Abstract: Systems and methods for predicting query execution time for concurrent and dynamic database workloads include decomposing each query into a sequence of query pipelines based on the query plan from a query optimizer, and predicting an execution time of each pipeline with a progress predictor for a progress chart of query pipelines.

Abstract: A system to optimize layout of database objects in a relational database management system stored on a plurality of storage classes each characterized by a price and a storage capacity includes a time-based query optimizer and a layout recommender coupled to the time-based query optimizer to estimate a total cost of operation (TCO) for a query workload on each data layout. The layout recommender includes an auxiliary object selection comprising database objects that include auxiliary objects that are optional to place with auxiliary object candidates being given from an auxiliary object recommender component.

Abstract: Systems and methods are disclosed for managing a multi-store execution environment by applying opportunistic materialized views to improve workload performance and executing a plan on multiple database engines to increase query processing speed by leveraging unique capabilities of each engine by enabling stages of a query to execute on multiple engines, and by moving materialized views across engines.

Abstract: An efficient cost-based scheduling method called incremental cost-based scheduling, iCBS, maps each job, based on its arrival time and SLA function, to a fixed point in the dual space of linear functions. Due to this mapping, in the dual space, the job will not change their locations over time. Instead, at the time of selecting the next job with the highest priority to execute, a line with appropriate angle in the query space is used to locate the current job with the highest CBS score in logarithmic time. Because only those points that are located on the convex hull in the dual space can be chosen, a dynamic convex hull maintaining method incrementally maintains the job with the highest CBS score over time.

Abstract: Systems and methods are disclosed for placing tenants in a cloud based database server, by estimating with a processor a cost of placing a set of tenants on a server using a simulator; estimating a relative importance of different tenant's queries; and selecting an optimal server for each tenant based with a cost-based schedular to maximize profit using the simulator.

Abstract: A swap-based multitenant database load balancing (SWAT) invention finds an optimal swap set that eliminates hotspots, and finds a sequencing and parallelization of swap executions for the multitenant database.

Abstract: A system and method for data sharing are provided. Context parameters associated with a user are gathered from a plurality of applications. A mobile context of the user is determined based on the gathered context parameters. Content is presented to the user in accordance with the mobile context of the user.

Abstract: Systems and methods for prioritizing queries in a cloud based database by determining a priority score in real time under an imprecise query execution time; and incrementally maintaining query priority scores.

Abstract: A method for automatic database design for scalability by receiving a database schema and database workload; applying transaction chopping to split a large transaction into smaller transactions; select one or more transactions using dynamic programming based on transaction weights; deriving a database design that covers the selected transactions; and generating a transaction class design that is scalable.

Abstract: A method is disclosed to perform performance prediction for cloud-based databases by building on a computer a cloud database performance model using one or more training workloads; and using the learned model on the computer to predict database performance in the cloud for a new workload.

Abstract: A method to facilitate data sharing for cloud applications includes determining one or more cost levers for a cloud service provider to share data among applications; determining a costing function that considers a resource cost of creating and maintaining the sharing, potential penalties to be paid if a service level agreement (SLA) is breached by the cloud service provider, and overprovisioning of services from the provider; and interactively answering what-if questions on pricing of services to allow a consumer to explore the cost of data sharing from the provider.

Abstract: A platform as a service (PaaS) is provided as part of a data sharing scheme. The PaaS is configured to setup a materialized shared space in a cloud. The materialized shared space includes a set of resources invested by the PaaS to permit data sharing between a tenant application and at least one consumer application. A sharing middleware configured to provide the at least one consumer application with access to the data in the materialized shared space.

Abstract: A system for evolutionary analytics supports three dimensions (analytical workflows, the users, and the data) by rewriting workflows to be more efficient by using answers materialized as part of previous workflow execution runs in the system.

Abstract: A method for database consolidation includes generating a model for expected penalty estimation; determining a tenant's value as a function of query arrival rate and SLA penalty; placing a tenant to minimize a total expected cost in the order of the tenant value; and progressively using additional servers to prevent any server from being saturated to guarantee a tenant placement that costs no more than four times the cost of any other placement

Abstract: A system and method are provided for data sharing. A sharing negotiation tool permits at least one consumer application to negotiate a service level agreement on a dataset which is being shared by a tenant application on a cloud. A capacity planner is configured to determine an appropriate allocation of cloud resources to ensure that the service level agreement of the at least one consumer application is satisfied.

Abstract: A system and method are provided for data sharing. A uniform communication framework is provided as part of a sharing service on the cloud platform to facilitate data sharing among a plurality of applications. The uniform communication framework includes an application programming interface which provides a communication gateway to permit a first application to access data of a second application stored in the data store.

Abstract: A method for balancing database workloads among a plurality of database servers includes when a new server is available, selecting a set of master and slave databases to be migrated to the new server; and migrating the selected databases to result in a balanced new workload among all servers while minimizing migration cost; and during operation, if workload unbalance is detected in real time from a workload change in a certain database, iteratively selecting one database at a time to migrate to a different server to balance the workload.

Abstract: A computer implemented method of workload scheduling under two different types of Service Level Agreements (SLAs), namely soft and hard SLA which employ both a deadline- and cost-aware scheduler called iCBS-DH. The scheduler is evaluated against deadline and cost performance of various scheduling policies under a large range of SLA cost function and deadline types.

Abstract: Systems and methods are disclosed to consolidate workload on cloud-based computers by co-locating one or more high-penalty tenants on one or more cloud-based servers; reducing service level agreement (SLA) violations by over-provisioning the cloud-based server; and maximizing an SLA profit.

Abstract: Systems and methods are disclosed for scheduling one or more queries for execution with one or more cloud computers by applying a service level agreement (SLA) analysis using an SLA-tree data structure to each query in a query queue; using the SLA-tree data structure to determine an overall profit in the query queue; and generating a profit-oriented schedule to select the next query for execution by the cloud computers.