The DOI matches its Crossref record; 34 of the 35 references it cites were found in a registry or an index.
MAUI: making smartphones last longer with code offload
Abstract
This paper presents MAUI, a system that enables fine-grained energy-aware offload of mobile code to the infrastructure. Previous approaches to these problems either relied heavily on programmer support to partition an application, or they were coarse-grained requiring full process (or full VM) migration. MAUI uses the benefits of a managed code environment to offer the best of both worlds: it supports fine-grained code offload to maximize energy savings with minimal burden on the programmer. MAUI decides at run-time which methods should be remotely executed, driven by an optimization engine that achieves the best energy savings possible under the mobile device's current connectivity constrains. In our evaluation, we show that MAUI enables: 1) a resource-intensive face recognition application that consumes an order of magnitude less energy, 2) a latency-sensitive arcade game application that doubles its refresh rate, and 3) a voice-based language translation application that bypasses the limitations of the smartphone environment by executing unsupported components remotely.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“The Cloud servers [61] might support massive storage required for handling decentralized distributed ledger for health data.”
From Blockchain leveraged decentralized IoT ehealth framework (2020) · cites this paper as [61]
Citations
Cites
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
Self-citationLive migration of virtual machines
Clark, C. J., Fraser, K. A., Hand, S., Hansen, J. G., Jul, E., Limpach, C., Pratt, I., & Warfield, A. (2005) · doi:10.5555/1251203.1251223 · Cited by 2,713
Recent advances in rechargeable battery materials: a chemist’s perspective
Palacín, M. R. (2009) · Chemical Society Reviews · doi:10.1039/b820555h · Cited by 1,421
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
Bandwidth estimation: metrics, measurement techniques, and tools
Prosad, R., Davrolis, C., Murray, M., & claffy, K. (2003) · IEEE Network · doi:10.1109/mnet.2003.1248658 · Cited by 729
Fine-grained mobility in the Emerald system
Jul, E., Levy, H. M., Hutchinson, N. C., & Black, A. P. (1988) · ACM Transactions on Computer Systems · doi:10.1145/35037.42182 · Cited by 728
Transparent process migration: Design alternatives and the Sprite implementation
Ousterhout, J. K., & Douglis, F. (n.d.) · NASA Technical Reports Server (NASA) · Cited by 446
Augmented smartphone applications through clone cloud execution
Chun, B.-G., & Maniatis, P. (2009) · Cited by 422
The design and implementation of Zap
Osman, S., Subhraveti, D., Su, G., & Nieh, J. (2002) · ACM SIGOPS Operating Systems Review · doi:10.1145/844128.844162 · Cited by 407
Amoeba: a distributed operating system for the 1990s
Mullender, S. J., van Rossum, G., Tananbaum, A. S., van Renesse, R., & van Staveren, H. (1990) · Computer · doi:10.1109/2.53354 · Cited by 405
Research works printed but not linked: 10
- 10.1002/spe.4380210802 · DOI
- 10.5555/874075.876398 · DOI
- 10.5555/850928.851899 · DOI
- 10.5555/826025.826367 · DOI
- P. Kougiouris . Use Reflection to Discover and Assess the Most Common Types in the .NET Framework . http://msdn.microsoft.com/en-us/magazine/cc188926.aspx#S3 , 2002 . P. Kougiouris. Use Reflection to Discover and Assess the Most Common Types in the .NET Framework. http://msdn.microsoft.com/en-us/magazine/cc188926.aspx#S3, 2002.
- U. Kremer , J. Hicks , and J. M. Rehg . Compiler-Directed Remote Task Execution for Power Management . In Proceedings of The Workshop on Compilers and Operating Systems for Low Power (COLP) , Philadelphia, PA , October 2000 . U. Kremer, J. Hicks, and J. M. Rehg. Compiler-Directed Remote Task Execution for Power Management. In Proceedings of The Workshop on Compilers and Operating Systems for Low Power (COLP), Philadelphia, PA, October 2000.
