research-article

The Emerging Landscape of Edge Computing

Authors:

Shadi A. Noghabi,

Sharad Agarwal,

Ganesh AnanthanarayananAuthors Info & Claims

GetMobile: Mobile Computing and Communications, Volume 23, Issue 4

Pages 11 - 20

https://doi.org/10.1145/3400713.3400717

Published: 18 May 2020 Publication History

Abstract

Edge computing is a trending notion introduced a decade ago as a new computing paradigm for interactive mobile applications. The initial vision of the edge was a multi-tenant resource that will be used opportunistically for low-latency mobile applications. Despite that vision, we see in practice a different set of applications, driven by large-scale enterprises that have emerged and are driving realworld edge deployments today. In these applications, the edge is the primary place of storage and computation and, if network conditions allow, the cloud is opportunistically used alongside. We show how these enterprise deployments are driving innovation in edge computing. Enterprise-driven scenarios have a different motivation for using the edge. Instead of latency, the primary factors are limited bandwidth and unreliability of the network link to the cloud. The enterprise deployment layout is also unique: on-premise, single-tenant edges with shared, redundant outbound links. These previously unexplored characteristics of enterprise-driven edge scenarios open up a number of unique and exciting future research challenges for our community.

References

[1]

Airbus leveraging IoT and IBM's Watson for connected aircraft. https://internetofbusiness. com/airbus-leveraging-iot-and-ibms-watsonfor- connected-aircraft/ Accessed: 03/2019. a[2] Apple's A12 Bionic chip runs Core ML apps up to 9 times faster. https://venturebeat.com/ 2018/09/12/apples-a12-bionic-chip-runs-core-mlapps- up-to-9-times-faster/. Accessed: 02/2019.

[2]

Atos becomes official IoT partner for Coca- Cola Hellenic Bottling Company. https://atos. net/en/2018/press- release_2018_07_03/atosbecomes- official-iot-partner-coca-cola-hellenicbottling- company. Accessed: 03/2019.

[3]

AWSGreengrass. https://aws.amazon.com/ greengrass/. Accessed: 03/2019.

[4]

Azure Internet of Things. https://azure.microsoft. com/en-us/suites/iot-suite/. Accessed: 03/2019.

[5]

Azure IoT Edge. https://azure.microsoft.com/enus/ services/iot-edge/. Accessed: 03/2019.

[6]

Azure Regions. https://azure.microsoft.com/ en-us/global-infrastructure/regions/. Accessed: 02/2019.

[7]

Building a new future: Transforming Australia's construction industry with digital technologies. https://customers.microsoft.com/en-us/story/ atf-services. Accessed: 03/2019.

[8]

CAF Increases Train Safety with AWS IoT. https://aws.amazon.com/solutions/case-studies/ caf/. Accessed: 03/2019.

[9]

Developers help build a safe, standardized flight platform for industrial drones with Azure IoT Hub. https://customers.microsoft.com/en-us/ story/droneworks. Accessed: 03/2019.

[10]

Edge Computing at Chick-fil-A. https:// medium.com/@cfatechblog/edge-computing-atchick- fil-a-7d67242675e2. Accessed: 02/2019.

[11]

Fixed Broadband Speedtest Data for United States. https://www.speedtest.net/reports/unitedstates/ 2018/fixed/. Accessed: 02/2019.

[12]

Google Network Edge Locations. https: //cloud. google.com/vpc/docs/edge-locations. Accessed: 02/2019.

[13]

How Alaska outsmarts Mother Nature in the cloud. https://customers.microsoft.com/ en-us/story/alaskadotpf-government-azure-iot. Accessed: 03/2019.

[14]

Improving safety and efficiency in BMW manufacturing plants with an open source platform for managing inventory delivery. https://www.microsoft.com/developerblog/ 2018/12/19/improving-safety-and-efficiencyin- bmw-manufacturing-plants-with-an-opensource- platform-for-managing- inventorydelivery/. Accessed: 03/2019.

[15]

Internet of Aircraft Things: An industry set to be transformed. https://aviationweek.com/ connected- aerospace/internet-aircraft-thingsindustry- set-be-transformed. Accessed: 03/2019.

