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 schedule jobs in a cloud computing infrastructure by receiving in a first queue jobs with deadlines or constraints specified in a hard service level agreement (SLA); receiving in a second queue jobs with a penalty cost metric specified in a soft SLA; and minimizing both constraint violation count and total penalty cost in the cloud computing infrastructure by identifying jobs with deadlines in the first queue and delaying jobs in the first queue within a predetermined slack range in favor of jobs in the second queue to improve the penalty cost metric.

Abstract: Systems and method are disclosed for query optimization in a scale-out system with a single query processing machine and a distributed storage engine to store data by receiving a query rewritten for an internal schema; optimizing a query execution plan for the query; and executing the plan and returning result to an application.

Abstract: A system includes one or more application nodes sending SQL queries to one or more query execution nodes. Each query execution node translates the SQL queries into key-based requests to the storage engine, which comprises one or more storage nodes. The system provides an abstraction layer with an external schema and an internal schema, maps the external schema into the internal schema; and generates one or more microshards for the storage node.

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.