- J. Lewin . i Phone Users 30 Times More Likely To Watch YouTube Videos . http://www.podcastingnews.com/2008/03/19/iphone-users-30-times-watch-youtube-videos/ , 2008 . J. Lewin. iPhone Users 30 Times More Likely To Watch YouTube Videos. http://www.podcastingnews.com/2008/03/19/iphone-users-30-times-watch-youtube-videos/, 2008.
- 10.5555/1060289.1060323 · DOI
- J. Richter . CLR via C#. Microsoft Press ; 2 nd edition, 2006 . J. Richter. CLR via C#. Microsoft Press; 2nd edition, 2006.
- 10.5555/874075.876396 · DOI
Cited by
Sorted by citations
Items 101–200 of 1,004
Mobile cloud computing for computation offloading: Issues and challenges
Akherfi, K., Gerndt, M., & Harroud, H. (2016) · Applied Computing and Informatics · doi:10.1016/j.aci.2016.11.002 · Cited by 236
Internet of Things: an overview
Khodadadi, F., Dastjerdi, A. V., & Buyya, R. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-805395-9.00001-0 · Cited by 233
Fog Computing: A Comprehensive Architectural Survey
Habibi, P., Farhoudi, M., Kazemian, S., Khorsandi, S., & Leon‐Garcia, A. (2020) · IEEE Access · doi:10.1109/access.2020.2983253 · Cited by 233
Resource-Efficient Federated Learning with Hierarchical Aggregation in Edge Computing
Wang, Z., Xu, H., Liu, J., Huang, H., Qiao, C., & Zhao, Y. (2021) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom42981.2021.9488756 · Cited by 239
Cao, H., & Cai, J. (2017) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2017.2740724 · Cited by 226
Mobile Edge Cloud System: Architectures, Challenges, and Approaches
Liu, H., Eldarrat, F., Alqahtani, H., Reznik, A., de Foy, X., & Zhang, Y. (2017) · IEEE Systems Journal · doi:10.1109/jsyst.2017.2654119 · Cited by 217
Kim, Y., Soyata, T., & Feyzi-Behnagh, R. (2018) · IEEE Access · doi:10.1109/access.2018.2791861 · Cited by 230
Learning for Computation Offloading in Mobile Edge Computing
Dinh, T. Q., La, Q. D., Quek, T. Q. S., & Shin, H. (2018) · IEEE Transactions on Communications · doi:10.1109/tcomm.2018.2866572 · Cited by 214
ENORM: A Framework For Edge NOde Resource Management
Wang, N., Varghese, B., Matthaiou, M., & Nikolopoulos, D. S. (2017) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2017.2753775 · Cited by 213
Zhang, X., Cao, Z., & Dong, W. (2020) · IEEE Access · doi:10.1109/access.2020.3013005 · Cited by 219
Energy-Efficient Admission of Delay-Sensitive Tasks for Mobile Edge Computing
Lyu, X., Tian, H., Ni, W., Zhang, K., Zhang, P., & Liu, R. P. (2018) · IEEE Transactions on Communications · doi:10.1109/tcomm.2018.2799937 · Cited by 211
A Novel Framework for Mobile-Edge Computing by Optimizing Task Offloading
Naouri, A., Wu, H., Nouri, N. A., Dhelim, S., & Ning, H. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3064225 · Cited by 210
Edge Cloud Offloading Algorithms
Wang, J., Pan, J., Esposito, F., Calyam, P. P., Yang, Z., & Mohapatra, P. (2019) · ACM Computing Surveys · doi:10.1145/3284387 · Cited by 208
Chen, M.-H., Liang, B., & Dong, M. (2017) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom.2017.8057150 · Cited by 206
Mobile cloud computing: Challenges and future research directions
Noor, T. H., Zeadally, S., Alfazi, A., & Sheng, Q. Z. (2018) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2018.04.018 · Cited by 206
A game-theoretic approach to computation offloading in mobile cloud computing
Cardellini, V., Personé, V. D. N., Valerio, V. D., Facchinei, F., Grassi, V., Presti, F. L., & Piccialli, V. (2015) · Mathematical Programming · doi:10.1007/s10107-015-0881-6 · Cited by 203
Cloud-Based Malware Detection Game for Mobile Devices with Offloading
