; and dynamically adjusting resource allocation among different customers based on current customer workload and the expected profit to maximize the expected profit for a cloud computing service provider.

Abstract: An admission control system for a cloud database includes a machine learning prediction module to estimate a predicted probability for a newly arrived query with a deadline, if admitted into the cloud database, to finish its execution before said deadline, wherein the prediction considers query characteristics and current system conditions. The system also includes a decision module applying the predicted probability to admit a query into the cloud database with a target of profit maximization with an expected profit determined using one or more service level agreements (SLAs).

Abstract: Systems and methods are disclosed to manage resources in a cloud-based computing system by generating a model of a relationship between cloud database resources and an expected profit based on cloud-server system parameters and service level agreements (SLAs) that indicates profits for different system performances, wherein the model comprises a two level optimization/control problem, wherein model receives system metrics, number of replicas, and arrival rate as the multiple input; and dynamically adjusting resource allocation among different customers based on current customer workload and the expected profit to maximize the expected profit for a cloud computing service provider.