Quantifying the Impact of Edge Computing on Mobile Applications
Open accessAbstract
Computational offloading services at the edge of the Internet for mobile devices are becoming a reality. Using a wide range of mobile applications, we explore how such infrastructure improves latency and energy consumption relative to the cloud. We present experimental results from WiFi and 4G LTE networks that confirm substantial wins from edge computing for highly interactive mobile applications.
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Many new types of applications, such as cooperative autonomous driving, have strict or tight delay requirements that the cloud would have difficulty meeting since it may be far away from the users.
“many new types of applications, e.g., cooperative autonomous driving, have strict or tight delay requirements that the cloud would have difficulty meeting since it may be far away from the users [4].”
From Convergence of Edge Computing and Deep Learning: A Comprehensive Survey (2020) · cites this paper as [4]
The Introduction describes future compute- and latency-sensitive applications, and §3.1 reports that response times worsen as offload sites become more distant. The paper gives cognitive assistance and mobile augmented reality as examples, but our copy does not identify cooperative autonomous driving or establish its delay requirements. (LLM, conf=0.85)
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Cites
Turk, M., & Pentland, A. (1991) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.1991.3.1.71 · Cited by 13,733 · see citing works
Fog computing and its role in the internet of things
Bonomi, F., Milito, R., Zhu, J., & Addepalli, S. (2012) · Proceedings of the first edition of the MCC workshop on Mobile cloud computing · doi:10.1145/2342509.2342513 · Cited by 6,244 · see citing works
Bradski, G. R. (2000) · Medical Entomology and Zoology · Cited by 5,994 · see citing works
The case for vm-based cloudlets in mobile computing
Satyanarayanan, M., Bahl, P., Caceres, R., & Davies, N. (2009) · IEEE Pervasive Computing · doi:10.1109/mprv.2009.82 · Cited by 3,660 · see citing works
Self-citationPervasive computing: vision and challenges
Satyanarayanan, M. (2001) · IEEE Personal Communications · doi:10.1109/98.943998 · Cited by 2,416 · see citing works
Self-citationA close examination of performance and power characteristics of 4G LTE networks
Huang, J., Qian, F., Gerber, A., Mao, Z. M., Sen, S. B., & Spatscheck, O. (2012) · International Conference on Mobile Systems, Applications, and Services · doi:10.1145/2307636.2307658 · Cited by 996 · see citing works
Li, A., Yang, X., Kandula, S., & Zhang, M. (2010) · ACM Internet Measurement Conference (IMC) · doi:10.1145/1879141.1879143 · Cited by 885 · see citing works
Agile application-aware adaptation for mobility
Noble, B. D., Satyanarayanan, M., Narayanan, D., Tilton, J. E., Flinn, J., & Walker, K. R. (1997) · ACM SIGOPS Symposium on Operating Systems Principles (SOSP) · doi:10.1145/268998.266708 · Cited by 740 · see citing works
Self-citationEnergy-aware adaptation for mobile applications
Flinn, J., & Satyanarayanan, M. (1999) · doi:10.1145/319151.319155 · Cited by 596 · see citing works
Self-citationCOMET: code offload by migrating execution transparently
Gordon, M. S., Jamshidi, D. A., Mahlke, S. A., Mao, Z. M., & Xu, C. (2012) · doi:10.5555/2387880.2387890 · Cited by 370 · see citing works
Predictive-corrective incompressible SPH
Solenthaler, B., & Pajarola, R. (2009) · ACM Transactions on Graphics · doi:10.1145/1531326.1531346 · Cited by 362 · see citing works
DRX mechanism for power saving in LTE
Bontu, C. S., & Illidge, E. (2009) · IEEE Communications Magazine · doi:10.1109/mcom.2009.5116800 · Cited by 261 · see citing works
A Brief History of Cloud Offload