Xiao, L., Li, Y., Huang, X., & Du, X. (2017) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2017.2687918 · Cited by 201
EC-SAGINs: Edge-Computing-Enhanced Space–Air–Ground-Integrated Networks for Internet of Vehicles
Yu, S., Gong, X., Shi, Q., Wang, X., & Chen, X. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3052542 · Cited by 201
Evaluating the Effectiveness of Model-based Power Characterization
Mccullough, J. C., Agarwal, Y., Chandrashekar, J., Snoeren, A. C., & Gupta, R. K. (2011) · Cited by 199
Towards the Decentralised Cloud
Ferrer, A. J., Marquès, J. M., & Jorba, J. (2019) · ACM Computing Surveys · doi:10.1145/3243929 · Cited by 199
Energy Management Techniques in Modern Mobile Handsets
Vallina-Rodríguez, N., & Crowcroft, J. (2012) · IEEE Communications Surveys & Tutorials · doi:10.1109/surv.2012.021312.00045 · Cited by 198
Energy-Aware Marine Predators Algorithm for Task Scheduling in IoT-Based Fog Computing Applications
Abdel‐Basset, M., Mohamed, R., Elhoseny, M., Bashir, A. K., Jolfaei, A., & Kumar, N. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.3001067 · Cited by 198
Rachuri, K. K., Cecilia Mascolo, C., Musolesi, M., & Rentfrow, P. J. (2011) · ACM Annual International Conference on Mobile Computing and Networking (MobiCom) · doi:10.1145/2030613.2030623 · Cited by 197
A Survey of Mobile Cloud Computing Applications: Perspectives and Challenges
Wang, Y., Chen, I.-R., & Wang, D.-C. (2014) · Wireless Personal Communications · doi:10.1007/s11277-014-2102-7 · Cited by 195
Satyanarayanan, M. (2010) · doi:10.1145/1810931.1810936 · Cited by 195
Constandache, I., Bao, X., Azizyan, M., & Choudhury, R. R. (2010) · ACM Annual International Conference on Mobile Computing and Networking (MobiCom) · doi:10.1145/1859995.1860013 · Cited by 194
Byers, C. C. (2017) · IEEE Communications Magazine · doi:10.1109/mcom.2017.1600885 · Cited by 194
A Framework for Cooperative Resource Management in Mobile Cloud Computing
Kaewpuang, R., Niyato, D. T., Wang, P., & Hossain, E. (2013) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2013.131209 · Cited by 193
The Fog Balancing: Load Distribution for Small Cell Cloud Computing
Oueis, J., Strinati, E. C., & Barbarossa, S. (2015) · IEEE Vehicular Technology Conference · doi:10.1109/vtcspring.2015.7146129 · Cited by 190
Fog Computing: An Overview of Big IoT Data Analytics
Anwar, M. R., Wang, S., Zia, M. A., Jadoon, A. K., Akram, U., & Raza, S. (2018) · Wireless Communications and Mobile Computing · doi:10.1155/2018/7157192 · Cited by 189
Thiagarajan, N., Aggarwal, G., Nicoarǎ, A., Boneh, D., & Singh, J. (2012) · International World Wide Web Conference (WWW) · doi:10.1145/2187836.2187843 · Cited by 188
Neurosurgeon: Collaborative Intelligence Between the Cloud and Mobile Edge
Kang, Y., Hauswald, J., Gao, C., Rovinski, A., Mudge, T., Mars, J., & Tang, L. (2017) · ACM SIGPLAN Notices · doi:10.1145/3093336.3037698 · Cited by 185
Enabling intelligence in fog computing to achieve energy and latency reduction
Lã, Q. D., Ngo, M. V., Dinh, T. Q., Quek, T. Q. S., & Shin, H. (2018) · Digital Communications and Networks · doi:10.1016/j.dcan.2018.10.008 · Cited by 183
Computation Offloading for Multi-Access Mobile Edge Computing in Ultra-Dense Networks
Guo, H., Liu, J., & Zhang, J. (2018) · IEEE Communications Magazine · doi:10.1109/mcom.2018.1701069 · Cited by 182
Joint offloading decision and resource allocation for multi-user multi-task mobile cloud
Chen, M.-H., Liang, B., & Dong, M. (2016) · International Conference on Communications · doi:10.1109/icc.2016.7510999 · Cited by 179
