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 201–300 of 1,004
Distributed Optimization for Energy-Efficient Fog Computing in the Tactile Internet
Xiao, Y., & Krunz, M. (2018) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2018.2872287 · Cited by 130
Understanding Edge Computing: Engineering Evolution With Artificial Intelligence
Huh, J., & Seo, Y. (2019) · IEEE Access · doi:10.1109/access.2019.2945338 · Cited by 128
Fog data analytics: A taxonomy and process model
Kumari, A., Tanwar, S., Tyagi, S., Kumar, N., Parizi, R. M., & Choo, K. R. (2018) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2018.12.013 · Cited by 126
An Efficient Application Partitioning Algorithm in Mobile Environments
Wu, H., Knottenbelt, W. J., & Wolter, K. (2019) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2019.2891695 · Cited by 125
To offload or not to offload: An efficient code partition algorithm for mobile cloud computing
Zhang, Y., Liu, H., Jiao, L., & Fu, X. (2012) · IEEE International Conference on Cloud Networking · doi:10.1109/cloudnet.2012.6483660 · Cited by 124
Lee, Y., Ju, Y., Min, C., Kang, S., Hwang, I., & Song, J. (2012) · International Conference on Mobile Systems, Applications, and Services · doi:10.1145/2307636.2307641 · Cited by 122
Selfish Decentralized Computation Offloading for Mobile Cloud Computing in Dense Wireless Networks
Jošilo, S., & Dán, G. (2018) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2018.2829874 · Cited by 121
From the internet of things to embedded intelligence
Guo, B., Zhang, D., Yu, Z., Liang, Y., Wang, Z., & Zhou, X. (2012) · World Wide Web · doi:10.1007/s11280-012-0188-y · Cited by 120
Multi-Objective Decision-Making for Mobile Cloud Offloading: A Survey
Wu, H. (2018) · IEEE Access · doi:10.1109/access.2018.2791504 · Cited by 120
Zheng, G., Xu, C. G., Wen, M., & Zhao, X. (2022) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2022.3183577 · Cited by 120
Refactoring android Java code for on-demand computation offloading
Zhang, Y., Huang, G., Liu, X., Zhang, W., Mei, H., & Yang, S. (2012) · Conference on Object-Oriented Programming Systems, Languages, and Applications · doi:10.1145/2384616.2384634 · Cited by 119
Modeling, Profiling, and Debugging the Energy Consumption of Mobile Devices
Hoque, M. A., Siekkinen, M., Khan, K. N., Xiao, Y., & Tarkoma, S. (2015) · ACM Computing Surveys · doi:10.1145/2840723 · Cited by 117
Mobile-Edge Computing Versus Centralized Cloud Computing Over a Converged FiWi Access Network
Rimal, B. P., Van, D. P., & Maier, M. (2017) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2017.2706085 · Cited by 117
Blockchain leveraged decentralized IoT ehealth framework
Uddin, M. A., Stranieri, A., Gondal, I., & Balasubramanian, V. (2020) · Internet of Things · doi:10.1016/j.iot.2020.100159 · Cited by 116
Jain, P. K., Manweiler, J., & Choudhury, R. R. (2015) · International Conference on Mobile Systems, Applications, and Services · doi:10.1145/2742647.2742666 · Cited by 116
Computing with Nearby Mobile Devices: A Work Sharing Algorithm for Mobile Edge-Clouds
Fernando, N., Loke, S. W., & Rahayu, W. J. (2016) · IEEE Transactions on Cloud Computing · doi:10.1109/tcc.2016.2560163 · Cited by 115
Rault, T., Bouabdallah, A., Challal, Y., & Marin, F. (2016) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2016.08.003 · Cited by 115
ParaDrop: Enabling Lightweight Multi-tenancy at the Network’s Extreme Edge
Liu, P., Willis, D., & Banerjee, S. (2016) · IEEE/ACM Symposium on Edge Computing (SEC) · doi:10.1109/sec.2016.39 · Cited by 115
Mobile Cyber Physical Systems: Current Challenges and Future Networking Applications
Guo, Y., Hu, X., Hu, B., Cheng, J. S., Zhou, M., & Kwok, R. Y. K. (2017) · IEEE Access · doi:10.1109/access.2017.2782881 · Cited by 113
Computing at the mobile edge: designing elastic android applications for computation offloading
