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 901–1,000 of 1,004
Energy and Profit-Aware Proof-of-Stake Offloading in Blockchain-based VANETs
Maio, V. D., Uriarte, R. B., & Brandić, I. (2019) · IEEE/ACM International Conference on Utility and Cloud Computing (UCC) · doi:10.1145/3344341.3368797 · Cited by 20
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
BeeHive: Sub-second Elasticity for Web Services with Semi-FaaS Execution
Zhao, Z., Wu, M., Tang, J., Zang, B., Wang, Z., & Chen, H. (2023) · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/3575693.3575752 · Cited by 20
Run-time adaptation of mobile applications using genetic algorithms
Pascual, G. G., Pinto, M., & Fuentes, L. (2013) · International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS) · doi:10.1109/seams.2013.6595494 · Cited by 20
Computation offloading for fast CNN inference in edge computing
Yang, Q., Luo, X., Li, P., Miyazaki, T., & Wang, X. (2019) · ACM Conference on Research in Adaptive and Convergent Systems (RACS) · doi:10.1145/3338840.3355669 · Cited by 19
Energy Efficient Implementation of Machine Learning Algorithms on Hardware Platforms
Osta, M., Alameh, M., Younes, H., Ibrahim, A., & Valle, M. (2019) · IEEE International Conference on Electronics, Circuits and Systems (ICECS) · doi:10.1109/icecs46596.2019.8965157 · Cited by 19
AoDNN: An Auto-Offloading Approach to Optimize Deep Inference for Fostering Mobile Web
Huang, Y., Qiao, X., Dustdar, S., & Li, Y. (2022) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom48880.2022.9796763 · Cited by 19
Janus: Latency-Aware Traffic Scheduling for IoT Data Streaming in Edge Environments
Wen, Z., Yang, R., Qian, B., Xuan, Y., Lu, L., Wang, Z., Peng, H., Xu, J., Zomaya, A. Y., & Ranjan, R. K. (2023) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2023.3312131 · Cited by 19
Allocation Algorithms for Autonomous Management of Collaborative Cloudlets
Bohez, S., Verbelen, T., Simoens, P., & Dhoedt, B. (2014) · IEEE International Conference on Mobile Cloud Computing, Services, and Engineering (MobileCloud) · doi:10.1109/mobilecloud.2014.14 · Cited by 19
Energy Efficient Seamless Service Provisioning in Mobile Cloud Computing
Ravi, A., & Peddoju, S. K. (2013) · IEEE International Symposium on Service-Oriented System Engineering (SOSE) · doi:10.1109/sose.2013.67 · Cited by 19
Towards energy efficient smart phone applications: Energy models for offloading tasks into the cloud
Segata, M., Bloessl, B., Sommer, C., & Dressler, F. (2014) · International Conference on Communications · doi:10.1109/icc.2014.6883681 · Cited by 19
Yoo, C., Hwang, I., Rozner, E., Gu, Y. J., & Dickerson, R. F. (2016) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2858036.2858286 · Cited by 19
Mobile cloud computing: A survey and propose solution framework
Chalaemwongwan, N., & Kurutach, W. (2016) · International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON) · doi:10.1109/ecticon.2016.7561437 · Cited by 19
Saha, S., Habib, M. A., Adhikary, T., Razzaque, M. A., & Rahman, M. M. (2017) · Multimedia Systems · doi:10.1007/s00530-017-0563-8 · Cited by 19
Toward High Mobile GPU Performance Through Collaborative Workload Offloading
Wu, C., Yang, B., Zhu, W., & Zhang, Y. (2017) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2017.2754482 · Cited by 19
Getting the most from map data structures in Android
Saborido, R., Morales, R., Khomh, F., Guéhéneuc, Y., & Antoniol, G. (2018) · Empirical Software Engineering · doi:10.1007/s10664-018-9607-8 · Cited by 19
