Abstract
Emerging mobile applications, such as cognitive assistance and augmented reality (AR) based gaming, are increasingly computation-intensive and latency-sensitive, while running on resource-constrained devices. The standard approaches to addressing these involve either offloading to a cloud(let) or local system optimizations to speed up the computation, often trading off computation quality for low latency. Instead, we observe that these applications often operate on similar input data from the camera feed and share common processing components, both within the same (type of) applications and across different ones. Therefore, deduplicating processing across applications could deliver the best of both worlds. In this paper, we present Potluck, to achieve approximate deduplication. At the core of the system is a cache service that stores and shares processing results between applications and a set of algorithms to process the input data to maximize deduplication opportunities. This is implemented as a background service on Android. Extensive evaluation shows that Potluck can reduce the processing latency for our AR and vision workloads by a factor of 2.5 to 10.
- Alexa Skills Kit: Add Voice to Your Big Idea and Reach More Customers. https://developer.amazon.com/alexa-skills-kit.Google Scholar
- Android Interface Definition Language for IPC. https://developer.android.com/guide/components/aidl.html.Google Scholar
- Google's indoor VPS navigation by Tango-ready phone. http://mashable.com/2017/05/google-visual-positioning-service-tango-augmented-reality/.Google Scholar
- How stores will use augmented reality. https://www.technologyreview.com/s/601664/.Google Scholar
- N-Dimensional Arrays for Java. http://nd4j.org/.Google Scholar
- Open-source computer vision. http://opencv.org/.Google Scholar
- Openhft java runtime compiler. https://github.com/OpenHFT/Java-Runtime-Compiler.Google Scholar
- Openshade with visually impaired users. http://www.openshades.com/.Google Scholar
- PokeMon Go augmented reality game. http://www.pokemongo.com/.Google Scholar
- World around you with Google Lens and the Assistant. https://www.blog.google/products/assistant/world-around-you-google-lens-and-assistant/.Google Scholar
- M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, et al. TensorFlow: A System for Large-Scale Machine Learning. In OSDI, volume 16, pages 265--283, 2016. Google Scholar
Digital Library
- H. Bay, T. Tuytelaars, and L. Van Gool. Surf: Speeded up robust features. In European conference on computer vision, pages 404--417. Springer, 2006. Google Scholar
Digital Library
- G. Bertasius, J. Shi, and L. Torresani. Deepedge: A multi-scale bifurcated deep network for top-down contour detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 4380--4389, 2015.Google Scholar
Cross Ref
- K. Boos, D. Chu, and E. Cuervo. Flashback: Immersive virtual reality on mobile devices via rendering memoization. In Proceedings of the 14th Annual International Conference on Mobile Systems, Applications, and Services, pages 291--304. ACM, 2016. Google Scholar
Digital Library
- T. Y.-H. Chen, L. Ravindranath, S. Deng, P. Bahl, and H. Balakrishnan. Glimpse: Continuous, real-time object recognition on mobile devices. In Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems, pages 155--168. ACM, 2015. Google Scholar
Digital Library
- M. Datar, N. Immorlica, P. Indyk, and V. S. Mirrokni. Locality-sensitive hashing scheme based on p-stable distributions. In Proceedings of the twentieth annual symposium on Computational geometry, pages 253--262. ACM, 2004. Google Scholar
Digital Library
- H. Falaki, R. Mahajan, S. Kandula, D. Lymberopoulos, R. Govindan, and D. Estrin. Diversity in smartphone usage. In Proceedings of the 8th international conference on Mobile systems, applications, and services, pages 179--194. ACM, 2010. Google Scholar