Orsini, G., Bade, D., & Lamersdorf, W. (2015) · 2015 8th IFIP Wireless and Mobile Networking Conference (WMNC) · doi:10.1109/wmnc.2015.10 · Cited by 112
Stochastic Analysis of Delayed Mobile Offloading in Heterogeneous Networks
Wu, H., & Wolter, K. (2017) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2017.2711014 · Cited by 112
Decentralized Algorithm for Randomized Task Allocation in Fog Computing Systems
Jošilo, S., & Dán, G. (2018) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2018.2880874 · Cited by 115
Balancing energy, latency and accuracy for mobile sensor data classification
Chu, D. C. Y., Lane, N. D., Lai, T. T.-T., Pang, C., Meng, X., Guo, Q., Li, F., & Zhao, F. (2011) · ACM Conference on Embedded Networked Sensor Systems (SenSys) · doi:10.1145/2070942.2070949 · Cited by 111
Raeisi-Varzaneh, M., Dakkak, O., Habbal, A., & Kim, B.-S. (2023) · IEEE Access · doi:10.1109/access.2023.3256522 · Cited by 111
Bao, X., & Choudhury, R. R. (2010) · International Conference on Mobile Systems, Applications, and Services · doi:10.1145/1814433.1814468 · Cited by 110
Help your mobile applications with fog computing
Hassan, M. A., Xiao, M., Wei, Q., & Chen, S. (2015) · IEEE International Conference on Sensing, Communication, and Networking (SECON) · doi:10.1109/seconw.2015.7328146 · Cited by 110
Towards Energy-Efficient Wireless Networking in the Big Data Era: A Survey
Cao, X., Liu, L., Cheng, Y., & Shen, X. (2017) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2017.2771534 · Cited by 112
A Survey and Taxonomy of Cyber Foraging of Mobile Devices
Sharifi, M., Kafaie, S., & Kashefi, O. (2011) · IEEE Communications Surveys & Tutorials · doi:10.1109/surv.2011.111411.00016 · Cited by 109
Miluzzo, E., Cáceres, R., & Chen, Y.-F. R. (2012) · Mobile Cloud Computing & Services · doi:10.1145/2307849.2307854 · Cited by 109
Lowering the barriers to large-scale mobile crowdsensing
Xiao, Y., Simoens, P., Pillai, P. S., Ha, K., & Satyanarayanan, M. (2013) · IEEE Workshop on Mobile Computing Systems and Applications (WMCSA) · doi:10.1145/2444776.2444789 · Cited by 108
Executing mobile applications on the cloud: Framework and issues
Hung, S., Shih, C., Shieh, J., Lee, C.-P., & Huang, Y. (2011) · Computers & Mathematics with Applications · doi:10.1016/j.camwa.2011.10.044 · Cited by 108
Optimized Provisioning of Edge Computing Resources With Heterogeneous Workload in IoT Networks
Kherraf, N., Alameddine, H. A., Sharafeddine, S., Assi, C. M., & Ghrayeb, A. (2019) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2019.2894955 · Cited by 108
Ge, Y., Zhang, Y., Qiu, Q., & Lu, Y.-H. (2012) · International Symposium on Low Power Electronics and Design (ISLPED) · doi:10.1145/2333660.2333724 · Cited by 108
The Case for Mobile Edge-Clouds
Drolia, U., Martins, R., Tan, J., Chheda, A., Sanghavi, M., Gandhi, R., & Narasimhan, P. (2013) · Autonomic and Trusted Computing · doi:10.1109/uic-atc.2013.94 · Cited by 107
Dynamic Resource Scheduling in Mobile Edge Cloud with Cloud Radio Access Network
Wang, X., Wang, K., Wu, S., Di, S., Jin, H., Yang, K., & Ou, S. (2018) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2018.2832124 · Cited by 107
A Brief History of Cloud Offload
Satyanarayanan, M. (2015) · GetMobile Mobile Computing and Communications · doi:10.1145/2721914.2721921 · Cited by 106
Network-centric performance analysis of runtime application migration in mobile cloud computing
Ahmed, E., Akhunzada, A., Whaiduzzaman, M., Gani, A., Hamid, S. H. A., & Buyya, R. (2014) · Simulation Modelling Practice and Theory · doi:10.1016/j.simpat.2014.07.001 · Cited by 106
Shafique, M., Theocharides, T., Bouganis, C.-S., Hanif, M. A., Khalid, F., Hafiz, R., & Rehman, S. (2018) · Design, Automation, and Test in Europe · doi:10.23919/date.2018.8342120 · Cited by 107
