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 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, an apparatus and an article of manufacture for processing a random-walk based vertex-proximity query on a graph. The method includes computing at least one vertex cluster and corresponding meta-information from a graph, dynamically updating the clustering and corresponding meta-information upon modification of the graph, and identifying a vertex cluster relevant to at least one query vertex and aggregating corresponding meta-information of the cluster to process the query.

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 system for clustering vertices in a streaming graph includes a structural sampler configured to receive a stream of edges. The structural sampler includes a reservoir manager configured to receive the stream of edges and create a structural reservoir and a support reservoir and a graph manager configured to receive the structural reservoir from the reservoir manager and to create a sampled graph from the structural reservoir, wherein the sampled graph includes one or more clusters that each include one or more connected vertices.

Abstract: Embodiments of the invention include methods for identifying one or more clusters in a streaming graph, the method includes receiving a stream of edges and sampling the stream of edges to create a structural reservoir and support reservoir. The method also includes creating a sampled graph from the structural reservoir and identifying the one or more clusters in the sampled graph by grouping one or more connected vertices in the sampled graph.

Abstract: A system for clustering vertices in a streaming graph includes a structural sampler configured to receive a stream of edges. The structural sampler includes a reservoir manager configured to receive the stream of edges and create a structural reservoir and a support reservoir and a graph manager configured to receive the structural reservoir from the reservoir manager and to create a sampled graph from the structural reservoir, wherein the sampled graph includes one or more clusters that each include one or more connected vertices.

Abstract: Embodiments of the invention include methods for identifying one or more clusters in a streaming graph, the method includes receiving a stream of edges and sampling the stream of edges to create a structural reservoir and support reservoir. The method also includes creating a sampled graph from the structural reservoir and identifying the one or more clusters in the sampled graph by grouping one or more connected vertices in the sampled graph.

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: A method, an apparatus and an article of manufacture for processing a random-walk based vertex-proximity query on a graph. The method includes computing at least one vertex cluster and corresponding meta-information from a graph, dynamically updating the clustering and corresponding meta-information upon modification of the graph, and identifying a vertex cluster relevant to at least one query vertex and aggregating corresponding meta-information of the cluster to process the query.

Abstract: Distributed data processing of problems representing resource assignment tasks. The problems are modeled as programs, and the programs are partitioned into sub-instances. Those sub-instances are executed in a distributed computing environment. The partitioning reduces communication costs between sub-instances and convergence time for the optimization program.

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.