Satyanarayanan, M. (2015) · GetMobile Mobile Computing and Communications · doi:10.1145/2721914.2721921 · Cited by 106 · see citing works
Self-citationFlinn, J. (2012) · Synthesis lectures on mobile and pervasive computing · doi:10.2200/s00447ed1v01y201209mpc010 · Cited by 98 · see citing works
The Impact of Mobile Multimedia Applications on Data Center Consolidation
Ha, K., Pillai, P. S., Lewis, G. A., Simanta, S., Clinch, S., Davies, N., & Satyanarayanan, M. (2013) · IEEE International Conference on Cloud Engineering · doi:10.1109/ic2e.2013.17 · Cited by 96 · see citing works
Self-citationAccelerating Mobile Applications through Flip-Flop Replication
Gordon, M. S., Hong, K., Chen, P. M., Flinn, J., Mahlke, S. A., & Mao, Z. M. (2015) · International Conference on Mobile Systems, Applications, and Services · doi:10.1145/2742647.2742649 · Cited by 63 · see citing works
Early Implementation Experience with Wearable Cognitive Assistance Applications
Chen, Z., Jiang, L., Hu, W., Ha, K., Amos, B., Pillai, P. S., Hauptmann, A. G., & Satyanarayanan, M. (2015) · Workshop on Wearable Systems and Applications (WearSys) · doi:10.1145/2753509.2753517 · Cited by 50 · see citing works
Self-citation3D mobile augmented reality in urban scenes
Gabriel Takacs, A., Choubassi, M. E., Wu, Y., & Kozintsev, I. V. (2011) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2011.6012101 · Cited by 20 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- [16]16. A. Li, X. Yang, S. Kandula, and M. Zhang. Cloudcmp: comparing public cloud providers. In Proceedings of the 10th annual conference on Internet measurement, pages 1-14. ACM, 2010.
Cited by
Sorted by citations
Convergence of Edge Computing and Deep Learning: A Comprehensive Survey
Wang, X., Han, Y., Leung, V. C. M., Niyato, D., Yan, X., & Chen, X. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2970550 · Cited by 1,498 · see citing works
“, cooperative autonomous driving, have strict or tight delay requirements that the cloud would have difficulty meeting since it may be far away from the users [4].”
BackgroundA Survey on Mobile Edge Networks: Convergence of Computing, Caching and Communications
Wang, S., Zhang, X., Zhang, Y., Wang, L., YANG, J., & Wang, W. (2017) · IEEE Access · doi:10.1109/access.2017.2685434 · Cited by 952 · see citing works
Singh, R., & Gill, S. S. (2023) · Internet of Things and Cyber-Physical Systems · doi:10.1016/j.iotcps.2023.02.004 · Cited by 572 · see citing works
A Survey of Recent Advances in Edge-Computing-Powered Artificial Intelligence of Things
Chang, Z., Liu, S., Xiong, X., Cai, Z., & Tu, G. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3088875 · Cited by 431 · see citing works
Edge Computing in 5G: A Review
Hassan, N., Yau, K. A., & Wu, C. (2019) · IEEE Access · doi:10.1109/access.2019.2938534 · Cited by 399 · see citing works
Federated Learning in Edge Computing: A Systematic Survey
Abreha, H. G., Hayajneh, M., & Serhani, M. A. (2022) · Sensors · doi:10.3390/s22020450 · Cited by 366 · see citing works
Fog Computing in Healthcare–A Review and Discussion
Kraemer, F. A., Bråten, A. E., Tamkittikhun, N., & Palma, D. (2017) · IEEE Access · doi:10.1109/access.2017.2704100 · Cited by 335 · see citing works
de Assunção, M. D., Veith, A. D. S., & Buyya, R. (2017) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2017.12.001 · Cited by 303 · see citing works
LAVEA: latency-aware video analytics on edge computing platform
Yi, S., Hao, Z., Zhang, Q., Zhang, Q., Shi, W., & Li, Q. (2017) · Proceedings of the Second ACM/IEEE Symposium on Edge Computing · doi:10.1145/3132211.3134459 · Cited by 244 · see citing works
Zhang, X., Cao, Z., & Dong, W. (2020) · IEEE Access · doi:10.1109/access.2020.3013005 · Cited by 212 · see citing works
Bringing Computation Closer toward the User Network: Is Edge Computing the Solution?