Advancing the state of mobile cloud computing
Bahl, P., Han, R. Y., Li, L. E., & Satyanarayanan, M. (2012) · Mobile Cloud Computing & Services · doi:10.1145/2307849.2307856 · Cited by 178
Self-citationOffloading Dependent Tasks with Communication Delay and Deadline Constraint
Sundar, S., & Liang, B. (2018) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom.2018.8486305 · Cited by 178
A Game Theory Based Efficient Computation Offloading in an UAV Network
Messous, M.-A., Senouci, S., Sedjelmaci, H., & Cherkaoui, S. (2019) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2019.2902318 · Cited by 178
Nath, S. (2012) · International Conference on Mobile Systems, Applications, and Services · doi:10.1145/2307636.2307640 · Cited by 177
Cloudlet load balancing in wireless metropolitan area networks
Jia, M., Liang, W., Xu, Z., & Huang, M. (2016) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom.2016.7524411 · Cited by 176
Efficient Live Migration of Edge Services Leveraging Container Layered Storage
Ma, L., Yi, S., Carter, N., & Li, Q. (2019) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2018.2871842 · Cited by 175
Efficient service handoff across edge servers via docker container migration
Ma, L., Yi, S., & Li, Q. (2017) · ACM/IEEE Symposium on Edge Computing (SEC) · doi:10.1145/3132211.3134460 · Cited by 175
Task Offloading in Fog Computing for Using Smart Ant Colony Optimization
Kishor, A., & Chakarbarty, C. (2021) · Wireless Personal Communications · doi:10.1007/s11277-021-08714-7 · Cited by 174
Energy-Efficient Decision Making for Mobile Cloud Offloading
Wu, H., Sun, Y., & Wolter, K. (2018) · IEEE Transactions on Cloud Computing · doi:10.1109/tcc.2018.2789446 · Cited by 173
Resource Allocation and Service Provisioning in Multi-Agent Cloud Robotics: A Comprehensive Survey
Afrin, M., Jin, J., Rahman, A., Rahman, A., Wan, J., & Hossain, E. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3061435 · Cited by 180
Mobile Edge Computing Empowered Fiber-Wireless Access Networks in the 5G Era
Rimal, B. P., Van, D. P., & Maier, M. (2017) · IEEE Communications Magazine · doi:10.1109/mcom.2017.1600156cm · Cited by 170
Jia, M., Cao, J., & Yang, L. (2014) · IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) · doi:10.1109/infcomw.2014.6849257 · Cited by 169
Chen, M.-H., Dong, M., & Liang, B. (2018) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2018.2815533 · Cited by 169
Multi-access edge computing: open issues, challenges and future perspectives
Shahzadi, S., Iqbal, M., Dagiuklas, T., & Qayyum, Z. U. (2017) · Journal of Cloud Computing Advances Systems and Applications · doi:10.1186/s13677-017-0097-9 · Cited by 167
Energy-efficient scheduling policy for collaborative execution in mobile cloud computing
Zhang, W., Wen, Y., & Wu, D. O. (2013) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infcom.2013.6566761 · Cited by 163
Shi, C., Habak, K., Pandurangan, P., Ammar, M., Naik, M., & Zegura, E. W. (2014) · ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc) · doi:10.1145/2632951.2632958 · Cited by 163
A Survey of Research on Mobile Cloud Computing
Guan, L., Xu, K., Song, M., & Song, J. (2011) · IEEE/ACIS International Conference on Computer and Information Science (ICIS) · doi:10.1109/icis.2011.67 · Cited by 162
Incentive mechanism for computation offloading using edge computing: A Stackelberg game approach
Liu, Y., Xu, C., Zhan, Y., Liu, Z., Guan, J., & Zhang, H. (2017) · Computer Networks · doi:10.1016/j.comnet.2017.03.015 · Cited by 161
JALAD: Joint Accuracy-And Latency-Aware Deep Structure Decoupling for Edge-Cloud Execution