Optimized assignment patterns in Mobile Edge Cloud networks
Ceselli, A., Fiore, M., Premoli, M., & Secci, S. (2018) · Computers & Operations Research · doi:10.1016/j.cor.2018.02.022 · Cited by 19
A Research Review on Energy Consumption of Different Frameworks in Mobile Cloud Computing
Ramasubbareddy, S., & Sasikala, R. (2018) · Lecture notes in networks and systems · doi:10.1007/978-981-10-8201-6_15 · Cited by 19
SMER: a secure method of exchanging resources in heterogeneous internet of things
Zhang, Y., Han, Y., & Wen, J. (2018) · Frontiers of Computer Science · doi:10.1007/s11704-018-6524-3 · Cited by 19
Jeong, H.-J., Shin, C. H., Shin, K. Y., Lee, H.-J., & Moon, S. (2019) · ACM Symposium on Cloud Computing (SoCC) · doi:10.1145/3357223.3362735 · Cited by 19
A comparative study on the energy consumption of Progressive Web Apps
Huber, S., Demetz, L., & Felderer, M. (2022) · Information Systems · doi:10.1016/j.is.2022.102017 · Cited by 19
Mobile, Collaborative Augmented Reality Using Cloudlets
Bohez, S., Turck, J. D., Verbelen, T., Simoens, P., & Dhoedt, B. (2013) · Mobile Wireless Middleware, Operating Systems, and Applications · doi:10.1109/mobilware.2013.13 · Cited by 19
A Novel Statistical Cost Model and an Algorithm for Efficient Application Offloading to Clouds
Barrameda, J., & Samaan, N. (2015) · IEEE Transactions on Cloud Computing · doi:10.1109/tcc.2015.2513404 · Cited by 19
Conceptualizing a Real-Time Remote Cardiac Health Monitoring System
Page, A., Hassanalieragh, M., Soyata, T., Aktaş, M. K., Kantarcı, B., & Andreescu, S. (2015) · Advances in wireless technologies and telecommunication book series · doi:10.4018/978-1-4666-8662-5.ch001 · Cited by 19
Sharing-Aware Cloud-Based Mobile Outsourcing
Mei, C., Taylor, D., Wang, C., Chandra, A., & Weissman, J. (2012) · IEEE ... International Conference on Cloud Computing · doi:10.1109/cloud.2012.48 · Cited by 18
The State of the Art in Implementing Machine Learning for Mobile Apps: A Survey
Dai, X., Spasić, ., Chapman, S., & Meyer, B. (2020) · IEEE SoutheastCon · doi:10.1109/southeastcon44009.2020.9249652 · Cited by 21
Safe and Practical GPU Computation in TrustZone
Park, H., & Lin, F. X. (2023) · European Conference on Computer Systems · doi:10.1145/3552326.3567483 · Cited by 18
Adaptive Activity and Context Recognition Using Multimodal Sensors in Smart Devices
Faye, S., Frank, R., & Engel, T. (2015) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-319-29003-4_3 · Cited by 18
Handoff Between Proxies in the Proxy-Based Mobile Computing System
Khalaj, A., & Lutfiyya, H. L. (2013) · Mobile Wireless Middleware, Operating Systems, and Applications · doi:10.1109/mobilware.2013.15 · Cited by 18
Han, S., Nandakumar, R., Philipose, M., Krishnamurthy, A., & Wetherall, D. J. (2014) · doi:10.1145/2611264.2611269 · Cited by 18
Reducing Task Completion Time in Mobile Offloading Systems through Online Adaptive Local Restart
Wang, Q., & Wolter, K. (2015) · ACM/SPEC International Conference on Performance Engineering (ICPE) · doi:10.1145/2668930.2688041 · Cited by 18
Cloud Offload in Hostile Environments
Ha, K., Lewis, G. A., Simanta, S., & Satyanarayanan, M. (2011) · Cited by 18
eWU-TV: User-Centric Energy-Efficient Digital TV Broadcast Over Wi-Fi Networks
Singhal, C., De, S. K., Trestian, R., & Muntean, G. (2015) · IEEE Transactions on Broadcasting · doi:10.1109/tbc.2014.2380891 · Cited by 18
Arching over the Mobile Computing Chasm: Platforms and Runtimes
Tarkoma, S., & Lagerspetz, E. (2010) · Computer · doi:10.1109/mc.2010.272 · Cited by 18