Zou, J., Hao, T., Chen, Y., & Jin, H. (2020) · IEEE Transactions on Computers · doi:10.1109/tc.2020.2987567 · Cited by 106
Mechanisms for Resource Allocation and Pricing in Mobile Edge Computing Systems
Bahreini, T., Badri, H., & Grosu, D. (2021) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2021.3099731 · Cited by 106
SAMI: Service-based arbitrated multi-tier infrastructure for Mobile Cloud Computing
Sanaei, Z., Abolfazli, S., Gani, A., & Shiraz, M. (2012) · IEEE/CIC International Conference on Communications in China - Workshops (CIC/ICCC) · doi:10.1109/icccw.2012.6316466 · Cited by 105
Energy Efficient Computational Offloading Framework for Mobile Cloud Computing
Shiraz, M., Gani, A., Shamim, A., Khan, S., & Ahmad, R. W. (2015) · Journal of Grid Computing · doi:10.1007/s10723-014-9323-6 · Cited by 104
Mobile cloud computing service models: a user-centric approach
Huang, D., Xing, T., & Wu, H. (2013) · IEEE Network · doi:10.1109/mnet.2013.6616109 · Cited by 104
GreenDroid: Automated Diagnosis of Energy Inefficiency for Smartphone Applications
Liu, Y., Xu, C., Cheung, S.-C., & Lü, J. (2014) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2014.2323982 · Cited by 103
Rich Mobile Applications: Genesis, taxonomy, and open issues
Abolfazli, S., Sanaei, Z., Gani, A., Xia, F., & Yang, L. T. (2013) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2013.09.009 · Cited by 102
Towards resource sharing in mobile device clouds
Mtibaa, A., Fahim, A., Harras, K. A., & Ammar, M. (2013) · doi:10.1145/2491266.2491276 · Cited by 102
Computation Offloading Scheduling for Periodic Tasks in Mobile Edge Computing
Jošilo, S., & Dán, G. (2020) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2020.2968209 · Cited by 102
Zeng, X., Fang, B., Shen, H., & Zhang, M. (2020) · ACM Conference on Embedded Networked Sensor Systems (SenSys) · doi:10.1145/3384419.3430721 · Cited by 102
Edge and Central Cloud Computing: A Perfect Pairing for High Energy Efficiency and Low-Latency
Hu, X., Wang, L., Wong, K., Tao, M., Zhang, Y., & Zheng, Z. (2019) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2019.2950632 · Cited by 101
Energy-Efficient Computation Offloading for Multicore-Based Mobile Devices
Geng, Y., Yang, Y. X., & Cao, G. (2018) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom.2018.8485875 · Cited by 100
Eshratifar, A. E., & Pedram, M. (2018) · ACM Great Lakes Symposium on VLSI (GLSVLSI) · doi:10.1145/3194554.3194565 · Cited by 99
eDoctor: automatically diagnosing abnormal battery drain issues on smartphones
Ma, X., Huang, P., Jin, X., Wang, P. J., Park, S., Shen, D., Zhou, Y., Saul, L. K., & Voelker, G. M. (2013) · Cited by 99
Flinn, J. (2012) · Synthesis lectures on mobile and pervasive computing · doi:10.2200/s00447ed1v01y201209mpc010 · Cited by 98
Dual-Side Optimization for Cost-Delay Tradeoff in Mobile Edge Computing
Kim, Y., Kwak, J., & Chong, S. (2017) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2017.2762423 · Cited by 98
AppATP: An Energy Conserving Adaptive Mobile-Cloud Transmission Protocol
Liu, F., Shu, P., & Lui, J. C. S. (2015) · IEEE Transactions on Computers · doi:10.1109/tc.2015.2401032 · Cited by 97
When D2D meets cloud: Hybrid mobile task offloadings in fog computing
Chen, X., & Zhang, J. (2017) · International Conference on Communications · doi:10.1109/icc.2017.7996590 · Cited by 97
Peng, H., Wen, W., Tseng, M., & Li, L.-L. (2019) · Applied Soft Computing · doi:10.1016/j.asoc.2019.04.027 · Cited by 97
Recent Advances in Collaborative Scheduling of Computing Tasks in an Edge Computing Paradigm
Chen, S., Li, Q., Zhou, M., & Abusorrah, A. M. (2021) · Sensors · doi:10.3390/s21030779 · Cited by 97
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
Cooperative load balancing scheme for edge computing resources