Li, H., Hu, C., Jiang, J., Wang, Z., Wen, Y., & Zhu, W. (2018) · 2018 IEEE 24th International Conference on Parallel and Distributed Systems (ICPADS) · doi:10.1109/padsw.2018.8645013 · Cited by 160
Arpacı, İ. (2018) · Computers in Human Behavior · doi:10.1016/j.chb.2018.09.005 · Cited by 160
Computation Offloading With Data Caching Enhancement for Mobile Edge Computing
Yu, S., Langar, R., Fu, X., Wang, L., & Han, Z. (2018) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2018.2869144 · Cited by 159
Mobile code offloading: from concept to practice and beyond
Flores, H., Hui, P., Tarkoma, S., Li, Y., Srirama, S. N., & Buyya, R. (2015) · IEEE Communications Magazine · doi:10.1109/mcom.2015.7060486 · Cited by 157
Challenges on wireless heterogeneous networks for mobile cloud computing
Lei, L., Zhangdui, Z., Zheng, K., Chen, J., & Meng, H. (2013) · IEEE Wireless Communications · doi:10.1109/mwc.2013.6549281 · Cited by 157
Liang, B. (2017) · Cambridge University Press eBooks · doi:10.1017/9781316771655.005 · Cited by 157
Pejović, V., & Musolesi, M. (2015) · ACM Computing Surveys · doi:10.1145/2693843 · Cited by 163
Revisiting storage for smartphones
Kim, H., Agrawal, N., & Ungureanu, C. (2012) · ACM Transactions on Storage · doi:10.1145/2385603.2385607 · Cited by 156
Satyanarayanan, M. (2011) · ACM SIGMOBILE Mobile Computing and Communications Review · doi:10.1145/2016598.2016600 · Cited by 155
ULOOF: A User Level Online Offloading Framework for Mobile Edge Computing
Neto, J. L. D., Yu, S.-Y., Macedo, D. F., Nogueira, J. M. S., Langar, R., & Secci, S. (2018) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2018.2815015 · Cited by 155
Progressive authentication: deciding when to authenticate on mobile phones
Riva, O., Qin, C., Strauß, K., & Lymberopoulos, D. (2012) · Cited by 154
Lai, Z., Hu, Y., Cui, Y., Sun, L., & Dai, N. (2017) · ACM Annual International Conference on Mobile Computing and Networking (MobiCom) · doi:10.1145/3117811.3117815 · Cited by 154
eTime: Energy-efficient transmission between cloud and mobile devices
Shu, P., Liu, F., Jin, H., Chen, M., Wen, F., Qu, Y., & Li, B. (2013) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infcom.2013.6566762 · Cited by 153
Adaptive Computation Offloading from Mobile Devices into the Cloud
Kovachev, D., Yu, T., & Klamma, R. (2012) · IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA) · doi:10.1109/ispa.2012.115 · Cited by 152
Adaptive Multi-Resource Allocation for Cloudlet-Based Mobile Cloud Computing System
Liu, Y., Lee, M. J., & Zheng, Y. (2015) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2015.2504091 · Cited by 152
MAPCloud: Mobile Applications on an Elastic and Scalable 2-Tier Cloud Architecture
Rahimi, M. R., Venkatasubramanian, N., Mehrotra, S., & Vasilakos, A. V. (2012) · IEEE/ACM International Conference on Utility and Cloud Computing (UCC) · doi:10.1109/ucc.2012.25 · Cited by 151
Bajaj, K., Sharma, B., & Singh, R. (2021) · Complex & Intelligent Systems · doi:10.1007/s40747-021-00434-6 · Cited by 151
Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges
Pejović, V., & Musolesi, M. (2013) · Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) · doi:10.48550/arxiv.1306.2356 · Cited by 151
Application optimization in mobile cloud computing: Motivation, taxonomies, and open challenges
Ahmed, E., Gani, A., Sookhak, M., Hamid, S. H. A., & Xia, F. (2015) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2015.02.003 · Cited by 149
Cost-Efficient Resource Provisioning for Dynamic Requests in Cloud Assisted Mobile Edge Computing
