Introduction | Model | Solution | Evaluation Scheduling with Task Duplication for Computation Offloading Arani Bhattacharya (arbhattachar@cs.stonybrook.edu) Ansuman Banerjee Pradipta De IEEE CCNC 2017 Introduction | Model | Solution | Evaluation

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

How Offloading Works Network IEEE CCNC 2017 Introduction | Model | Solution | Evaluation Introduction | Model | Solution | Evaluation

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

Our Contribution Fast Algorithm Optimal Schedule Evaluated with synthetic and real workload IEEE CCNC 2017 Introduction | Model | Solution | Evaluation Introduction | Model | Solution | Evaluation

Formal Model Network: Fixed latency Architecture: One Mobile and one server Multiple cores IEEE CCNC 2017 Introduction | Model | Solution | Evaluation Introduction | Model | Solution | Evaluation

Formal Model DAG Only local execution allowed Local or cloud execution allowed IEEE CCNC 2017 Introduction | Model | Solution | Evaluation Introduction | Model | Solution | Evaluation

Minimize application finish time OBJECTIVE Minimize application finish time IEEE CCNC 2017 Introduction | Model | Solution | Evaluation Introduction | Model | Solution | Evaluation

Conventional Approach Uses graph partitioning High time complexity IEEE CCNC 2017 Introduction | Model | Solution | Evaluation Introduction | Model | Solution | Evaluation

Our Approach Duplication Dynamic Programming algorithm possible IEEE CCNC 2017 Introduction | Model | Solution | Evaluation Introduction | Model | Solution | Evaluation

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

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

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

Makespan IEEE CCNC 2017 Introduction | Model | Solution | Evaluation

Scheduling Time IEEE CCNC 2017 Introduction | Model | Solution | Evaluation Introduction | Model | Solution | Evaluation

Evaluation Simulation-based Trace-based SPECjvm08 programs Obtained by aspect-oriented programming IEEE CCNC 2017 Introduction | Model | Solution | Evaluation Introduction | Model | Solution | Evaluation

Makespan IEEE CCNC 2017 Introduction | Model | Solution | Evaluation

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