Digital Library
- J. Flinn. Cyber foraging: Bridging mobile and cloud computing. Synthesis Lectures on Mobile and Pervasive Computing, 7(2):1--103, 2012.Google Scholar
Cross Ref
- P. Georgiev, N. D. Lane, K. K. Rachuri, and C. Mascolo. LEO: Scheduling sensor inference algorithms across heterogeneous mobile processors and network resources. In Proceedings of the Annual International Conference on Mobile Computing and Networking, MobiCom16. ACM, 2016. Google Scholar
Digital Library
- P. K. Gunda, L. Ravindranath, C. A. Thekkath, Y. Yu, and L. Zhuang. Nectar: Automatic Management of Data and Computation in Datacenters. In OSDI, volume 10, pages 1--8, 2010. Google Scholar
Digital Library
- K. Ha, Z. Chen, W. Hu, W. Richter, P. Pillai, and M. Satyanarayanan. Towards Wearable Cognitive Assistance. In Proceedings of the 12th Annual International Conference on Mobile Systems, Applications, and Services, MobiSys '14, pages 68--81, New York, NY, USA, 2014. ACM. Google Scholar
Digital Library
- J. Hafner, H. S. Sawhney, W. Equitz, M. Flickner, and W. Niblack. Efficient color histogram indexing for quadratic form distance functions. IEEE transactions on pattern analysis and machine intelligence, 17(7):729--736, 1995. Google Scholar
Digital Library
- S. Han, H. Shen, M. Philipose, S. Agarwal, A. Wolman, and A. Krishnamurthy. MCDNN: An Approximation-Based Execution Framework for Deep Stream Processing Under Resource Constraints. In Proceedings of the 14th Annual International Conference on Mobile Systems, Applications, and Services, MobiSys '16, pages 123--136, New York, NY, USA, 2016. ACM. Google Scholar
Digital Library
- C. Harris and M. Stephens. A combined corner and edge detector. In Alvey vision conference, volume 15, pages 10--5244. Citeseer, 1988.Google Scholar
- I. Jolliffe. Principal component analysis. Wiley Online Library, 2002.Google Scholar
- K. Kannan, S. Bhattacharya, K. Raj, M. Murugan, and D. Voigt. Seesaw-similarity exploiting storage for accelerating analytics workflows. In HotStorage, 2016. Google Scholar
Digital Library
- J. M. Keller, M. R. Gray, and J. A. Givens. A fuzzy k-nearest neighbor algorithm. IEEE transactions on systems, man, and cybernetics, (4):580--585, 1985.Google Scholar
- A. Krizhevsky and G. Hinton. Learning multiple layers of features from tiny images. 2009.Google Scholar
- A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, pages 1097--1105, 2012. Google Scholar
Digital Library
- J. Le Feuvre, J. Thiesse, M. Parmentier, M. Raulet, and C. Daguet. Ultra high definition HEVC DASH data set. In Proceedings of the 5th ACM Multimedia Systems Conference, pages 7--12. ACM, 2014. Google Scholar
Digital Library
- Y. LeCun, C. Cortes, and C. J. Burges. Mnist handwritten digit database. AT&T Labs {Online}. Available: http://yann.lecun.com/exdb/mnist, 2, 2010.Google Scholar
- K. Lee, D. Chu, E. Cuervo, J. Kopf, Y. Degtyarev, S. Grizan, A. Wolman, and J. Flinn. Outatime: Using Speculation to Enable Low-Latency Continuous Interaction for Mobile Cloud Gaming. In Proceedings of the 13th Annual International Conference on Mobile Systems, Applications, and Services, MobiSys '15, pages 151--165, New York, NY, USA, 2015. ACM. Google Scholar
Digital Library
- R. LiKamWa and L. Zhong. Starfish: Efficient concurrency support for computer vision applications. In Proceedings of the 13th Annual International Conference on Mobile Systems, Applications, and Services, pages 213--226. ACM, 2015. Google Scholar
Digital Library
- X. Liu, H. Li, X. Lu, T. Xie, Q. Mei, H. Mei, and F. Feng. Understanding Diverse Smarpthone Usage Patterns from Large-Scale Appstore-Service Profiles. arXiv preprint arXiv:1702.05060, 2017.Google Scholar