Ahmed, E., Ahmed, A., Yaqoob, I., Shuja, J., Gani, A., Imran, M., & Shoaib, M. (2017) · IEEE Communications Magazine · doi:10.1109/mcom.2017.1700120 · Cited by 192 · see citing works
Edge-Oriented Computing Paradigms: A Survey on Architecture Design and System Management
Li, C., Xue, Y., Wang, J., Zhang, W., & Li, T. (2019) · ACM Computing Surveys · doi:10.1145/3154815 · Cited by 180 · see citing works
Self-citationAn execution model for serverless functions at the edge
Hall, A., & Ramachandran, U. (2019) · IEEE/ACM International Conference on Internet-of-Things Design and Implementation (IoTDI) · doi:10.1145/3302505.3310084 · Cited by 153 · see citing works
DeepWear: Adaptive Local Offloading for On-Wearable Deep Learning
Xu, M., Qian, F., Zhu, M., Huang, F., Pushp, S., & Liu, X. (2020) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2019.2893250 · Cited by 138 · see citing works
Post-cloud computing paradigms: a survey and comparison
Zhou, Y., Zhang, D., & Xiong, N. N. (2017) · Tsinghua Science & Technology · doi:10.23919/tst.2017.8195353 · Cited by 137 · see citing works
A Review on Computational Intelligence Techniques in Cloud and Edge Computing
Asim, M., Wang, Y., Wang, K., & Huang, P.-Q. (2020) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2020.3007905 · Cited by 141 · see citing works
DeepEdgeBench: Benchmarking deep neural networks on edge devices
Baller, S. P., Jindal, A., Chadha, M., & Gerndt, M. (2021) · 2021 IEEE International Conference on Cloud Engineering (IC2E) · doi:10.1109/ic2e52221.2021.00016 · Cited by 134 · see citing works
An Approach to QoS-based Task Distribution in Edge Computing Networks for IoT Applications
Song, Y., Yau, S. S., Yu, R., Zhang, X., & Xue, G. (2017) · IEEE International Conference on Edge Computing and Communications (EDGE) · doi:10.1109/ieee.edge.2017.50 · Cited by 124 · see citing works
Performance evaluation of a Fog-assisted IoT solution for e-Health applications
Vilela, P. H., Rodrigues, J. J. P. C., Šolić, P., Saleem, K., & Furtado, V. (2019) · Future Generation Computer Systems · doi:10.1016/j.future.2019.02.055 · Cited by 124 · see citing works
Distributing Computing in the Internet of Things: Cloud, Fog and Edge Computing Overview
Escamilla-Ambrosio, P. J., Rodríguez-Mota, A., Anaya, E. A., Acosta-Bermejo, R., & Salinas-Rosales, M. (2017) · Studies in computational intelligence · doi:10.1007/978-3-319-64063-1_4 · Cited by 119 · see citing works
Differential Privacy Preserving of Training Model in Wireless Big Data with Edge Computing
Du, M., Wang, K., Xia, Z., & Zhang, Y. (2020) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2018.2829886 · Cited by 111 · see citing works
Taherizadeh, S., Stankovski, V., & Grobelnik, M. (2018) · Sensors · doi:10.3390/s18092938 · Cited by 102 · see citing works
Liang, W., Pei, H., Cai, Q., & Wang, Y. (2020) · Neurocomputing · doi:10.1016/j.neucom.2018.10.108 · Cited by 100 · see citing works
Cost-Effective Edge Server Placement in Wireless Metropolitan Area Networks
Zeng, F., Ren, Y., Deng, X., & Li, W. (2018) · Sensors · doi:10.3390/s19010032 · Cited by 99 · see citing works
Collaborate or Separate? Distributed Service Caching in Mobile Edge Clouds
Xu, Z., Zhou, L., Chau, C.-K., Liang, W., Xia, Q., & Zhou, P. (2020) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom41043.2020.9155365 · Cited by 100 · see citing works
Edge and Fog Computing Enabled AI for IoT-An Overview
Zou, Z., Jin, Y., Nevalainen, P., Huan, Y., Heikkonen, J., & Westerlund, T. (2019) · IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS) · doi:10.1109/aicas.2019.8771621 · Cited by 97 · see citing works