Ma, X., Wang, S., Zhang, S., Yang, P., Lin, C., & Shen, X. (2019) · IEEE Transactions on Cloud Computing · doi:10.1109/tcc.2019.2903240 · Cited by 149
Cachier: Edge-Caching for Recognition Applications
Drolia, U., Guo, K., Tan, J., Gandhi, R., & Narasimhan, P. (2017) · 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs.2017.94 · Cited by 148
Just-in-time provisioning for cyber foraging
Ha, K., Pillai, P. S., Richter, W., Abe, Y., & Satyanarayanan, M. (2013) · International Conference on Mobile Systems, Applications, and Services · doi:10.1145/2462456.2464451 · Cited by 148
MuSIC: Mobility-Aware Optimal Service Allocation in Mobile Cloud Computing
Rahimi, M. R., Venkatasubramanian, N., & Vasilakos, A. V. (2013) · IEEE ... International Conference on Cloud Computing · doi:10.1109/cloud.2013.100 · Cited by 147
Zhao, T., Zhou, S., Guo, X., Yun, Z., & Niu, Z. (2015) · IEEE Globecom Workshops (GC Wkshps) · doi:10.1109/glocomw.2015.7414063 · Cited by 147
Device Analyzer: Understanding Smartphone Usage
Wagner, D. T., Rice, A. C., & Beresford, A. R. (2014) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-319-11569-6_16 · Cited by 146
DNNOff: Offloading DNN-Based Intelligent IoT Applications in Mobile Edge Computing
Chen, X., Li, M., Zhong, H., Ma, Y., & Hsu, C. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3075464 · Cited by 146
Multi-User Multi-Task Offloading and Resource Allocation in Mobile Cloud Systems
Chen, M.-H., Liang, B., & Dong, M. (2018) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2018.2864559 · Cited by 145
Furion: Engineering High-Quality Immersive Virtual Reality on Today's Mobile Devices
Lai, Z., Hu, Y., Cui, Y., Sun, L., Dai, N., & Lee, H.-S. (2019) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2019.2913364 · Cited by 145
Liu, J., Ahmed, E., Shiraz, M., Gani, A., Buyya, R., & Qureshi, A. (2014) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2014.09.009 · Cited by 144
Toward Vehicle-Assisted Cloud Computing for Smartphones
Zhang, H., Zhang, Q., & Du, X. (2015) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2015.2480004 · Cited by 144
mCloud: A Context-Aware Offloading Framework for Heterogeneous Mobile Cloud
Zhou, B., Dastjerdi, A. V., Calheiros, R. N., Srirama, S. N., & Buyya, R. (2015) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2015.2511002 · Cited by 144
IONN: Incremental Offloading of Neural Network Computations from Mobile Devices to Edge Servers
Jeong, H.-J., Lee, H.-J., Shin, C. H., & Moon, S.-M. (2018) · Proceedings of the ACM Symposium on Cloud Computing · doi:10.1145/3267809.3267828 · Cited by 143
A Context Sensitive Offloading Scheme for Mobile Cloud Computing Service
Zhou, B., Dastjerdi, A. V., Calheiros, R. N., Srirama, S. N., & Buyya, R. (2015) · IEEE ... International Conference on Cloud Computing · doi:10.1109/cloud.2015.119 · Cited by 141
Shaukat, U., Ahmed, E., Anwar, Z., & Xia, F. (2016) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2015.11.009 · Cited by 140
Video Caching, Analytics, and Delivery at the Wireless Edge: A Survey and Future Directions
Jedari, B., Premsankar, G., Illahi, G. K., Francesco, M. D., Mehrabi, A., & Ylä-Jääski, A. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.3035427 · Cited by 139
A Framework for Partitioning and Execution of Data Stream Applications in Mobile Cloud Computing
Yang, L., Cao, J., Tang, S., Li, T., & Chan, A. (2012) · IEEE ... International Conference on Cloud Computing · doi:10.1109/cloud.2012.97 · Cited by 139
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
Can mobile cloudlets support mobile applications?