Make smartphones last a day: Pre-processing based computer vision application offloading
Li, J., Peng, Z., Xiao, B., & Hua, Y. (2015) · IEEE International Conference on Sensing, Communication, and Networking (SECON) · doi:10.1109/sahcn.2015.7338347 · Cited by 18
Mahmood, K., & Shila, D. M. (2016) · IEEE World Forum on Internet of Things (WF-IoT) · doi:10.1109/wf-iot.2016.7845457 · Cited by 18
Proactive Mobile Fog Computing using Work Stealing
Soo, S., Chang, C., Loke, S. W., & Srirama, S. N. (2017) · International Journal of Mobile Computing and Multimedia Communications · doi:10.4018/ijmcmc.2017100101 · Cited by 18
Energy‐aware dynamic task offloading and collective task execution in mobile cloud computing
Saritha, V., & Nagaraju, D. (2019) · International Journal of Communication Systems · doi:10.1002/dac.3914 · Cited by 18
On-demand deployment for IoT applications
Zhang, J., Ma, M., He, W., & Wang, P. (2020) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2020.101794 · Cited by 18
A framework for the recognition of horse gaits through wearable devices
Casella, E., Khamesi, A. R., & Silvestri, S. (2020) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2020.101213 · Cited by 18
Selecting Energy Efficient Cluster-Head Trajectories for Collaborative Mobile Sensing
Loomba, R., de Fréin, R., & Jennings, B. (2015) · Globecom · doi:10.1109/glocom.2015.7417727 · Cited by 18
Computation offloading strategies based on energy minimization under computational rate constraints
Barbarossa, S., Lorenzo, P. D., & Sardellitti, S. (2014) · European Conference on Networks and Communications · doi:10.1109/eucnc.2014.6882634 · Cited by 18
CrowdVision: A Computing Platform for Video Crowdprocessing Using Deep Learning
Lu, Z., Chan, K., Pu, S., & Porta, T. L. (2018) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2018.2864212 · Cited by 18
Habak, K., Shi, C., Zegura, E. W., Harras, K. A., & Ammar, M. (2017) · Wiley eBooks · doi:10.1002/9781119187202.ch7 · Cited by 18
CoSMiC: A hierarchical cloudlet-based storage architecture for mobile clouds
Duro, F. R., Garcia‐Blas, J., Higuero, D., Perez, O., & Carretero, J. (2014) · Simulation Modelling Practice and Theory · doi:10.1016/j.simpat.2014.07.007 · Cited by 17
CloudShield: Efficient anti-malware smartphone patching with a P2P network on the cloud
Barbera, M. V., Kosta, S., Stefa, J., Hui, P., & Mei, A. (2012) · IEEE International Conference on Peer-to-Peer Computing (P2P) · doi:10.1109/p2p.2012.6335810 · Cited by 17
Bittencourt, L. F., RANA, O. F., & Petri, I. (2016) · Communications in computer and information science · doi:10.1007/978-3-319-29582-4_1 · Cited by 17
Moitree: A Middleware for Cloud-Assisted Mobile Distributed Apps
Khan, M. A. R., Debnath, H., Paiker, N. R., Gehani, N., Ding, X., Curtmola, R., & Borcea, C. (2016) · IEEE International Conference on Mobile Cloud Computing, Services, and Engineering (MobileCloud) · doi:10.1109/mobilecloud.2016.21 · Cited by 17
A Self-Cloning Agents Based Model for High-Performance Mobile-Cloud Computing
Angın, P., Bhargava, B., & Jin, Z. (2015) · IEEE ... International Conference on Cloud Computing · doi:10.1109/cloud.2015.48 · Cited by 17
Optimizing Offloading Strategies in Mobile Cloud Computing
Hyytiä, E., Spyropoulos, T., & Ott, J. (2013) · Cited by 17
SMOC: A secure mobile cloud computing platform
Hao, Z., Tang, Y., Zhang, Y., Novak, E., Carter, N., & Li, Q. (2015) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom.2015.7218658 · Cited by 17
