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, systems, and articles of manufacture for automated scheduling management of MapReduce flow-graph applications. A method includes determining a job schedule of MapReduce jobs within each of multiple MapReduce flows in a cluster environment, wherein said job schedule does not violate a precedence relationship within the corresponding MapReduce flow and reduces makespan of the corresponding MapReduce flow, determining a flow schedule for the multiple MapReduce flows based on consideration of a given metric, wherein said flow schedule comprises a number of slots allotted to each of the multiple MapReduce flows, and wherein said number of slots is less than or equal to a number of the one or more MapReduce jobs within each corresponding MapReduce flow, and transforming each job schedule into the flow schedule to allocate resources for the multiple MapReduce flows in the cluster environment without violating a precedence constraint of the multiple MapReduce flows.

Abstract: A method for allocating parallel, independent, data tasks includes receiving data tasks, each of the data tasks having a penalty function, determining a generic ordering of the data tasks according to the penalty functions, wherein the generic ordering includes solving an aggregate objective function of the penalty functions, the method further including determining a schedule of the data tasks given the generic ordering, which packs the data tasks to be performed.

Abstract: A method for allocating parallel, independent, data tasks includes receiving data tasks, each of the data tasks having a penalty function, determining a generic ordering of the data tasks according to the penalty functions, wherein the generic ordering includes solving an aggregate objective function of the penalty functions, the method further including determining a schedule of the data tasks given the generic ordering, which packs the data tasks to be performed.

Abstract: A method, apparatus, and computer program product for scheduling stream-based applications in a distributed computer system with configurable networks are provided. The method includes choosing, at a highest temporal level, jobs that will run, an optimal template alternative for the jobs that will run, network topology, and candidate processing nodes for processing elements of the optimal template alternative for each running job to maximize importance of work performed by the system. The method further includes making, at a medium temporal level, fractional allocations and re-allocations of the candidate processing elements to the processing nodes in the system to react to changing importance of the work. The method also includes revising, at a lowest temporal level, the fractional allocations and re-allocations on a continual basis to react to burstiness of the work, and to differences between projected and real progress of the work.

Abstract: A method for allocating parallel, independent, data tasks includes receiving data tasks, each of the data tasks having a penalty function, determining a generic ordering of the data tasks according to the penalty functions, wherein the generic ordering includes solving an aggregate objective function of the penalty functions, the method further including determining a schedule of the data tasks given the generic ordering, which packs the data tasks to be performed.

Abstract: Techniques for scheduling one or more MapReduce jobs in a presence of one or more priority classes are provided. The techniques include obtaining a preferred ordering for one or more MapReduce jobs, wherein the preferred ordering comprises one or more priority classes, prioritizing the one or more priority classes subject to one or more dynamic minimum slot guarantees for each priority class, and iteratively employing a MapReduce scheduler, once per priority class, in priority class order, to optimize performance of the one or more MapReduce jobs.

Abstract: Techniques for scheduling multiple flows in a multi-platform cluster environment are provided. The techniques include partitioning a cluster into one or more platform containers associated with one or more platforms in the cluster, scheduling one or more flows in each of the one or more platform containers, wherein the one or more flows are created as one or more flow containers, scheduling one or more individual jobs into the one or more flow containers to create a moldable schedule of one or more jobs, flows and platforms, and automatically converting the moldable schedule into a malleable schedule.

Abstract: Techniques for scheduling multiple flows in a multi-platform cluster environment are provided. The techniques include partitioning a cluster into one or more platform containers associated with one or more platforms in the cluster, scheduling one or more flows in each of the one or more platform containers, wherein the one or more flows are created as one or more flow containers, scheduling one or more individual jobs into the one or more flow containers to create a moldable schedule of one or more jobs, flows and platforms, and automatically converting the moldable schedule into a malleable schedule.

Abstract: Techniques for partitioning an operator flow graph are provided. The techniques include receiving source code for a stream processing application, wherein the source code comprises an operator flow graph, wherein the operator flow graph comprises a plurality of operators, receiving profiling data associated with the plurality of operators and one or more processing requirements of the operators, defining a candidate partition as a coalescing of one or more of the operators into one or more sets of processing elements (PEs), using the profiling data to create one or more candidate partitions of the processing elements, using the one or more candidate partitions to choose a desired partitioning of the operator flow graph, and compiling the source code into an executable code based on the desired partitioning.

Abstract: A method for scheduling a data processing job includes receiving the data processing job formed of a plurality of computing units, combining the plurality of computing units into a plurality of sets of tasks, each set including tasks of about equal estimated size, and different sets having different sized tasks, and assigning the tasks to a plurality of processors using a dynamic longest processing time (DLPT) scheme.

Abstract: Techniques for partitioning an operator flow graph are provided. The techniques include receiving source code for a stream processing application, wherein the source code comprises an operator flow graph, wherein the operator flow graph comprises a plurality of operators, receiving profiling data associated with the plurality of operators and one or more processing requirements of the operators, defining a candidate partition as a coalescing of one or more of the operators into one or more sets of processing elements (PEs), using the profiling data to create one or more candidate partitions of the processing elements, using the one or more candidate partitions to choose a desired partitioning of the operator flow graph, and compiling the source code into an executable code based on the desired partitioning.

Abstract: Techniques for partitioning an operator flow graph are provided. The techniques include receiving source code for a stream processing application, wherein the source code comprises an operator flow graph, wherein the operator flow graph comprises a plurality of operators, receiving profiling data associated with the plurality of operators and one or more processing requirements of the operators, defining a candidate partition as a coalescing of one or more of the operators into one or more sets of processing elements (PEs), using the profiling data to create one or more candidate partitions of the processing elements, using the one or more candidate partitions to choose a desired partitioning of the operator flow graph, and compiling the source code into an executable code based on the desired partitioning.

Abstract: Techniques for scheduling a plurality of jobs sharing input are provided. The techniques include partitioning one or more input datasets into multiple subcomponents, analyzing a plurality of jobs to determine which of the plurality of jobs require scanning of one or more common subcomponents of the one or more input datasets, and scheduling a plurality of jobs that require scanning of one or more common subcomponents of the one or more input datasets, facilitating a single scanning of the one or more common subcomponents to be used as input by each of the plurality of jobs.

Abstract: Fault tolerance is provided in a distributed system. The complexity of replicas and rollback requests are avoided; instead, a local failure in a component of a distributed system is tolerated. The local failure is tolerated by storing state related to a requested operation on the component, persisting that stored state in a data store, such as a relational database, asynchronously processing the operation request, and if a failure occurs, restarting the component using the stored state from the data store.

Abstract: Techniques for scheduling multiple flows in a multi-platform cluster environment are provided. The techniques include partitioning a cluster into one or more platform containers associated with one or more platforms in the cluster, scheduling one or more flows in each of the one or more platform containers, wherein the one or more flows are created as one or more flow containers, scheduling one or more individual jobs into the one or more flow containers to create a moldable schedule of one or more jobs, flows and platforms, and automatically converting the moldable schedule into a malleable schedule.

Abstract: Techniques for scheduling one or more MapReduce jobs in a presence of one or more priority classes are provided. The techniques include obtaining a preferred ordering for one or more MapReduce jobs, wherein the preferred ordering comprises one or more priority classes, prioritizing the one or more priority classes subject to one or more dynamic minimum slot guarantees for each priority class, and iteratively employing a MapReduce scheduler, once per priority class, in priority class order, to optimize performance of the one or more MapReduce jobs.