[16]

Oil and gas experts use machine learning to deploy predictive analytics at the edge. https:// customers.microsoft.com/en-us/story/schneiderelectric- process-mfg-resources-azure-machinelearning. Accessed: 03/2019.

[17]

One grain at a time: how Bühler is combining advanced data analysis with machine learning to tackle a global food chain problem. https:// customers.microsoft.com/en-us/story/ buhlergroup-azure-machine-learning-iot-edgeswitzerland. Accessed: 03/2019.

[18]

PCL Construction uses IoT with Azure to revolutionize the construction industry. https://customers.microsoft.com/en-us/story/ pcl-construction-professional-services-azure. Accessed: 03/2019.

[19]

Residential landline and fixed broadband services. https://www.ofcom.org.uk/__data/assets/ pdf_file/0015/113640/landline-broadband.pdf. Accessed: 02/2019.

[20]

Shell invests in safety with Azure, AI, and machine vision to better protect customers and service champions. https://customers.microsoft. com/en-us/story/shell-mining-oil-gas-azure databricks. Accessed: 03/2019.

[21]

Speedtest Market Report for Egypt. https:// www.speedtest.net/reports/egypt/. Accessed: 02/2019.

[22]

Speedtest Market Reports. https://www. speedtest.net/reports/. Accessed: 03/2019.

[23]

Transportation: What you need to know, on the go. https://www.foghorn.io/transportation/. Accessed: 03/2019.

[24]

VMware Edge--Innovate at the Edge. https:// www.vmware.com/ca/solutions/edge-internet-ofthings. html. Accessed: 03/2019.

[25]

Walmart establishes strategic partnership with Microsoft to further accelerate digital innovation in retail. https://news.microsoft.com/2018/07/16/ walmart-establishes-strategic-partnership-withmicrosoft- to-further-accelerate-digitalinnovation- in-retail/ Accessed: 03/2019.

[26]

XTO Energy taps into IoT and the cloud to optimize operations and drive growth with Azure. https://customers.microsoft.com/en-us/story/ exxonmobil-mining-oil-gas-azure. Accessed: 03/2019.

[27]

YOLO: Real-time object detection. https:// pjreddie.com/darknet/yolo/. Accessed: 03/2019.

[28]

G. Nanthanarayanan, V. Bahl, P. Bodík, K. Chintalapudi, M. Philipose, L.R. Sivalingam, and S. Sinha. 2017. Real-time video analytics -- the killer app for edge computing. IEEE Computer.

[29]

B.R. Badrinath, A. Fox, L. Kleinrock, G.J. Popek, P.L. Reiher, and M. Satyanarayanan. 2000. A conceptual framework for network and client adaptation. MONET 5, 4, 221--231.

[30]

P. Bahl, A. Adya, J. Pakhye, and A. Wolman. Oct. 2004. Reconsidering wireless systems with multiple radios. SIGCOMM Comput. Commun. Rev. 34, 5, 39--46.

Digital Library

[31]

R.K. Balan, M. Satyanarayanan, S.Y. Park, and T. Okoshi. Tactics-based remote execution for mobile computing. 2003. In Proceedings of the 1st International Conference on Mobile Systems, Applications and Services (New York, NY, USA), MobiSys '03, ACM, pp. 273--286.

[32]

V. Brik, A. Mishra, and S. Banerjee. 2005. Eliminating handoff latencies in 802.11 wlans using multiple radios: Applications, experience, and evaluation. In Proceedings of the 5th ACM SIGCOMM Conference on Internet Measurement (Berkeley, CA, USA, 2005), IMC '05, USENIX Association, pp. 27--27.

[33]

B.-G. Chun, S. Ihm, P. Maniatis, M. Naik, and A. Patti. Clonecloud: Elastic execution between mobile device and cloud. In Proceedings of the Sixth Conference on Computer Systems (New York, NY, USA, 2011), EuroSys '11, ACM, pp. 301--314.

Digital Library

[34]

B.-G. Chun and P. Maniatis. 2009. Augmented smartphone applications through clone cloud execution. In Proceedings of the 12th Conference on Hot Topics in Operating Systems (Berkeley, CA, USA), HotOS'09, USENIX Association, pp. 8--8.