Beraldi, R., Mtibaa, A., & Alnuweiri, H. (2017) · International Conference on Fog and Mobile Edge Computing (FMEC) · doi:10.1109/fmec.2017.7946414 · Cited by 96
Edge-SLAM: Edge-Assisted Visual Simultaneous Localization and Mapping
Ali, A. J. B., Kouroshli, M., Semenova, S., Hashemifar, Z. S., Ko, S. Y., & Dantu, K. (2022) · ACM Transactions on Embedded Computing Systems · doi:10.1145/3561972 · Cited by 96
Energy-Efficient Fault-Tolerant Data Storage and Processing in Mobile Cloud
Chen, C.-A., Won, M., Radu Stoleru, B., & Xie, G. G. (2014) · IEEE Transactions on Cloud Computing · doi:10.1109/tcc.2014.2326169 · Cited by 95
Revisiting storage for smartphones
Kim, H., Agrawal, N., & Ungureanu, C. (2012) · File and Storage Technologies · doi:10.5555/2208461.2208478 · Cited by 95
Cloudlet Enhanced Fiber-Wireless Access Networks for Mobile-Edge Computing
Rimal, B. P., Van, D. P., & Maier, M. (2017) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2017.2685578 · Cited by 95
Enabling Low-Latency Applications in LTE-A Based Mixed Fog/Cloud Computing Systems
Du, J., Zhao, L., Chu, X., Yu, F. R., Feng, J., & Chih‐Lin, I. (2018) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2018.2882991 · Cited by 95
Energy-Efficient Task Offloading for Time-Sensitive Applications in Fog Computing
Jiang, Y.-L., Chen, Y.-S., Yang, S.-W., & Wu, C.-H. (2018) · IEEE Systems Journal · doi:10.1109/jsyst.2018.2877850 · Cited by 95
Adaptive code offloading for mobile cloud applications
Flores, H., & Srirama, S. N. (2013) · Mobile Cloud Computing & Services · doi:10.1145/2482981.2482984 · Cited by 94
Sheng, M., Wang, Y., Wang, X., & Li, J. (2019) · IEEE Transactions on Communications · doi:10.1109/tcomm.2019.2959338 · Cited by 94
Multihop Offloading of Multiple DAG Tasks in Collaborative Edge Computing
Sahni, Y., Cao, J., Yang, L., & Ji, Y. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3030926 · Cited by 94
Joint offloading decision and resource allocation for mobile cloud with computing access point
Chen, M.-H., Dong, M., & Liang, B. (2016) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2016.7472331 · Cited by 93
Learning and Management for Internet of Things: Accounting for Adaptivity and Scalability
Chen, T., Barbarossa, S., Wang, X., Giannakis, G. B., & Zhang, Z.-L. (2019) · Proceedings of the IEEE · doi:10.1109/jproc.2019.2896243 · Cited by 93
Wang, L., Zhang, D., Yan, Z., Xiong, H., & Xie, B. (2015) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2015.2418283 · Cited by 92
Internet of surveillance: a cloud supported large-scale wireless surveillance system
Alsmirat, M. A., Jararweh, Y., Obaidat, I., & Gupta, B. B. (2016) · The Journal of Supercomputing · doi:10.1007/s11227-016-1857-x · Cited by 92
Tan, Z., Yu, F. R., Li, X., Ji, H., & Leung, V. C. M. (2017) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2017.2764002 · Cited by 92
Joint Computation Offloading and Resource Allocation for MEC-Enabled IoT Systems With Imperfect CSI
Wang, J., Feng, D., Zhang, S., Liu, A., & Xia, X. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3022802 · Cited by 92
AutoFL: Enabling Heterogeneity-Aware Energy Efficient Federated Learning
Kim, Y. G., & Wu, C.-J. (2021) · IEEE/ACM International Symposium on Microarchitecture (MICRO) · doi:10.1145/3466752.3480129 · Cited by 99
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
A Survey of Performance Optimization for Mobile Applications
Hort, M., Kechagia, M., Sarro, F., & Harman, M. (2022) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2021.3071193 · Cited by 91
A Survey of Cloudlet Based Mobile Computing
Pang, Z., Sun, L., Wang, Z., Tian, E., & Yang, S. (2015) · doi:10.1109/ccbd.2015.54 · Cited by 91
Augmentation Techniques for Mobile Cloud Computing
Zhou, B., & Buyya, R. (2018) · ACM Computing Surveys · doi:10.1145/3152397 · Cited by 91
