Abstract: Methods, systems, and computer program products for cross-platform scheduling with fairness and platform-specific optimization are provided herein. A method includes determining dimensions of a set of containers in which multiple tasks associated with a request are to be executed; assigning each of the containers to a processing node on one of multiple platforms based on the dimensions of the given container, and to a platform owner selected from the multiple platforms based on a comparison of resource requirements of each of the multiple platforms and the dimensions of the given container; and generating container assignments across the set of containers by incorporating the assigned node of each container in the set of containers, the assigned platform owner of each container in the set of containers, one or more scheduling requirements of each of the platforms, one or more utilization objectives, and enforcing a sharing guarantee of each of the platforms.

Abstract: Methods, systems, and computer program products for cross-platform scheduling with fairness and platform-specific optimization are provided herein. A method includes determining dimensions of a set of containers in which multiple tasks associated with a request are to be executed; assigning each of the containers to a processing node on one of multiple platforms based on the dimensions of the given container, and to a platform owner selected from the multiple platforms based on a comparison of resource requirements of each of the multiple platforms and the dimensions of the given container; and generating container assignments across the set of containers by incorporating the assigned node of each container in the set of containers, the assigned platform owner of each container in the set of containers, one or more scheduling requirements of each of the platforms, one or more utilization objectives, and enforcing a sharing guarantee of each of the platforms.

Abstract: Methods, systems, and computer program products for cross-platform scheduling with fairness and platform-specific optimization are provided herein. A method includes determining dimensions of a set of containers in which multiple tasks associated with a request are to be executed; assigning each of the containers to a processing node on one of multiple platforms based on the dimensions of the given container, and to a platform owner selected from the multiple platforms based on a comparison of resource requirements of each of the multiple platforms and the dimensions of the given container; and generating container assignments across the set of containers by incorporating the assigned node of each container in the set of containers, the assigned platform owner of each container in the set of containers, one or more scheduling requirements of each of the platforms, one or more utilization objectives, and enforcing a sharing guarantee of each of the platforms.

Abstract: Methods, systems, and computer program products for cross-platform scheduling with fairness and platform-specific optimization are provided herein. A method includes determining dimensions of a set of containers in which multiple tasks associated with a request are to be executed; assigning each of the containers to a processing node on one of multiple platforms based on the dimensions of the given container, and to a platform owner selected from the multiple platforms based on a comparison of resource requirements of each of the multiple platforms and the dimensions of the given container; and generating container assignments across the set of containers by incorporating the assigned node of each container in the set of containers, the assigned platform owner of each container in the set of containers, one or more scheduling requirements of each of the platforms, one or more utilization objectives, and enforcing a sharing guarantee of each of the platforms.

Abstract: Techniques for determining feasibility of a set of one or more operator partitioning constraints are provided. The techniques include receiving one or more sets of operator partitioning constraints, wherein each set of one or more constraints define one or more desired conditions for grouping together of operators into partitions and placing partitions on hosts, wherein each operator is embodied as software that performs a particular function, processing each set of one or more operator partitioning constraints to determine feasibility of each set of one or more operator partitioning constraints, creating and outputting one or more candidate partitions and one or more host placements for each set of feasible partitioning constraints, and creating and outputting a certificate of infeasibility for each set of infeasible partitioning constraints, wherein the certificate of infeasibility outlines one or more reasons for infeasibility.

Abstract: A storage system includes a plurality of data vats, and a processor including an optimizing unit that optimizes a value of data stored in the storage system. The optimizing unit optimizes the value by computing and implementing an optimal decision for allocating new data to a first data vat of the plurality of data vats, moving existing data from at least a second data vat of the plurality of data vats to the first data vat, and deleting existing data from the first data vat, based on an amount of data in each of the plurality of data vats.

Abstract: An apparatus and method for making fractional assignments of processing elements to processing nodes for stream-based applications in a distributed computer system includes determining an amount of processing power to give to each processing element. Based on a list of acceptable processing nodes, a determination of fractions of which processing nodes will work on each processing element is made. To update allocations of the amount of processing power and the fractions, the process is repeated.

Abstract: A system and method for choosing non-continual jobs to run in a stream-based distributed computer system includes determining a total amount of resources to be consumed by non-continual jobs. A priority threshold is determined above which jobs will be accepted, below which jobs will be rejected. Overall penalties are minimized relative to the priority threshold based on estimated completion times of the jobs. System constraints are applied to ensure that jobs meet set criteria such that a plurality of non-continual jobs are scheduled which consider the system constraints and minimize overall penalties using available resources.

Abstract: A method of choosing jobs to run in a stream based distributed computer system includes determining jobs to be run in a distributed stream-oriented system by deciding a priority threshold above which jobs will be accepted, below which jobs will be rejected. Overall importance is maximized relative to the priority threshold based on importance values assigned to all jobs. System constraints are applied to ensure jobs meet set criteria.

