ReStorEdge: An edge computing system with reuse semantics
Abstract: This paper investigates an edge computing system where requests are processed by a set of replicated edge servers. We investigate a class of applications where similar queries produce identical results. To reduce processing overhead on the edge servers we store the results of previous computations and return them when new queries are sufficiently similar to earlier ones that produced the results, avoiding the necessity of processing every new query. We implement a similarity-based data classification system, which we evaluate based on real-world datasets of images and voice queries. We evaluate a range of orchestration strategies to distribute queries and cached results between edge nodes and show that the throughput of queries over a system of distributed edge nodes can be increased by 25-33%, increasing its capacity for higher workloads.
- W. Shi, J. Cao, Q. Zhang, Y. Li, and L. Xu, “Edge computing: Vision and challenges,” IEEE internet of things journal, vol. 3, no. 5, pp. 637–646, 2016.
- C.-C. Hung, G. Ananthanarayanan, P. Bodik, L. Golubchik, M. Yu, P. Bahl, and M. Philipose, “Videoedge: Processing camera streams using hierarchical clusters,” in 2018 IEEE/ACM Symposium on Edge Computing (SEC). IEEE, 2018, pp. 115–131.
- A.-C. Nicolaescu, S. Mastorakis, and I. Psaras, “Store edge networked data (send): A data and performance driven edge storage framework,” in IEEE INFOCOM 2021 - IEEE Conference on Computer Communications, 2021, pp. 1–10.
- P. Guo and W. Hu, “Potluck: Cross-application approximate deduplication for computation-intensive mobile applications,” in Proceedings of the Twenty-Third International Conference on Architectural Support for Programming Languages and Operating Systems, 2018, pp. 271–284.
- M. W. Al Azad and S. Mastorakis, “The promise and challenges of computation deduplication and reuse at the network edge,” IEEE Wireless Communications, 2022.
- A. Andoni, P. Indyk, T. Laarhoven, I. Razenshteyn, and L. Schmidt, “Practical and optimal lsh for angular distance,” in Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 1, ser. NIPS’15. Cambridge, MA, USA: MIT Press, 2015, p. 1225–1233.
- N. Sundaram, A. Turmukhametova, N. Satish, T. Mostak, P. Indyk, S. Madden, and P. Dubey, “Streaming similarity search over one billion tweets using parallel locality-sensitive hashing,” Proc. VLDB Endow., vol. 6, no. 14, p. 1930–1941, sep 2013. [Online]. Available: https://doi.org/10.14778/2556549.2556574
- Q. Lv, W. Josephson, Z. Wang, M. Charikar, and K. Li, “Multi-probe lsh: Efficient indexing for high-dimensional similarity search,” in Proceedings of the 33rd International Conference on Very Large Data Bases, ser. VLDB ’07. VLDB Endowment, 2007, p. 950–961.
- A. Dasgupta, R. Kumar, and T. Sarlos, “Fast locality-sensitive hashing,” in Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ser. KDD ’11. New York, NY, USA: Association for Computing Machinery, 2011, p. 1073–1081. [Online]. Available: https://doi.org/10.1145/2020408.2020578
- W. Zhang, K. Gao, Y.-d. Zhang, and J.-t. Li, “Data-oriented locality sensitive hashing,” in Proceedings of the 18th ACM International Conference on Multimedia, ser. MM ’10. New York, NY, USA: Association for Computing Machinery, 2010, p. 1131–1134. [Online]. Available: https://doi.org/10.1145/1873951.1874168
- M. A. Azad and S. Mastorakis, “Reservoir: Named data for pervasive computation reuse at the network edge,” in 2022 IEEE International Conference on Pervasive Computing and Communications (PerCom). Los Alamitos, CA, USA: IEEE Computer Society, mar 2022, pp. 141–151. [Online]. Available: https://doi.ieeecomputersociety.org/10.1109/PerCom53586.2022.9762397
- A.-C. Nicolaescu, O. Ascigil, and I. Psaras, “Edge data repositories - the design of a store-process-send system at the edge,” in Proceedings of the 1st ACM CoNEXT Workshop on Emerging In-Network Computing Paradigms, 2019, p. 41–47.