[35]

CISCO. New Realities in Oil and Gas: Data Management and Analytics. Tech. rep., Cisco, 2017.

[36]

M.J. Clifford, R.K. Perrons, S.H. ALI, and T.A. Grice. Extracting Innovations: Mining, Energy, and Technological Change in the Digital Age. CRC Press, 2018.

[37]

E. Cuervo, A. Balasubramanian, D.-K. Cho, A. Wolman, S. Saroiu, R. Chandra, and P. Bahl. 2010. Maui: Making smartphones last longer with code offload. In Proceedings of the 8th International Conference on Mobile Systems, Applications, and Services (New York, NY, USA), MobiSys '10, ACM, pp. 49--62.

[38]

Federal Communications Commission, Office of Engineering and Technology. Measuring Fixed Broadband -- Eighth Report December 14, 2018. https://www.fcc.gov/reports-research/reports/ measuring-broadband-america/measuring-fixedbroadband- eighth-report. Accessed: 03/2019.

[39]

Daihen FogHorn. 2018. Automates Production of Industrial Transformers, Improves Materials Quality Monitoring and Collaboration. Tech. rep., FogHorn Systems.

[40]

FogHorn. 2018. GE Detects Early Defects and Improves Capacitor Production Yield with Edge Intelligence. Tech. rep., FogHorn Systems, 2018.

[41]

FOGHORN. Global Consumer Packaged Goods Company Improves Yield through Real-time Insights. Tech. rep., FogHorn Systems.

[42]

A. Fox, S.D. Gribble, E.A. Brewer and E. Amir. 1996. Adapting to network and client variability via on-demand dynamic distillation. In Proceedings of the Seventh International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS.

[43]

P. Garcia Lopez, A. Montresor, D. Epema, A. Datta, T. Higashino, A. Iamnitchi, M. Barcellos, P. Felber, and E. Riviere. 2015. Edge-centric computing: Vision and challenges. SIGCOMM Computer Communication Review 45, 5.

Digital Library

[44]

K. Ha, Y. Abe, T. Eiszler, Z. Chen, W. Hu, B. Amos, R. Upadhyaya, P. Pillai, and M. Satyanarayanan. 2017. You can teach elephants to dance: agile vm handoff for edge computing. In Proceedings of the Second ACM/IEEE Symposium on Edge Computing, ACM, p. 12.

[45]

B.D. Higgins, A. Reda, T. Alperovich, J. Flinn, T.J. Giuli, B. Noble, and D. Watson. 2010. Intentional networking: opportunistic exploitation of mobile network diversity. In Proceedings of the 16th Annual International Conference on Mobile Computing and Networking, MOBICOM Chicago, Illinois, USA, September 20--24, pp. 73--84.

[46]

S. Kandula, K.C.-J. Lin, T. Badirkhanli, and D. Katabi. 2008. Fatvap: Aggregating ap backhaul capacity to maximize throughput. In Proceedings of the 5th USENIX Symposium on Networked Systems Design and Implementation (Berkeley, CA, USA), NSDI'08, USENIX Association, pp. 89--104.

[47]

J.J. Kistler, and M. Satyanarayanan. Disconnected operation in the coda file system. Feb. 1992. ACM Trans. Comput. Syst. 10, 1, 3--25.

Digital Library

[48]

A. Li, X. Yang, S. Kandula, and M. Zhang. 2010. Cloudcmp: Comparing public cloud providers. In Proceedings of the 10th ACM SIGCOMM Conference on Internet Measurement, IMC '10, ACM, pp. 1--14.

[49]

F. Loewenherz, V. Bahl, and Y. Wang. March 2017. Video analytics towards vision zero. ITE Journal 87, 25--28.

[50]

S. Mahadev, G. Lewis, E. Morris, S. Simanta, J. Boleng, and K. Ha. 2013. The role of cloudlets in hostile environments. Pervasive Computing 12, 4.

[51]

A. Miu, H. Balakrishnan, and C.E. Koksal. 2005. Improving loss resilience with multi-radio diversity in wireless networks. In Proceedings of the 11th Annual International Conference on Mobile Computing and Networking (New York, NY, USA), MobiCom '05, ACM, pp. 16--30.