Abstract: A storage system includes a plurality of data vats, and a processor including an optimizing unit that optimizes a value of data stored in the storage system. The optimizing unit optimizes the value by computing and implementing an optimal decision for allocating new data to a first data vat of the plurality of data vats, moving existing data from at least a second data vat of the plurality of data vats to the first data vat, and deleting existing data from the first data vat, based on an amount of data in each of the plurality of data vats.

Abstract: The present invention relates to the problem of scheduling work for employees and/or other resources in a help desk or similar environment. The employees have different levels of training and availabilities. The jobs, which occur as a result of dynamically occurring events, consist of multiple tasks ordered by chain precedence. Each job and/or task carries with it a penalty which is a step function of the time taken to complete it, the deadlines and penalties having been negotiated as part of one or more service level agreement contracts. The goal is to minimize the total amount of penalties paid. The invention consists of a pair of heuristic schemes for this difficult scheduling problem, one greedy and one randomized. The greedy scheme is used to provide a quick initial solution, while the greedy and randomized schemes are combined in order to think more deeply about particular problem instances.

Abstract: A method (and system) of storing data in a value-based storage system, includes optimizing a value of data stored in the value-based storage system.

Abstract: A method of choosing jobs to run in a stream based distributed computer system includes determining jobs to be run in a distributed stream-oriented system by deciding a priority threshold above which jobs will be accepted, below which jobs will be rejected. Overall importance is maximized relative to the priority threshold based on importance values assigned to all jobs. System constraints are applied to ensure jobs meet set criteria.

Abstract: A method of choosing jobs to run in a stream based distributed computer system includes determining jobs to be run in a distributed stream-oriented system by deciding a priority threshold above which jobs will be accepted, below which jobs will be rejected. Overall importance is maximized relative to the priority threshold based on importance values assigned to all jobs. System constraints are applied to ensure jobs meet set criteria.

Abstract: A system and method for optimizing a storage system to support short data object lifetimes and highly utilized storage space are provided. With the system and method, data objects are clustered based on when they are anticipated to be deleted. When an application stores data, the application provides an indicator of the expected lifetime of the data, which may be a retention value, a relative priority of the data object, or the like. Data objects having similar expected lifetimes are clustered together in common data structures so that clusters of objects may be deleted efficiently in a single operation. Expected lifetimes may be changed by applications automatically. The system automatically determines how to handle these changes in expected lifetime using one or more of copying the data object, reclassifying the container in which the data object is held, and ignoring the change in expected lifetime for a time to investigate further changes in expected lifetime of other data objects.

Abstract: An apparatus and method for making fractional assignments of processing elements to processing nodes for stream-based applications in a distributed computer system includes determining an amount of processing power to give to each processing element. Based on a list of acceptable processing nodes, a determination of fractions of which processing nodes will work on each processing element is made. To update allocations of the amount of processing power and the fractions, the process is repeated.

Abstract: An apparatus and method for scheduling stream-based applications in a distributed computer system includes a scheduler configured to schedule work using three temporal levels. Each temporal level includes a method. A macro method is configured to schedule jobs that will run, in a highest temporal level, in accordance with a plurality of operation constraints to optimize importance of work. A micro method is configured to fractionally allocate, at a medium temporal level, processing elements to processing nodes in the system to react to changing importance of the work. A nano method is configured to revise, at a lowest temporal level, fractional allocations on a continual basis.

Abstract: A method of choosing jobs to run in a stream based distributed computer system includes determining jobs to be run in a distributed stream-oriented system by deciding a priority threshold above which jobs will be accepted, below which jobs will be rejected. Overall importance is maximized relative to the priority threshold based on importance values assigned to all jobs. System constraints are applied to ensure jobs meet set criteria.

Abstract: A system and method for choosing non-continual jobs to run in a stream-based distributed computer system includes determining a total amount of resources to be consumed by non-continual jobs. A priority threshold is determined above which jobs will be accepted, below which jobs will be rejected. Overall penalties are minimized relative to the priority threshold based on estimated completion times of the jobs. System constraints are applied to ensure that jobs meet set criteria such that a plurality of non-continual jobs are scheduled which consider the system constraints and minimize overall penalties using available resources.

Abstract: The present invention relates to the problem of scheduling work for employees and/or other resources in a help desk or similar environment. The employees have different levels of training and availabilities. The jobs, which occur as a result of dynamically occurring events, consist of multiple tasks ordered by chain precedence. Each job and/or task carries with it a penalty which is a step function of the time taken to complete it, the deadlines and penalties having been negotiated as part of one or more service level agreement contracts. The goal is to minimize the total amount of penalties paid. The invention consists of a pair of heuristic schemes for this difficult scheduling problem, one greedy and one randomized. The greedy scheme is used to provide a quick initial solution, while the greedy and randomized schemes are combined in order to think more deeply about particular problem instances.