Seamless Live Virtual Machine Migration with Cloudlets and Multipath TCP
Teka, F., Lung, C., & Ajila, S. A. (2015) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac.2015.31 · Cited by 17
Semi-Markov state estimation and policy optimization for energy efficient mobile sensing
Yi, W., Krishnamachari, B., & Annavaram, M. (2012) · IEEE International Conference on Sensing, Communication, and Networking (SECON) · doi:10.1109/secon.2012.6275823 · Cited by 17
MCC-OSGi: An OSGi-based mobile cloud service model
Houacine, F., Bouzefrane, S., Li, L., & Huang, D. (2013) · International Symposium on Autonomous Decentralized Systems · doi:10.1109/isads.2013.6513442 · Cited by 17
A Cloud Storage overlay to aggregate heterogeneous Cloud services
Machado, G. S., Bocek, T., Ammann, M., & Stiller, B. (2013) · Conference on Local Computer Networks · doi:10.1109/lcn.2013.6761296 · Cited by 17
Optimal Model-Based Policies for Component Migration of Mobile Cloud Services
Gabner, R., Schwefel, H.-P., Hummel, K. A., & Haring, G. (2011) · IEEE International Symposium on Network Computing and Applications (NCA) · doi:10.1109/nca.2011.33 · Cited by 17
Smart and Secure Cross-Device Apps for the Internet of Advanced Things
Busold, C., Heuser, S., Rios, J., Sadeghi, A., & Asokan, N. (2015) · Lecture notes in computer science · doi:10.1007/978-3-662-47854-7_17 · Cited by 17
A Systematic Literature Review of Mobile Cloud Computing
Eweoya, I., & Daramola, O. J. (2015) · International Journal of Multimedia and Ubiquitous Engineering · doi:10.14257/ijmue.2015.10.12.15 · Cited by 17
HVC: A Hybrid Cloud Computing Framework in Vehicular Environments
Feng, J., Liu, Z., Wu, C., & Ji, Y. (2017) · IEEE International Conference on Mobile Cloud Computing, Services, and Engineering (MobileCloud) · doi:10.1109/mobilecloud.2017.9 · Cited by 17
A critical overview of latest challenges and solutions of Mobile Cloud Computing
Allam, H., Nassiri, N., Rajan, A., & Ahmad, J. (2017) · International Conference on Fog and Mobile Edge Computing (FMEC) · doi:10.1109/fmec.2017.7946435 · Cited by 17
Joint Computation Offloading and Resource Allocation in Cloud Based Wireless HetNets
Nguyen, T. T., & Le, L. B. (2017) · Globecom · doi:10.1109/glocom.2017.8254705 · Cited by 17
MEACC: an energy-efficient framework for smart devices using cloud computing systems
Alsubhi, K., Imtiaz, Z., Raana, A., Ashraf, M. U., & Hayat, B. (2020) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.1900198 · Cited by 17
Edge computing: A systematic mapping study
Sakhdari, J., Zolfaghari, B., Izadpanah, S., Zargar, S. M., Rahati-Quchani, M., Shadi, M., Abrishami, S., & Rasoolzadegan, A. (2023) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.7741 · Cited by 19
PerDNN: Offloading Deep Neural Network Computations to Pervasive Edge Servers
Jeong, H.-J., Lee, H.-J., Shin, K. Y., Yoo, Y. H., & Moon, S. (2020) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs47774.2020.00114 · Cited by 17
A Multi-Objective Genetic Algorithm-Based Resource Scheduling in Mobile Cloud Computing
Ramasubbareddy, S., Swetha, E., Luhach, A. K., & Srinivas, T. A. S. (2021) · International Journal of Cognitive Informatics and Natural Intelligence · doi:10.4018/ijcini.20210701.oa5 · Cited by 17
Performance evaluation of remote display access for mobile cloud computing
Lin, Y., Kämäräinen, T., Francesco, M. D., & Ylä-Jääski, A. (2015) · Computer Communications · doi:10.1016/j.comcom.2015.05.006 · Cited by 17
Examining storage performance on mobile devices