Towards Computational Offloading in Mobile Device Clouds
Mtibaa, A., Harras, K. A., & Fahim, A. (2013) · IEEE International Conference on Cloud Computing Technology and Science · doi:10.1109/cloudcom.2013.50 · Cited by 90
Energy-Aware Computation Offloading and Transmit Power Allocation in Ultradense IoT Networks
Guo, H., Zhang, J., Liu, J., & Zhang, H. (2018) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2875535 · Cited by 90
Cuervo, E., Wolman, A., Cox, L. P., Lebeck, K., Razeen, A., Saroiu, S., & Musuvathi, M. (2015) · International Conference on Mobile Systems, Applications, and Services · doi:10.1145/2742647.2742657 · Cited by 90
Self-citationAccurate and Efficient Object Tracking Based on Passive RFID
Yang, L., Cao, J., Zhu, W., & Tang, S. (2015) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2014.2381232 · Cited by 89
Energy-Efficient Task Execution for Application as a General Topology in Mobile Cloud Computing
Zhang, W., & Wen, Y. (2015) · IEEE Transactions on Cloud Computing · doi:10.1109/tcc.2015.2511727 · Cited by 89
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
Chen, K., Li, T., Kim, H., Culler, D., & Katz, R. H. (2018) · ACM Conference on Embedded Networked Sensor Systems (SenSys) · doi:10.1145/3274783.3274834 · Cited by 89
MAGA: A Mobility-Aware Computation Offloading Decision for Distributed Mobile Cloud Computing
Shi, Y., Chen, S., & Xu, X. H. (2017) · IEEE Internet of Things Journal · doi:10.1109/jiot.2017.2776252 · Cited by 89
Time-and-Energy-Aware Computation Offloading in Handheld Devices to Coprocessors and Clouds
Lin, Y., Chu, E. T.-H., Lai, Y., & Huang, T.-J. (2014) · IEEE Systems Journal · doi:10.1109/jsyst.2013.2289556 · Cited by 88
Energy Delay Tradeoff in Cloud Offloading for Multi-Core Mobile Devices
Jiang, Z., & Mao, S. (2015) · IEEE Access · doi:10.1109/access.2015.2499300 · Cited by 88
MVR: An Architecture for Computation Offloading in Mobile Edge Computing
Wei, X., Wang, S., Zhou, A., Xu, J., Su, S., Kumar, S., & Yang, F. (2017) · IEEE International Conference on Edge Computing and Communications (EDGE) · doi:10.1109/ieee.edge.2017.42 · Cited by 88
Joint offloading and computing optimization in wireless powered mobile-edge computing systems
Wang, F., Xu, J., Wang, X., & Cui, S. R. (2017) · International Conference on Communications · doi:10.1109/icc.2017.7997477 · Cited by 88
Feng, Y., Hu, B., Hao, H., Gao, Y., Li, Z., & Tan, J. (2018) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2805910 · Cited by 88
Vallina-Rodríguez, N., & Crowcroft, J. (2011) · doi:10.1145/1999916.1999926 · Cited by 87
From MANET to people-centric networking: Milestones and open research challenges
Conti, M., Boldrini, C., Kanhere, S. S., Mingozzi, E., Pagani, E., Ruiz, P. M., & Younis, M. (2015) · Computer Communications · doi:10.1016/j.comcom.2015.09.007 · Cited by 88
A game theoretic analysis of selfish mobile computation offloading
Jošilo, S., & Dán, G. (2017) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom.2017.8057148 · Cited by 87
Hermes: Latency optimal task assignment for resource-constrained mobile computing
Kao, Y.-H., Krishnamachari, B., Ra, M.-R., & Bai, F. (2015) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom.2015.7218572 · Cited by 86
Enayet, A., Razzaque, M. A., Hassan, M. M., Alamri, A. M., & Fortino, G. (2018) · IEEE Communications Magazine · doi:10.1109/mcom.2018.1700293 · Cited by 86
An Efficient and Secured Framework for Mobile Cloud Computing
Elgendy, I. A., Zhang, W., Liu, C., & Hsu, C. (2018) · IEEE Transactions on Cloud Computing · doi:10.1109/tcc.2018.2847347 · Cited by 85
ICedge: When Edge Computing Meets Information-Centric Networking
Mastorakis, S., Mtibaa, A., Lee, J., & Misra, S. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2966924 · Cited by 85
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