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7th Biennial Ptolemy Miniconference Berkeley, CA February 13, 2007 Scheduling Data-Intensive Workflows Tim H. Wong, Daniel Zinn, Bertram Ludäscher (UC Davis)
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2Ptolemy Miniconference 2007Daniel Zinn Outline Problem motivation Assumptions Cost model Problem formalization Different “simplifications” and their complexity Prototypical Java implementation for Kepler Summary
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3Ptolemy Miniconference 2007Daniel Zinn Motivation: Distributed Execution of Scientific Workflows
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4Ptolemy Miniconference 2007Daniel Zinn Motivation: Distributed Execution of Scientific Workflows Process a set of data on a set of machines GOAL: Minimize WF-Execution time! Allocation Problem: Which actors are computed on which hosts?
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5Ptolemy Miniconference 2007Daniel Zinn Assumptions Arbitrary data size Arbitrary machine speed Arbitrary bandwidth Arbitrary number of inputs Scientific workflow is a DAG (!) GRID COMPUTING
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6Ptolemy Miniconference 2007Daniel Zinn Cost Model Communication Time: T C Function Execution Time: T E Total Time: T T = T C + T E Shipping and Handling Problem: Schedule all tasks such that the total time is minimal
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7Ptolemy Miniconference 2007Daniel Zinn Problem Variants and Complexities Task Handling Problem (THP) Data Shipping Problem (DSP) Reduction from Task Scheduling Problem [ERLA94] Reduction from Multiprocessor Scheduling Problem [KA99] Reduction from 1-Multiterminal Cut Shipping and Handling Problem (SHP) Communication Cost: Non-uniform Function Execution Cost: Non-uniform Complexity: NP-complete Communication Cost: Zero Function Execution Cost: Non-uniform Complexity: NP-complete Communication Cost: Non-uniform Function Execution Cost: Zero Complexity: NP-complete
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8Ptolemy Miniconference 2007Daniel Zinn easy-DSP: Uniform Transfer Rate, Uniform Data Size Given: Directed Acyclic Graph, Set of Colors Some vertices are already colored Edge Weight = 1, if two adjacent vertices are of different colors Edge Weight = 0, otherwise TASK: Color the rest of the vertices such that total weight is minimal! Cost Model: Minimize Total Shipped Volume! 4
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9Ptolemy Miniconference 2007Daniel Zinn 1 - Multi-Terminal CUT Given: Undirected Graph: G = (V,E) Set of Terminals: S V Edge Weights: 1 TASK: Find a multi-way cut of G with a minimum number of edges NP-Complete for more than 3 Terminals! Minimize #edges between different terminals! 4
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10Ptolemy Miniconference 2007Daniel Zinn Reduction: 1-MTC <= DSP 4 4 ? DSP 1-MTC “Order graph Color terminals”
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11Ptolemy Miniconference 2007Daniel Zinn Reduction: 1-MTC <= DSP 4 4 1 1 1 1 1 1 1 1 1 ?! DSP 1-MTC
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12Ptolemy Miniconference 2007Daniel Zinn Reduction: 1-MTC <= DSP 4 4 1 1 1 1 1 1 1 1 1 ! DSP 1-MTC
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13Ptolemy Miniconference 2007Daniel Zinn NP-Hard,... But: Need to solve Greedy Algorithm Dynamic Programing Algorithm Investigate Approximation Algorithms for MTC/related !
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14Ptolemy Miniconference 2007Daniel Zinn Prototypical Implementation... abstract only some nodes assigned concrete all nodes assigned scheduling
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15Ptolemy Miniconference 2007Daniel Zinn Prototypical Implementation... in Kepler! Abstract Workflow... SCHEDULING
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16Ptolemy Miniconference 2007Daniel Zinn Prototypical Implementation... in Kepler! Concrete Workflow...
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17Ptolemy Miniconference 2007Daniel Zinn Future Work Use Heuristics about looping to guess multiplicities (then not ACYCLIC any more!) Investigate approximation algorithms with error guarantees for 1-MTC => try to apply for DSP ALSO: Relevant for COMAD Workflows: can be “compiled” into a low-level conventional WF
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18Ptolemy Miniconference 2007Daniel Zinn Summary Bad news Scheduling is hard DSP is hard (for BEST plans) Good news Finding a quite good plan is easy Greedy/Dynamic Algorithms Open Problems Approximation Quality of “simple algorithms”? When do they perform badly? Does this occur often in real-life workflows?
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19Ptolemy Miniconference 2007Daniel Zinn References
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20Ptolemy Miniconference 2007Daniel Zinn Thank You. Questions?
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