- A. N. Ye, Z. Hu, C. Phillips, and I. Mohomed, “Alertme: Towards natural language-based live video trigger systems at the edge,” in Proceedings of the 4th International Workshop on Edge Systems, Analytics and Networking, ser. EdgeSys ’21. New York, NY, USA: Association for Computing Machinery, 2021, p. 67–72. [Online]. Available: https://doi.org/10.1145/3434770.3459740
- B. Hu and W. Hu, “Linkshare: Device-centric control for concurrent and continuous mobile-cloud interactions,” in Proceedings of the 4th ACM/IEEE Symposium on Edge Computing, ser. SEC ’19. New York, NY, USA: Association for Computing Machinery, 2019, p. 15–29. [Online]. Available: https://doi.org/10.1145/3318216.3363303
- M. Krol et al., “Computation offloading with ICN,” Proceedings of the 5th ACM Conference on Information-Centric Networking - ICN ’18, 2018.
- Y. Ye, L. Xiao, I.-L. Yen, and F. Bastani, “Secure, dependable, and high performance cloud storage,” in 2010 29th IEEE Symposium on Reliable Distributed Systems. IEEE, 2010, pp. 194–203.
- D. Harnik, B. Pinkas, and A. Shulman-Peleg, “Side channels in cloud services: Deduplication in cloud storage,” IEEE Security & Privacy, vol. 8, no. 6, pp. 40–47, 2010.
- J. Wang, Z. Zhao, Z. Xu, H. Zhang, L. Li, and Y. Guo, “I-sieve: An inline high performance deduplication system used in cloud storage,” Tsinghua Science and Technology, vol. 20, no. 1, pp. 17–27, 2015.
- B. Mao et al., “Improving storage availability in cloud-of-clouds with hybrid redundant data distribution,” in 2015 IEEE International Parallel and Distributed Processing Symposium, 2015, pp. 633–642.
- C. Barrios and M. Kumar, “Service caching and computation reuse strategies at the edge: A survey,” ACM Comput. Surv., jul 2023, just Accepted. [Online]. Available: https://doi.org/10.1145/3609504
- L. Zhang, A. Afanasyev, J. Burke, V. Jacobson, k. claffy, P. Crowley, C. Papadopoulos, L. Wang, and B. Zhang, “Named data networking,” SIGCOMM Comput. Commun. Rev., vol. 44, no. 3, p. 66–73, jul 2014. [Online]. Available: https://doi.org/10.1145/2656877.2656887
- S. Mastorakis, A. Mtibaa, J. Lee, and S. Misra, “ICedge: When Edge Computing Meets Information-Centric Networking,” IEEE Internet of Things Journal, vol. 7, no. 5, pp. 4203–4217, 2020.
- A. Sabnis, T. S. Salem, G. Neglia, M. Garetto, E. Leonardi, and R. K. Sitaraman, “Grades: Gradient descent for similarity caching,” in IEEE INFOCOM 2021 - IEEE Conference on Computer Communications, 2021, pp. 1–10.
- A. Katsarakis, Y. Ma, Z. Tan, A. Bainbridge, M. Balkwill, A. Dragojevic, B. Grot, B. Radunovic, and Y. Zhang, “Zeus: Locality-aware distributed transactions,” in Proceedings of the Sixteenth European Conference on Computer Systems, ser. EuroSys ’21. New York, NY, USA: Association for Computing Machinery, 2021, p. 145–161. [Online]. Available: https://doi.org/10.1145/3447786.3456234
- B. Charyyev and M. H. Gunes, “Locality-sensitive iot network traffic fingerprinting for device identification,” IEEE Internet of Things Journal, vol. 8, no. 3, pp. 1272–1281, 2021.