FoggyCache: Cross-Device Approximate Computation Reuse
Guo, P., Hu, B., Li, R., & Hu, W. (2018) · Proceedings of the 24th Annual International Conference on Mobile Computing and Networking · doi:10.1145/3241539.3241557 · Cited by 91 · see citing works
Self-citationOpportunities to Apply Precision Livestock Management on Rangelands
Bailey, D. W., Trotter, M. G., Tobin, C. T., & Thomas, M. G. (2021) · Frontiers in Sustainable Food Systems · doi:10.3389/fsufs.2021.611915 · Cited by 91 · see citing works
Mortazavi, S. H., Salehe, M., Gomes, C. S., Phillips, C., & de Lara, E. (2017) · ACM/IEEE Symposium on Edge Computing (SEC) · doi:10.1145/3132211.3134464 · Cited by 89 · see citing works
Li, Y., Orgerie, A., Ivan Rodero, A., Amersho, B. L., Parashar, M., & Menaud, J.-M. (2017) · Future Generation Computer Systems · doi:10.1016/j.future.2017.12.048 · Cited by 80 · see citing works
The Case for FPGA-Based Edge Computing
Xu, C., Jiang, S., Luo, G., Sun, G., An, N., Huang, G., & Liu, X. (2020) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2020.3041781 · Cited by 81 · see citing works
Adaptive Fog Configuration for the Industrial Internet of Things
Chen, L., Zhou, P., Gao, L., & Xu, J. (2018) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2846549 · Cited by 77 · see citing works
Ma, H., Huang, P., Zhou, Z., Zhang, X., & Chen, X. (2022) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2022.3147027 · Cited by 71 · see citing works
Cloud Versus Edge Deployment Strategies of Real-Time Face Recognition Inference
Koubaa, A., Ammar, A., Kanhouch, A., & AlHabashi, Y. (2022) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2021.3055835 · Cited by 69 · see citing works
What the Fog? Edge Computing Revisited: Promises, Applications and Future Challenges
Gedeon, J., Brandherm, F., Egert, R., Grube, T., & Mühlhäuser, M. (2019) · IEEE Access · doi:10.1109/access.2019.2948399 · Cited by 68 · see citing works
Towards Mission-Critical Control at the Edge and Over 5G
Skarin, P., Tärneberg, W., Årzén, K.-E., & Kihl, M. (2018) · IEEE International Conference on Edge Computing and Communications (EDGE) · doi:10.1109/edge.2018.00014 · Cited by 65 · see citing works
Communication-efficient distributed AI strategies for the IoT edge
Mwase, C., Jin, Y., Westerlund, T., Tenhunen, H., & Zou, Z. (2022) · Future Generation Computer Systems · doi:10.1016/j.future.2022.01.013 · Cited by 64 · see citing works
Zou, X., Liu, W., Huo, Z., Wang, S., Chen, Z., Xin, C., Bai, Y., Liang, Z., Gong, Y., Qian, Y., et al. (2023) · Sensors · doi:10.3390/s23052528 · Cited by 64 · see citing works
Self-citationSupporting Multi-Provider Serverless Computing on the Edge
Aske, A., & Zhao, X. (2018) · International Conference on Parallel Processing Workshops (ICPPW) · doi:10.1145/3229710.3229742 · Cited by 52 · see citing works
Ali-Eldin, A., Wang, B., & Shenoy, P. J. (2021) · International Conference for High Performance Computing, Networking, Storage and Analysis (SC) · doi:10.1145/3458817.3476142 · Cited by 58 · see citing works
The Seminal Role of Edge-Native Applications
Satyanarayanan, M., Klas, G., Silva, M., & Mangiante, S. (2019) · IEEE International Conference on Edge Computing and Communications (EDGE) · doi:10.1109/edge.2019.00022 · Cited by 52 · see citing works
Self-citationCaiazza, C., Giordano, S., Luconi, V., & Vecchio, A. (2022) · Computer Communications · doi:10.1016/j.comcom.2022.07.026 · Cited by 47 · see citing works
Addressing Application Latency Requirements through Edge Scheduling
