Rosaic: A Round-wise Fair Scheduling Approach for Mobile Clouds Based on Task Asymptotic Complexity
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This document proposes ROSAIC, a round-wise scheduling approach for mobile clouds based on task complexity. ROSAIC divides tasks into subtasks of equal complexity and schedules them in rounds across devices. It aims to address issues with traditional scheduling in mobile clouds like dynamic topology changes and unfair task distribution. The document outlines ROSAIC's architecture, describes its approach to estimating task complexity and runtime, and presents experimental results showing ROSAIC outperforms other scheduling strategies in reducing average completion time.
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Rosaic: A Round-wise Fair Scheduling Approach for Mobile Clouds Based on Task Asymptotic Complexity
- 1. ROSAC: A Round-wise Fair Scheduling Approach for Mobile Clouds Based on Task Asymptotic Complexity Shahid Al Noor, Ragib Hasan, University of Alabama at Birmingham Presenter: Mahmud Hossain, PhD Student, CIS, UAB. 5/31/2018
- 2. Problem Statement Traditional scheduling approaches cannot be applied as is to mobile cloud FIFO, Shortest Job First, Round Robin, Priority Queue Dynamic topological changes Devices vary in resource capabilities No efficient strategy for multiple tasks Unfair distribution of tasks among multiple crowdsourced devices 5/31/2018 2
- 3. Outline Introduction Motivation Architecture Complexity Analysis Experiment Conclusion 5/31/2018 3
- 4. Mobile Cloud An ad hoc cloud formed by crowdsourced device Offer storage, computation, or sensing as a service Resource intensive task offloading Can provide service without having continuous internet connection Delay tolerant network 5/31/2018 4
- 5. Generalize Architecture 5/31/2018 5 Cloud User Cloud Central System Mobile Node Cloudlet Task Result Subtask Subtask Subresult Subresult Result Task Result Cloud User Direct Connec on Indirect Connec on
- 6. Motivation A user does not receive monetary incentives during the time it is idle Querying a cloud provider repeatedly for new task is energy inefficient A small task might need to wait a more longer time to get the resources 5/31/2018 6
- 7. ROSAIC Overview Resource Scheduling based on task asymptotic complexity Divide each task into several subtasks Created several rounds taking a subtask from each tasks The complexity of the subtasks in each round is the same The tasks in the next round is sent only after the completion of the previous round 5/31/2018 7
- 8. ROSAIC Architecture 5/31/2018 8 Client MU Round-wise Subtask Generator Resource Analyzer Complexity Analyzer T1 Run me Analyzer Aggregator Client Client MU MU T2 Tn Mapper/ Reducer Client Client Client R1 R2 Rn
- 9. Runtime Estimation 5/31/2018 9 Verify Complexity Task Profile Profile Matches? Success? Complexity Specified? Determine Complexity Yes Yes Yes No No Task Es mate Task Run me Es mated Run me No
- 10. Complexity Estimation Pre-compute the complexity of all possible statements in a program Represent programs as graphs where node represents a statement Determine all the possible paths in the graph Match each node in a path with the corresponding pre-computed complexity to compute the path complexity 5/31/2018 10
- 11. Runtime Curve Generation Exact profile match Create a task profile for each task after running Match a task with its previously computed task Demo run Run a small portion of the task and compute the time Estimate runtime based on the sample run Similar profile match Find tasks that have similar complexity of current task Compare the code and estimate the time 5/31/2018 11
- 12. Simulation Setup Simulator: CloudSim Tasks: Word Count, Inverted Index, Sorting, sequence counter, and adjacency list Five devices, three Google nexus and tow Samsung S4 5/31/2018 12
- 13. Experimental Result 5/31/2018 13 Variation of average completion time with Node Size for Similar length Tasks 0 10000 20000 30000 40000 50000 60000 50 500 1000 Time(ms) No. of Nodes Random Strategy Shortest Task Similar Profile Demo Run Exact Profile Task Size=10
- 14. Experimental Result 5/31/2018 14 Variation of average completion time with Node Size for Similar length Tasks 0 50000 100000 150000 200000 250000 300000 50 500 1000 1500 2000 2500 3000 3500 Time(ms) No of Nodes Random Strategy Shortest Task Similar Profile Demo Run Exact Profile Task Size=50
- 15. Experimental Result (cont.) 5/31/2018 15 Variation of average completion time with Node Size for Variable length Tasks 0 100000 200000 300000 400000 500000 600000 700000 50 500 1000 1500 2000 2500 3000 Time(ms) No.ofNodes RandomStrategy ShortestTask SimilarProfile DemoRun ExactProfile Task Size=10
- 16. Experimental Result (cont.) 5/31/2018 16 Variation of average completion time with Node Size for Variable length Tasks 0 200000 400000 600000 800000 1000000 1200000 50 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 Time(ms) No.ofNodes RandomStrategy ShortestTask SimilarProfile DemoRun ExactProfile Task Size=50
- 17. Conclusion We proposed a scheduling approach based on task asymptotic complexity We presented an efficient way of determining task complexity We proposed three approaches for estimating the task computation time Our experimental result shows that ROSAIC outperforms some of the existing scheduling approaches in cloud 5/31/2018 17
- 18. 18 Thank You SECuRE and Trustworthy computing Lab Department of CIS, UAB http://secret.cis.uab.edu Shahid Noor Email: shaahid@cis.uab.edu Web: http://secret.cis.uab.edu/