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Introduction | Model | Solution | Evaluation
Scheduling with Task Duplication for Computation Offloading Arani Bhattacharya Ansuman Banerjee Pradipta De IEEE CCNC 2017 Introduction | Model | Solution | Evaluation
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Computation Offloading
Multiple Applications Slower Processor More complex applications in less time IEEE CCNC 2017 Introduction | Model | Solution | Evaluation Introduction | Model | Solution | Evaluation Introduction | Model | Solution | Evaluation
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How Offloading Works Network IEEE CCNC 2017
Introduction | Model | Solution | Evaluation Introduction | Model | Solution | Evaluation
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What is the Problem? Scheduler decides how to partition
Partitioning graph is NP-Hard Applications take time to partition How to split applications in less time? IEEE CCNC 2017 Introduction | Model | Solution | Evaluation Introduction | Model | Solution | Evaluation
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Our Contribution Fast Algorithm Optimal Schedule
Evaluated with synthetic and real workload IEEE CCNC 2017 Introduction | Model | Solution | Evaluation Introduction | Model | Solution | Evaluation
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Formal Model Network: Fixed latency Architecture:
One Mobile and one server Multiple cores IEEE CCNC 2017 Introduction | Model | Solution | Evaluation Introduction | Model | Solution | Evaluation
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Formal Model DAG Only local execution allowed
Local or cloud execution allowed IEEE CCNC 2017 Introduction | Model | Solution | Evaluation Introduction | Model | Solution | Evaluation
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Minimize application finish time
OBJECTIVE Minimize application finish time IEEE CCNC 2017 Introduction | Model | Solution | Evaluation Introduction | Model | Solution | Evaluation
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Conventional Approach
Uses graph partitioning High time complexity IEEE CCNC 2017 Introduction | Model | Solution | Evaluation Introduction | Model | Solution | Evaluation
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Our Approach Duplication Dynamic Programming algorithm possible
IEEE CCNC 2017 Introduction | Model | Solution | Evaluation Introduction | Model | Solution | Evaluation
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Example M C 40 X v3 M C 10 X M C 30 24 M C 70 54 M C 74 X v1 v5 v6 v2 M C 50 28 Mobile Cloud v1 10 v2 20 4 v3 v4 v5 v6 v4 Dependency Migration Time : 10 IEEE CCNC 2017 Introduction | Model | Solution | Evaluation Introduction | Model | Solution | Evaluation
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Evaluation Simulation-based Trace-based Random graphs
Experiments repeated 1000 times for high confidence interval Trace-based IEEE CCNC 2017 Introduction | Model | Solution | Evaluation Introduction | Model | Solution | Evaluation
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Evaluation Simulation-based Trace-based Random graphs
Experiments repeated 1000 times for high confidence interval Trace-based IEEE CCNC 2017 Introduction | Model | Solution | Evaluation Introduction | Model | Solution | Evaluation
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Makespan IEEE CCNC 2017 Introduction | Model | Solution | Evaluation
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Scheduling Time IEEE CCNC 2017
Introduction | Model | Solution | Evaluation Introduction | Model | Solution | Evaluation
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Evaluation Simulation-based Trace-based SPECjvm08 programs
Obtained by aspect-oriented programming IEEE CCNC 2017 Introduction | Model | Solution | Evaluation Introduction | Model | Solution | Evaluation
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Makespan IEEE CCNC 2017 Introduction | Model | Solution | Evaluation
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Introduction | Model | Solution | Evaluation
Conclusion Scheduling with task duplication in offloading Reduces both makespan and scheduling time Shown using theory and experiments IEEE CCNC 2017 Conclusion Introduction | Model | Solution | Evaluation
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