Aral, A., Brandić, I., Uriarte, R. B., Nicola, R. D., & Scoca, V. (2019) · Journal of Grid Computing · doi:10.1007/s10723-019-09493-z · Cited by 45 · see citing works
Latency-Aware Placement of Data Stream Analytics on Edge Computing
Veith, A. D. S., de Assunção, M. D., & Lefèvre, L. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-03596-9_14 · Cited by 44 · see citing works
Looking at Fog Computing for E-Health through the Lens of Deployment Challenges and Applications
Vilela, P. H., Rodrigues, J. J. P. C., Righi, R. D. R., Kozlov, S. A., & Rodrigues, V. F. (2020) · Sensors · doi:10.3390/s20092553 · Cited by 43 · see citing works
Accelerating Mobile Applications at the Network Edge with Software-Programmable FPGAs
Jiang, S., He, D., Yang, C., Xu, C., Luo, G., Chen, Y., Liu, Y., & Jiang, J. (2018) · IEEE INFOCOM 2018 - IEEE Conference on Computer Communications · doi:10.1109/infocom.2018.8485850 · Cited by 40 · see citing works
QoS-oriented capacity planning for edge computing
Noreikis, M., Xiao, Y., & Ylä-Jääski, A. (2017) · International Conference on Communications · doi:10.1109/icc.2017.7997387 · Cited by 41 · see citing works
Augmenting Cognition Through Edge Computing
Satyanarayanan, M., & Davies, N. (2019) · Computer · doi:10.1109/mc.2019.2911878 · Cited by 40 · see citing works
Self-citationAn Overview of Fog Data Analytics for IoT Applications
Bhatia, J., Italiya, K., Jadeja, K., Kumhar, M., Chauhan, U., Tanwar, S., Bhavsar, M. D., Sharma, R., Manea, D. L., Verdeș, M., et al. (2022) · Sensors · doi:10.3390/s23010199 · Cited by 40 · see citing works
A Survey of Fog Computing and Communication: Current Researches and Future DirectionsPreprint
Nath, S. B., Gupta, H., Chakraborty, S., & Ghosh, S. K. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1804.04365 · Cited by 39 · see citing works
Multi-access Edge Computing: A Survey
Tanaka, H., Yoshida, M., Mori, K., & Takahashi, N. (2018) · Journal of Information Processing · doi:10.2197/ipsjjip.26.87 · Cited by 36 · see citing works
Battle of Microservices: Towards Latency-Optimal Heuristic Scheduling for Edge Computing
Samanta, A., Li, Y., & Esposito, F. (2019) · IEEE International Conference on Network Softwarization (NetSoft) · doi:10.1109/netsoft.2019.8806674 · Cited by 37 · see citing works
Home Edge Computing (HEC): Design of a New Edge Computing Technology for Achieving Ultra-Low Latency
Babou, C. S. M., Fall, D., Kashihara, S., Niang, I., & Kadobayashi, Y. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-94340-4_1 · Cited by 35 · see citing works
TEMPOS: QoS Management Middleware for Edge Cloud Computing FaaS in the Internet of Things
Garbugli, A., Sabbioni, A., Antonio Corradi, A., & Bellavista, P. (2022) · IEEE Access · doi:10.1109/access.2022.3173434 · Cited by 33 · see citing works
Federated Edge Computing for Disaster Management in Remote Smart Oil Fields
Hussain, R. F., Salehi, M. A., Коваленко, А., Feng, Y., & Semiari, O. (2019) · IEEE International Conference on High Performance Computing and Communications; IEEE International Conference on Smart City; IEEE International Conference on Data Science and Systems (HPCC/SmartCity/DSS) · doi:10.1109/hpcc/smartcity/dss.2019.00134 · Cited by 32 · see citing works
Zhang, X., He, F., Chen, Q., Jiang, X., Bao, J., Ren, T., & Du, X. (2022) · Neural Computing and Applications · doi:10.1007/s00521-021-06815-9 · Cited by 35 · see citing works
A migration-enhanced edge computing support for mobile devices in hostile environments
Bellavista, P., Zanni, A., & Solimando, M. (2017) · International Wireless Communications and Mobile Computing Conference (IWCMC) · doi:10.1109/iwcmc.2017.7986415 · Cited by 32 · see citing works