Kim, H., Agrawal, N., & Ungureanu, C. (2011) · doi:10.1145/2043106.2043112 · Cited by 17
Providing local cloud services to mobile devices with inter-cloudlet communication
Rawadi, J., Artail, H. A., & Safa, H. (2014) · IEEE Mediterranean Electrotechnical Conference (MELECON) · doi:10.1109/melcon.2014.6820520 · Cited by 17
The human element as the key enabler of pervasiveness
Giordano, S., & Puccinelli, D. (2011) · Annual Mediterranean Ad Hoc Networking Workshop · doi:10.1109/med-hoc-net.2011.5970482 · Cited by 17
Context-Aware Prediction Model for Offloading Mobile Application Tasks to Mobile Cloud Environments
Jadad, H. A., Touzene, A., Day, K., Alziedi, N., & Arafeh, B. R. (2019) · International Journal of Cloud Applications and Computing · doi:10.4018/ijcac.2019070104 · Cited by 16
An Adaptive Resource Allocation Algorithm for Partitioned Services in Mobile Cloud Computing
Liu, Y., & Lee, M. J. (2015) · IEEE International Symposium on Service-Oriented System Engineering (SOSE) · doi:10.1109/sose.2015.19 · Cited by 16
Mobile cloud messaging supported by XMPP primitives
Flores, H., & Srirama, S. N. (2013) · Mobile Cloud Computing & Services · doi:10.1145/2482981.2482983 · Cited by 16
Mobile Web Browser Optimizations in the Cloud Era: A Survey
Wang, H., Kong, J., Guo, Y., & Chen, X. (2013) · IEEE International Symposium on Service-Oriented System Engineering (SOSE) · doi:10.1109/sose.2013.18 · Cited by 16
Wireless Body Area Networks: Where does energy go?
Lee, S., & Annavaram, M. (2012) · IEEE International Symposium on Workload Characterization · doi:10.1109/iiswc.2012.6402897 · Cited by 16
CMcloud: Cloud Platform for Cost-Effective Offloading of Mobile Applications
Chae, D., Kim, J., Kim, J., Kim, J., Yang, S., Cho, Y., Kwon, Y., & Paek, Y. (2014) · IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid) · doi:10.1109/ccgrid.2014.75 · Cited by 16
Cloud path selection using Fuzzy Analytic Hierarchy Process for offloading in Mobile Cloud Computing
Singla, C., & Kaushal, S. (2015) · International Conference on Recent Advances in Engineering & Computational Sciences · doi:10.1109/raecs.2015.7453370 · Cited by 16
Energy-aware computing for android platforms
Chang, H.-C., Agrawal, A. R., & Cameron, K. W. (2011) · International Conference on Energy Aware Computing · doi:10.1109/iceac.2011.6403625 · Cited by 16
Opportunistic Sharing of Continuous Mobile Sensing Data for Energy and Power Conservation
Moamen, A. A., & Jamali, N. (2017) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2017.2705685 · Cited by 16
Lagén, S., Pascual‐Iserte, A., Muñoz, O., & Vidal, J. (2017) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2017.2778692 · Cited by 16
A Survey of Mobile Computation Offloading: Applications, Approaches and Challenges
Kumar, S., Tyagi, M., Khanna, A., & Fore, V. (2018) · International Conference on Advances in Computing and Communication Engineering · doi:10.1109/icacce.2018.8441740 · Cited by 16
Olivier, P., Mehrab, A. K. M. F., Lankes, S., Karaoui, M. L., Lyerly, R., & Ravindran, B. (2019) · ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC) · doi:10.1145/3307681.3325408 · Cited by 16
On-Device Deep Learning Inference for Efficient Activity Data Collection
Mairittha, N., Mairittha, T., & Inoue, S. (2019) · Sensors · doi:10.3390/s19153434 · Cited by 16
AndroidOff:Offloading android application based on cost estimation
Chen, X., Chen, J., Liu, B., Ma, Y., Zhang, Y., & Zhong, H. (2019) · Journal of Systems and Software · doi:10.1016/j.jss.2019.110418 · Cited by 16