- W. Xu, H. Song, L. Hou, H. Zheng, X. Zhang, C. Zhang, W. Hu, Y. Wang, and B. Liu, “Soda: Similar 3d object detection accelerator at network edge for autonomous driving,” in IEEE INFOCOM 2021 - IEEE Conference on Computer Communications, 2021, pp. 1–10.
- P. Guo, B. Hu, R. Li, and W. Hu, “Foggycache: Cross-device approximate computation reuse,” in Proceedings of the 24th Annual International Conference on Mobile Computing and Networking, 2018.
- P. Zhang, H. Pan, Z. Li, P. He, Z. Zhang, G. Tyson, and G. Xie, “Accelerating lsh-based distributed search with in-network computation,” in IEEE INFOCOM 2021 - IEEE Conference on Computer Communications, 2021, pp. 1–10.
- S. Farrell, D. Kutscher, C. Dannewitz, B. Ohlman, A. Keränen, and P. Hallam-Baker, “Naming Things with Hashes,” RFC 6920, Apr. 2013. [Online]. Available: https://www.rfc-editor.org/info/rfc6920
- L. Saino, I. Psaras, and G. Pavlou, “Icarus: a caching simulator for information centric networking (icn),” in SimuTools. ICST, 2014.
- ——, “Hash-routing schemes for information centric networking,” in Proceedings of the 3rd ACM SIGCOMM Workshop on Information-Centric Networking, ser. ICN ’13. New York, NY, USA: Association for Computing Machinery, 2013, p. 27–32. [Online]. Available: https://doi.org/10.1145/2491224.2491232
- X. Tang et al., “Mix and match: Reorganizing tasks for enhancing data locality,” in Abstract Proceedings of the 2021 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems, ser. SIGMETRICS ’21. New York, NY, USA: Association for Computing Machinery, 2021, p. 47–48. [Online]. Available: https://doi.org/10.1145/3410220.3460103
- S. Colianni, “Mnist as .jpg,” Kaggle database website, 2017, posted at https://www.kaggle.com/datasets/scolianni/mnistasjpg.
- A. Anhari, “Alexa dataset,” Kaggle database website, 2018, posted at https://www.kaggle.com/datasets/aanhari/alexa-dataset.
- fluent.ai, “Fluent speech commands: A dataset for spoken language understanding research,” fluent.ai website, 2020.
- P. Haghani, S. Michel, P. Cudré-Mauroux, and K. Aberer, “Lsh at large - distributed knn search in high dimensions,” in WebDB, 2008.
- J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” CoRR, vol. abs/1506.02640, 2015. [Online]. Available: http://arxiv.org/abs/1506.02640
- T. Giannakopoulos, “pyaudioanalysis: An open-source python library for audio signal analysis,” PLOS ONE, vol. 10, no. 12, pp. 1–17, 12 2015. [Online]. Available: https://doi.org/10.1371/journal.pone.0144610
- D. Huggins-Daines, M. Kumar, A. Chan, A. Black, M. Ravishankar, and A. Rudnicky, “Pocketsphinx: A free, real-time continuous speech recognition system for hand-held devices,” in 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings, vol. 1, 2006, pp. I–I.
- “BGP in 2018 — The BGP Table,” May 2020, [accessed 25 May 2022]. [Online]. Available: https://blog.apnic.net/2019/01/16/bgp-in-2018-the-bgp-table/
- ETSI. (2018, 8) ETSI GS NFV-IFA 005. ETSI. [Online]. Available: https://www.etsi.org/deliver/etsi_gs/nfv-ifa/001_099/005/03.01.01_60/gs_nfv-ifa005v030101p.pdf
- ——. (2019, 6) OSM VNF ONBOARDING GUIDELINES. ETSI. [Online]. Available: https://osm.etsi.org/images/OSM_VNF_Onboarding_Guidelines_June_2019.pdf
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.