- D. G. Lowe. Distinctive image features from scale-invariant keypoints. International journal of computer vision, 60(2):91--110, 2004. Google Scholar
Digital Library
- L. McMillan and G. Bishop. Plenoptic modeling: An image-based rendering system. In Proceedings of the 22nd annual conference on Computer graphics and interactive techniques, pages 39--46. ACM, 1995. Google Scholar
Digital Library
- F. D. McSherry, R. Isaacs, M. A. Isard, and D. G. Murray. Differential dataflow, Oct. 20 2015. US Patent 9,165,035.Google Scholar
- P. Mermelstein. Distance measures for speech recognition, psychological and instrumental. Pattern recognition and artificial intelligence, 116:374--388, 1976.Google Scholar
- J. S. Miguel, J. Albericio, A. Moshovos, and N. E. Jerger. Doppelg"anger: A Cache for Approximate Computing. In Proceedings of the 48th International Symposium on Microarchitecture, pages 50--61. ACM, 2015. Google Scholar
Digital Library
- B. D. Noble, M. Satyanarayanan, D. Narayanan, J. E. Tilton, J. Flinn, and K. R. Walker. Agile Application-aware Adaptation for Mobility. In Proceedings of the Sixteenth ACM Symposium on Operating Systems Principles, SOSP '97, pages 276--287, New York, NY, USA, 1997. ACM. Google Scholar
Digital Library
- L. Popa, M. Budiu, Y. Yu, and M. Isard. DryadInc: Reusing Work in Large-scale Computations. In HotCloud, 2009. Google Scholar
Digital Library
- E. Rosten and T. Drummond. Machine learning for high-speed corner detection. In European conference on computer vision, pages 430--443. Springer, 2006. Google Scholar
Digital Library
- M. Satyanarayanan, P. Bahl, R. Caceres, and N. Davies. The case for vm-based cloudlets in mobile computing. IEEE pervasive Computing, 8(4), 2009. Google Scholar
Digital Library
- A. Shashua, Y. Gdalyahu, and G. Hayun. Pedestrian detection for driving assistance systems: Single-frame classification and system level performance. In Intelligent Vehicles Symposium, 2004 IEEE, pages 1--6. IEEE, 2004.Google Scholar
Cross Ref
- F. Suard, A. Rakotomamonjy, A. Bensrhair, and A. Broggi. Pedestrian detection using infrared images and histograms of oriented gradients. In Intelligent Vehicles Symposium, 2006 IEEE, pages 206--212. IEEE, 2006.Google Scholar
Cross Ref
- Y. Tang and J. Yang. Secure deduplication of general computations. In USENIX Annual Technical Conference, pages 319--331, 2015. Google Scholar
Digital Library
- K. Walsh and E. G. Sirer. Experience with an object reputation system for peer-to-peer filesharing. In USENIX NSDI, volume 6, 2006. Google Scholar
Digital Library
- H. Wang, T. Tian, M. Ma, and J. Wu. Joint Compression of Near-Duplicate Videos. IEEE Transactions on Multimedia, 2016. Google Scholar
Digital Library
- Z. Wang, D. Liu, J. Yang, W. Han, and T. Huang. Deep networks for image super-resolution with sparse prior. In Proceedings of the IEEE International Conference on Computer Vision, pages 370--378, 2015. Google Scholar
Digital Library
- H. Wu, X. Sun, J. Yang, W. Zeng, and F. Wu. Lossless Compression of JPEG Coded Photo Collections. IEEE Transactions on Image Processing, 25(6):2684--2696, 2016. Google Scholar
Digital Library
- T. Zhang, A. Chowdhery, P. V. Bahl, K. Jamieson, and S. Banerjee. The design and implementation of a wireless video surveillance system. In Proceedings of the 21st Annual International Conference on Mobile Computing and Networking, pages 426--438. ACM, 2015. Google Scholar
Digital Library
- K. Zhou, Q. Hou, R. Wang, and B. Guo. Real-time kd-tree construction on graphics hardware. ACM Transactions on Graphics (TOG), 27(5):126, 2008. Google Scholar
Digital Library
Index Terms
Potluck: Cross-Application Approximate Deduplication for Computation-Intensive Mobile Applications
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