A Computing Offloading Game for Mobile Devices and Edge Cloud Servers
Li, M., Wu, Q., Zhu, J., Zheng, R., & Zhang, M. (2018) · Wireless Communications and Mobile Computing · doi:10.1155/2018/2179316 · Cited by 30 · see citing works
Near-Optimal and Collaborative Service Caching in Mobile Edge Clouds
Xu, Z., Zhou, L., Chau, C.-K., Liang, W., Dai, H., Chen, L., Xu, W., Xia, Q., & Zhou, P. (2022) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2022.3144175 · Cited by 30 · see citing works
Towards Fully Offloaded Cloud-based AR
Shea, R., Sun, A., Fu, S. D., & Liu, J. (2017) · ACM Multimedia Systems Conference (MMSys) · doi:10.1145/3083187.3084012 · Cited by 28 · see citing works
Distributed Operator Placement for IoT Data Analytics Across Edge and Cloud Resources
Renart, E. G., Veith, A. D. S., Balouek‐Thomert, D., de Assunção, M. D., Lefèvre, L., & Parashar, M. (2019) · IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid) · doi:10.1109/ccgrid.2019.00060 · Cited by 27 · see citing works
Collaborative execution of distributed mobile and IoT applications running at the edge
Shurman, M. M., & Aljarah, M. K. (2017) · International Conference on Electrical and Computing Technologies and Applications (ICECTA) · doi:10.1109/icecta.2017.8252057 · Cited by 25 · see citing works
Cloud, Edge, and Mobile Computing for Smart Cities
Liu, Q., Gu, J., Yang, J., Li, Y., Sha, D., Xu, M., Shams, I., Yu, M., & Yang, C. P. (2021) · The urban book series · doi:10.1007/978-981-15-8983-6_41 · Cited by 29 · see citing works
Wang, N., Shen, G., Bose, S. K., & Shao, W. (2018) · IEEE Access · doi:10.1109/access.2018.2888602 · Cited by 24 · see citing works
Rendering multi-party mobile augmented reality from edge
Zhang, L., Sun, A., Shea, R., Liu, J., & Zhang, M. (2019) · International Workshop on Network and Operating Systems Support for Digital Audio and Video (NOSSDAV) · doi:10.1145/3304112.3325612 · Cited by 24 · see citing works
Li, X., Zeng, F., Fang, G., Huang, Y., & Tao, X. (2020) · IET Communications · doi:10.1049/iet-com.2020.0651 · Cited by 23 · see citing works
Veith, A. D. S., de Assunção, M. D., & Lefèvre, L. (2021) · IEEE Transactions on Cloud Computing · doi:10.1109/tcc.2021.3097879 · Cited by 22 · see citing works
Spatially-Temporally Collaborative Service Placement and Task Scheduling in MEC Networks
Feng, C., Yang, Q., Quek, T. Q. S., Wu, W., & Guo, K. (2023) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2023.3297169 · Cited by 22 · see citing works
Leveraging Renewable Energy in Edge Clouds for Data Stream Analysis in IoT
Li, Y., Orgerie, A., Ivan Rodero, A., Parashar, M., & Menaud, J.-M. (2017) · IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid) · doi:10.1109/ccgrid.2017.92 · Cited by 22 · see citing works
Quality of Service Channelling for Latency Sensitive Edge Applications
Aral, A., & Brandić, I. (2017) · IEEE International Conference on Edge Computing and Communications (EDGE) · doi:10.1109/ieee.edge.2017.30 · Cited by 21 · see citing works
Stable Service Caching in MECs of Hierarchical Service Markets With Uncertain Request Rates
Xu, Z., Xia, Q., Wang, L., Zhou, P., Lui, J. C. S., Liang, W., Xu, W., & Wu, G. (2022) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2022.3149870 · Cited by 21 · see citing works
Replica selection and placement techniques on the IoT and edge computing: a deep study
Shao, Z.-L., Huang, C., & Li, H. (2021) · Wireless Networks · doi:10.1007/s11276-021-02793-x · Cited by 20 · see citing works
