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Odessa: enabling interactive perception applications on mobile devices

Published: 28 June 2011 Publication History

Abstract

Resource constrained mobile devices need to leverage computation on nearby servers to run responsive applications that recognize objects, people, or gestures from real-time video. The two key questions that impact performance are what computation to offload, and how to structure the parallelism across the mobile device and server. To answer these questions, we develop and evaluate three interactive perceptual applications. We find that offloading and parallelism choices should be dynamic, even for a given application, as performance depends on scene complexity as well as environmental factors such as the network and device capabilities. To this end we develop Odessa, a novel, lightweight, runtime that automatically and adaptively makes offloading and parallelism decisions for mobile interactive perception applications. Our evaluation shows that the incremental greedy strategy of Odessa converges to an operating point that is close to an ideal offline partitioning. It provides more than a 3x improvement in application performance over partitioning suggested by domain experts. Odessa works well across a variety of execution environments, and is agile to changes in the network, device and application inputs.

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      cover image ACM Conferences
      MobiSys '11: Proceedings of the 9th international conference on Mobile systems, applications, and services
      June 2011
      430 pages
      ISBN:9781450306430
      DOI:10.1145/1999995
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      Published: 28 June 2011

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      Author Tags

      1. incremental partitioning
      2. mobile perception application
      3. offloading
      4. parallel processing
      5. video processing

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      • (2024)Real-Time Offloading for Dependent and Parallel Tasks in Cloud-Edge Environments Using Deep Reinforcement LearningIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2023.334917735:3(391-404)Online publication date: Mar-2024
      • (2024)Energy-Latency Computation Offloading and Approximate Computing in Mobile-Edge Computing NetworksIEEE Transactions on Network and Service Management10.1109/TNSM.2024.336085021:3(3401-3415)Online publication date: Jun-2024
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