[52]

L.B. Mummert, M.R. Ebling, and M. Satyanarayanan. Exploiting weak connectivity for mobile file access. In Proceedings of the Fifteenth ACM Symposium on Operating Systems Principles (1995), SOSP.

Digital Library

[53]

A.J. Nicholson, Y. Chawathe, M.Y. Chen, B.D. Noble, and D. Wetherall Improved access point selection. In Proceedings of the 4th International Conference on Mobile Systems, Applications and Services (New York, NY, USA, 2006), MobiSys '06, ACM, pp. 233--245.

Digital Library

[54]

A.J. Nicholson, S. Wolchok, and B.D. Noble. Juggler: Virtual networks for fun and profit. IEEE Transactions on Mobile Computing 9, 1 (Jan. 2010), pp. 31--43.

[55]

B. Noble, M. Satyanarayanan D. Narayanan, J.E. Tilton, J. Flinn and K.R. Walker. 1997. Agile application-aware adaptation for mobility. In Proceedings of the Symposium on Operating Systems Principles (SOSP), ACM.

[56]

OPENSIGNAL. Mobile Network Experience Report. January 2019. https://www.opensignal. com/reports/2019/01/usa/mobile-networkexperience. Accessed: 03/2019.

[57]

P. Owen, and R. Martin. 2018. The Business Case for IIOT Edge Intelligence. Tech. rep., ABI Research.

[58]

M.-R. Ra, A. Sheth, L. Mummert, P. Pillai, D. Wetherall, and R. Govindan. 2011. Odessa: Enabling interactive perception applications on mobile devices. In Proceedings of the 9th International Conference on Mobile Systems, Applications, and Services (New York, NY, USA, MobiSys '11, ACM, pp. 43--56.

[59]

M. Satyanarayanan. 2001. Pervasive computing: Vision and challenges. IEEE Personal Communications 8, 10--17.

[60]

M. Satyanarayanan. 2017. The emergence of edge computing. Computer 50, 1.

[61]

M. Satyanarayanan, P. Bahl, R. Caceres, and N. Davies. 2009. The case for vm-based cloudlets in mobile computing. Pervasive Computing 8, 4.

Digital Library

[62]

M. Satyanarayanan, R. Schuster, M. Ebling. G. Fettweis, H. Flinchk, K. Jopshi and K. Sabnani. 2015. An open ecosystem for mobile-cloud convergence. Communications Magazine 53, 3.

[63]

M. Satyanarayanan, P. Simoens, Y. Xiao, P. Pillai, Z. Chen, K. Ha, W. Hu and B. Amos. 2015.Edge analytics in the internet of things. Pervasive Computing 14, 2.

[64]

W. Shi, J. Cao, Q. Zhang, Y. Li, and L. Xu. 2016. Edge computing: Vision and challenges. Internet of Things Journal 3, 5.

[65]

D. Vasisht, Z. Kapetanovic, J. Won, X. Jin, R. Chandra, S.N. Sinha, A. Kapoor, M. Sudarshan, and S. Stratman. FarmBeats: An iot platform for data-driven agriculture. 2017. In Proceedings of the Symposium on Networked Systems Design and Implementation (NSDI), USENIX.

[66]

J. Wang. B. Amox. A. Das, P. Pillai, N. Sadeh, and M. Satyanarayanan. 2017. A scalable and privacy-aware iot service for live video analytics. In Proceedings of the 8th ACM on Multimedia Systems Conference, ACM, pp. 38--49.

[67]

J. Wang, Z. Feng, Z. Chen, S. George, M. Bala, P. Pillai, S.-W. Yang, and M. Satyanarayanan. Bandwidth-efficient live video analytics for drones via edge computing. In Proceedings of the Third ACM/IEEE Symposium on Edge Computing (2018), SEC '18, IEEE.

[68]

S. Yi, Z Hao, Q. Zhang, Q. Zhang, W. Shi, and Q. Li. 2017. Lavea: Latency-aware video analytics on edge computing platform. In Proceedings of the Second ACM/IEEE Symposium on Edge Computing. SEC '17, ACM.