The Role of Edge Offload for Hardware-Accelerated Mobile Devices
Satyanarayanan, M., Beckmann, N., Lewis, G. A., & Lucia, B. (2021) · IEEE Workshop on Mobile Computing Systems and Applications (WMCSA) · doi:10.1145/3446382.3448360 · Cited by 20 · see citing works
Self-citationWhy cloud applications are not ready for the edge (yet)
Nguyen, C. L. T., Mehta, A., Klein, C., & Elmroth, E. (2019) · ACM/IEEE Symposium on Edge Computing (SEC) · doi:10.1145/3318216.3363298 · Cited by 18 · see citing works
Advancing Edge Computing with Federated Deep Learning: Strategies and Challenges
Nadella, G. S. (2024) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2024.60602 · Cited by 20 · see citing works
de Souza, F. R., de Assunção, M. D., Caron, E., & Veith, A. D. S. (2020) · International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD) · doi:10.1109/sbac-pad49847.2020.00019 · Cited by 17 · see citing works
To Cache or Not to Cache: Stable Service Caching in Mobile Edge-Clouds of a Service Market
Xu, Z., Qin, Y., Zhou, P., Lui, J. C. S., Liang, W., Xia, Q., Xu, W., & Wu, G. (2020) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs47774.2020.00051 · Cited by 17 · see citing works
Energy consumption of smartphones and IoT devices when using different versions of the HTTP protocol
Caiazza, C., Luconi, V., & Vecchio, A. (2023) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2023.101871 · Cited by 16 · see citing works
Edge computing with peer to peer interactions
Karagiannis, V., Venito, A., Coelho, R., Borkowski, M., & Fohler, G. (2019) · doi:10.1145/3313150.3313226 · Cited by 16 · see citing works
Kim, B., Lim, K., Cho, S.-J., & Park, M. (2019) · IEEE Access · doi:10.1109/access.2019.2920314 · Cited by 16 · see citing works
Tango of edge and cloud execution for reliability
Suryavansh, S., Bothra, C., Chiang, M., Peng, C., & Bagchi, S. (2019) · doi:10.1145/3366614.3368103 · Cited by 16 · see citing works
Multi-Objective Reinforcement Learning for Reconfiguring Data Stream Analytics on Edge Computing
Veith, A. D. S., de Souza, F. R., de Assunção, M. D., Lefèvre, L., & dos Anjos, J. C. S. (2019) · Proceedings of the International Conference on Parallel Processing · doi:10.1145/3337821.3337894 · Cited by 15 · see citing works
Xu, R., Park, C., Khan, S., Jin, W., Moe, S. J. S., & Kim, D. H. (2023) · IEEE Access · doi:10.1109/access.2023.3325475 · Cited by 14 · see citing works
Video Streaming Schemes for Industrial IoT
Kanzaki, H., Schubert, K., & Bambos, N. (2017) · International Conference on Computer Communications and Networks (ICCCN) · doi:10.1109/icccn.2017.8038533 · Cited by 14 · see citing works
Adaptive offloading with MPTCP for unmanned aerial vehicle surveillance system
Jung, W.-S., Yim, J., & Ko, Y. (2018) · Annals of Telecommunications · doi:10.1007/s12243-018-0660-5 · Cited by 13 · see citing works
de Souza, F. R., Veith, A. D. S., de Assunção, M. D., & Caron, E. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-65310-1_12 · Cited by 13 · see citing works
Next generation edge computing: A roadmap to net zero emissions
Singh, R., & Gill, S. S. (2023) · Journal of Economy and Technology · doi:10.1016/j.ject.2023.12.001 · Cited by 13 · see citing works
Truong, H., Narendra, N. C., & Lin, K.-J. (2018) · Service Oriented Computing and Applications · doi:10.1007/s11761-018-0228-2 · Cited by 13 · see citing works
Inferring regional access network topologies
Zhang, Z., Marder, A., Mok, R. K. P., Huffaker, B., Luckie, M. J., claffy, K., & Schulman, A. (2021) · ACM Internet Measurement Conference (IMC) · doi:10.1145/3487552.3487812 · Cited by 12 · see citing works