BCEdge: Blockchain‐based resource management in D2D‐assisted mobile edge computing
Zhou, A., Sun, Q., & Li, J. (2019) · Software Practice and Experience · doi:10.1002/spe.2758 · Cited by 16
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
Efficient resourceful mobile cloud architecture (mRARSA) for resource demanding applications
Islam, A., Kumar, A. S., Mohiuddin, K., Yasmin, S., Khaleel, M. A., & Hussain, M. R. (2020) · Journal of Cloud Computing Advances Systems and Applications · doi:10.1186/s13677-020-0155-6 · Cited by 16
Zhang, Y., Zhang, W., Peng, K., Yan, D., & Wu, Q. (2020) · Automatika · doi:10.1080/00051144.2020.1837499 · Cited by 16
Power behavior analysis of mobile applications using Bugu
Li, Y., Chen, H., & Shi, W. (2014) · Sustainable Computing Informatics and Systems · doi:10.1016/j.suscom.2014.07.002 · Cited by 16
Mobile Web Services: State of the Art and Challenges
Elgazzar, K., & Martin, P. (2014) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2014.050324 · Cited by 16
Distributed joint optimization of radio and computational resources for mobile cloud computing
Sardellitti, S., Scutari, G., & Barbarossa, S. (2014) · IEEE International Conference on Cloud Networking · doi:10.1109/cloudnet.2014.6968994 · Cited by 16
Improving scientific application execution on android mobile devices via code refactorings
Rodríguez, A., Mateos, C., & Zunino, A. (2016) · Software Practice and Experience · doi:10.1002/spe.2419 · Cited by 16
OaaS: offload as a service in fog networks
Tran, D. H., Tran, N. H. K., Pham, C., Kazmi, S. M. A., Huh, E., & Hong, C. S. (2017) · Computing · doi:10.1007/s00607-017-0551-z · Cited by 16
Context-Aware Task Offloading for Wearable Devices
Yang, Y. X., Geng, Y., Qiu, L., Hu, W., & Cao, G. (2017) · International Conference on Computer Communications and Networks (ICCCN) · doi:10.1109/icccn.2017.8038470 · Cited by 16
A collaborative CPU–GPU approach for principal component analysis on mobile heterogeneous platforms
Valery, O., Liu, P., & Wu, J. (2018) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2018.05.006 · Cited by 16
Fuzzy-Logic-Based Decision Engine for Offloading IoT Application Using Fog Computing
Dhanya, N. M., Kousalya, G., Balarksihnan, P., & Raj, P. (2018) · Advances in computer and electrical engineering book series · doi:10.4018/978-1-5225-5972-6.ch009 · Cited by 16
ECCO: Edge-Cloud Chaining and Orchestration Framework for Road Context Assessment
Cozzolino, V., Ott, J., Ding, A. Y., & Mortier, R. (2020) · IEEE/ACM International Conference on Internet-of-Things Design and Implementation (IoTDI) · doi:10.1109/iotdi49375.2020.00029 · Cited by 16
Chen, Y., Chen, S., Li, K., Liang, W., & Li, Z. (2022) · Cluster Computing · doi:10.1007/s10586-022-03768-z · Cited by 15
Partial Offloading Strategy Based on Deep Reinforcement Learning in the Internet of Vehicles
Tian, S., Zhu, X., Feng, B., Zheng, Z., Liu, H., & Li, Z. (2025) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2025.3543976 · Cited by 15
Recent Trends in Edge Computing
Kannadhasan, S., Ganesh, S., Palaniyammal, P., Priya, G. N., & Singh, V. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-1147-0.ch013 · Cited by 15
Modeling and Analysis of Mobile Cloud Computing Based on Bigraph Theory
Yu, L., TSAI, W. T., Wei, X., Gao, J. Z., Hildebrandt, T., & Guo, X. Q. (2014) · IEEE International Conference on Mobile Cloud Computing, Services, and Engineering (MobileCloud) · doi:10.1109/mobilecloud.2014.11 · Cited by 15
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