Cited By

Zhang YZhang XAnanthanarayanan GIyer AShu YBahl VMao ZChowdhury MVanbever LZhang I(2024)VulcanProceedings of the 21st USENIX Symposium on Networked Systems Design and Implementation10.5555/3691825.3691902(1385-1402)Online publication date: 16-Apr-2024
https://dl.acm.org/doi/10.5555/3691825.3691902
Kolevski DMichael K(2024)Edge Computing and IoT Data Breaches: Security, Privacy, Trust, and RegulationIEEE Technology and Society Magazine10.1109/MTS.2024.337260543:1(22-32)Online publication date: Mar-2024
https://doi.org/10.1109/MTS.2024.3372605
Choi WYoo YLee KNiu ZCheng PXiong YYang GYoo C(2024)Intelligent Packet Processing for Performant Containers in IoTIEEE Internet of Things Journal10.1109/JIOT.2024.345341011:24(40388-40403)Online publication date: 15-Dec-2024
https://doi.org/10.1109/JIOT.2024.3453410
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The Emerging Landscape of Edge Computing
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Published In

cover image GetMobile: Mobile Computing and Communications

GetMobile: Mobile Computing and Communications Volume 23, Issue 4

December 2019

34 pages

ISSN:2375-0529

EISSN:2375-0537

DOI:10.1145/3400713

Editor:
Landon Cox
Microsoft Research

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Copyright © 2020 Copyright is held by the owner/author(s).

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Publication History

Published: 18 May 2020

Published in SIGMOBILE-GETMOBILE Volume 23, Issue 4

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Cited By

Zhang YZhang XAnanthanarayanan GIyer AShu YBahl VMao ZChowdhury MVanbever LZhang I(2024)VulcanProceedings of the 21st USENIX Symposium on Networked Systems Design and Implementation10.5555/3691825.3691902(1385-1402)Online publication date: 16-Apr-2024
https://dl.acm.org/doi/10.5555/3691825.3691902
Kolevski DMichael K(2024)Edge Computing and IoT Data Breaches: Security, Privacy, Trust, and RegulationIEEE Technology and Society Magazine10.1109/MTS.2024.337260543:1(22-32)Online publication date: Mar-2024
https://doi.org/10.1109/MTS.2024.3372605
Choi WYoo YLee KNiu ZCheng PXiong YYang GYoo C(2024)Intelligent Packet Processing for Performant Containers in IoTIEEE Internet of Things Journal10.1109/JIOT.2024.345341011:24(40388-40403)Online publication date: 15-Dec-2024
https://doi.org/10.1109/JIOT.2024.3453410
Jung KMitra GGopalakrishnan SPattabiraman K(2024)ImmunoPlane: Middleware for Providing Adaptivity to Distributed Internet-of-Things Applications2024 IEEE/ACM Ninth International Conference on Internet-of-Things Design and Implementation (IoTDI)10.1109/IoTDI61053.2024.00006(13-24)Online publication date: 13-May-2024
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Zeng YZhou RJiao LHan ZYu JMa Y(2024)Efficient Online DNN Inference with Continuous Learning in Edge Computing2024 IEEE/ACM 32nd International Symposium on Quality of Service (IWQoS)10.1109/IWQoS61813.2024.10682835(1-10)Online publication date: 19-Jun-2024
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Mishra CSampson JKandemir MNarayanan VDas C(2024)Usas: A Sustainable Continuous-Learning´ Framework for Edge Servers2024 IEEE International Symposium on High-Performance Computer Architecture (HPCA)10.1109/HPCA57654.2024.00073(891-907)Online publication date: 2-Mar-2024
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Zietsch JKulaga RHeld HHerrmann CThiede S(2024)Multi-layer edge resource placement optimization for factoriesJournal of Intelligent Manufacturing10.1007/s10845-022-02071-335:2(825-840)Online publication date: 1-Feb-2024
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Hu BHu WSha KBanerjee SChen J(2023)Middleware Support for Edge Data Analytics over Heterogeneous ScenariosProceedings of the Eighth ACM/IEEE Symposium on Edge Computing10.1145/3583740.3626613(171-184)Online publication date: 6-Dec-2023
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