Aljarah, M. K., Shurman, M. M., & Alnabelsi, S. H. (2019) · International Journal of Electrical and Computer Engineering (IJECE) · doi:10.11591/ijece.v10i1.pp296-307 · Cited by 12 · see citing works
The advantage of computation offloading in multi-access edge computing
Singh, R., Armour, S., Khan, A., Sooriyabandara, M., & Oikonomou, G. (2019) · 2019 Fourth International Conference on Fog and Mobile Edge Computing (FMEC) · doi:10.1109/fmec.2019.8795335 · Cited by 11 · see citing works
Service Reliability Based on Fault Prediction and Container Migration in Edge Computing
Liu, L., Kang, L., Li, X., & Zhou, Z. (2023) · Applied Sciences · doi:10.3390/app132312865 · Cited by 11 · see citing works
A Multi-Cloudlet Infrastructure for Future Smart Cities
Gedeon, J., Krisztinkovics, J., Meurisch, C., Stein, M., Wang, L., & Mühlhäuser, M. (2018) · International Workshop on Edge Systems, Analytics and Networking (EdgeSys) · doi:10.1145/3213344.3213348 · Cited by 11 · see citing works
Task Scheduling With Multicore Edge Computing in Dense Small Cell Networks
Lai, W. K., Shieh, C., & Chen, Y.-P. (2021) · IEEE Access · doi:10.1109/access.2021.3119874 · Cited by 11 · see citing works
Silva, J. A., Cerqueira, F., Paulino, H., Lourenço, J., Leitao, J. C. A., & Preguiça, N. (2020) · Future Generation Computer Systems · doi:10.1016/j.future.2020.12.008 · Cited by 11 · see citing works
Edge Capacity Planning for Real Time Compute-Intensive Applications
Noreikis, M., Xiao, Y., & Jiang, Y. (2019) · IEEE International Conference on Fog Computing (ICFC) · doi:10.1109/icfc.2019.00029 · Cited by 11 · see citing works
Investigating and Modelling of Task Offloading Latency in Edge-Cloud Environment
Almutairi, J., & Aldossary, M. (2021) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2021.018145 · Cited by 10 · see citing works
Peng, Q., Xia, Y., Wang, Y., Wu, C., Luo, X., & Lee, J. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-33702-5_36 · Cited by 10 · see citing works
Veith, A. D. S., de Assunção, M. D., & Lefèvre, L. (2019) · International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD) · doi:10.1109/sbac-pad.2019.00021 · Cited by 9 · see citing works
Quality of Service Impact on Edge Physics Simulations for VR
Friston, S., Griffith, E. J., Swapp, D., Lrondi, C., Jjunju, F. P. M., Ward, R. J., Marshall, A., & Steed, A. J. (2021) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2021.3067757 · Cited by 9 · see citing works
- Authors
- Hu, W., Gao, Y., Ha, K., Wang, J., Amos, B., Chen, Z., Pillai, P., & Satyanarayanan, M.
- Affiliations
- Carnegie Mellon University (US)
- Intel (United States) (US)
- Venue
- Proceedings of the 7th ACM SIGOPS Asia-Pacific Workshop on Systems
- Publisher
- ACM
- Published
- 2016-08-04
- Pages
- 1-8
- OpenAlex
- W2508388616
- Cited by
- 171
- References
- 18
- Citations by year
- 2026: 2 · 2025: 6 · 2024: 8 · 2023: 13 · 2022: 20 · 2021: 23 · 2020: 24 · 2019: 30 · 2018: 25 · 2017: 20
- FWCI
- 21.36
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- ACM SIGOPS Asia-Pacific Workshop on Systems (APSys) (publishedVersion)
- Funders
- National Science Foundation (CNS-1518865)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Information Systems › Mobile and Web Applications
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Green IT and Sustainability
- Keywords
- edge computing, computation offloading, mobile application, latency, energy consumption, cloud computing, Wi-Fi, 4G LTE, mobile devices
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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