Li, Y., & Wang, W. (2014) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom.2014.6848036 · Cited by 138
Boos, K., Chu, D. C. Y., & Cuervo, E. (2016) · International Conference on Mobile Systems, Applications, and Services · doi:10.1145/2906388.2906418 · Cited by 137
Self-citationA Survey of Opportunistic Offloading
Xu, D., Li, Y., Chen, X., Li, J., Hui, P., Chen, S., & Crowcroft, J. (2018) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2018.2808242 · Cited by 136
Xia, F., Ding, F., Li, J., Kong, X., Yang, L. T., & Ma, J. (2013) · Information Systems Frontiers · doi:10.1007/s10796-013-9458-1 · Cited by 134
An SMDP-Based Service Model for Interdomain Resource Allocation in Mobile Cloud Networks
Liang, H., Cai, L., Huang, D., Shen, X., & Peng, D. (2012) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2012.2194748 · Cited by 134
Ding, Y., Li, K., Liu, C., & Li, K. (2021) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2021.3112604 · Cited by 133
Seamless application execution in mobile cloud computing: Motivation, taxonomy, and open challenges
Ahmed, E., Gani, A., Khan, M. K., Buyya, R., & Khan, S. U. (2015) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2015.03.001 · Cited by 131
Gong, Y., Yao, H., Wang, J., Li, M., & Guo, S. (2022) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2022.3141728 · Cited by 131
Mobile Cloud Computing: A Comparison of Application ModelsPreprint
Kovachev, D., Cao, Y., & Klamma, R. (2011) · arXiv (Cornell University) · doi:10.48550/arxiv.1107.4940 · Cited by 131
Tactical Cloudlets: Moving Cloud Computing to the Edge
Lewis, G. A., Echeverría, S., Simanta, S., Bradshaw, B., & Root, J. (2014) · IEEE Military Communications Conference (MILCOM) · doi:10.1109/milcom.2014.238 · Cited by 130
Metadata
- Authors
- Cuervo, E., Balasubramanian, A., Cho, D.-K., Wolman, A., Saroiu, S., Chandra, R., & Bahl, P.
- Affiliations
- Duke University (US)
- University of Massachusetts Amherst (US)
- University of California, Los Angeles (US)
- Microsoft (United States) (US)
- Venue
- Proceedings of the 8th international conference on Mobile systems, applications, and services
- Publisher
- ACM
- Published
- 2010-06-15
- Pages
- 49-62
- OpenAlex
- W2101788345
- Cited by
- 2,281
- References
- 35
- Citations by year
- 2026: 10 · 2025: 25 · 2024: 35 · 2023: 50 · 2022: 76 · 2021: 106 · 2020: 155 · 2019: 178 · 2018: 241 · 2017: 247 · 2016: 239 · 2015: 295 · 2014: 237 · 2013: 192 · 2012: 127
- FWCI
- 168.65
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Mobile Systems, Applications, and Services (publishedVersion)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Green IT and Sustainability
- Physical Sciences › Computer Science › Hardware and Architecture › Parallel Computing and Optimization Techniques
- Physical Sciences › Computer Science › Information Systems › Mobile and Web Applications
- Keywords
- mobile code, managed code, runtime optimization, connectivity constraints, face recognition, speech translation
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review