Joint offloading and scheduling decisions for DAG applications in mobile edge computing

Wang, 2017, Joint offloading and computing optimization in wireless powered mobile-edge computing systems, IEEE Trans. Wirel. Commun., PP, 1

Mao, 2017, Stochastic joint radio and computational resource management for multi-user mobile-edge computing systems, IEEE Trans. Wirel. Commun., 16, 5994, 10.1109/TWC.2017.2717986

Chiang, 2017, Fog and IoT: An overview of research opportunities, IEEE Int. Things J., 3, 854, 10.1109/JIOT.2016.2584538

Cohen, 2008, Embedded speech recognition applications in mobile phones: Status, trends, and challenges, 5352

Soyata, 2012, Cloud-vision: Real-time face recognition using a mobile-cloudlet-cloud acceleration architecture, 000059

Barbarossa, 2014, Communicating while computing: Distributed mobile cloud computing over 5g heterogeneous networks, IEEE Signal Process. Mag., 31, 45, 10.1109/MSP.2014.2334709

Bellavista, 2017, Converging mobile edge computing, fog computing, and IoT quality requirements, 313

Ahmed, 2016, A survey on mobile edge computing

Sabella, 2016, Mobile-edge computing architecture: The role of MEC in the internet of things, IEEE Consum. Electron. Mag., 5, 84, 10.1109/MCE.2016.2590118

Beck, 2014, Mobile edge computing: A taxonomy, 48

Tran, 2017, Collaborative mobile edge computing in 5g networks: New paradigms, scenarios, and challenges, IEEE Commun. Mag., 55, 54, 10.1109/MCOM.2017.1600863

Yu, 2016, 1

Mahmoodi, 2016, Optimal joint scheduling and cloud offloading for mobile applications, IEEE Trans. Cloud Comput., PP, 1

Yang, 2015, Multi-user computation partitioning for latency sensitive mobile cloud applications, IEEE Trans. Comput., 64, 2253, 10.1109/TC.2014.2366735

Wang, 2016, Mobile-edge computing: Partial computation offloading using dynamic voltage scaling, IEEE Trans. Commun., 64, 4268

Zhang, 2013, Energy-optimal mobile cloud computing under stochastic wireless channel, IEEE Trans. Wirel.s Commun., 12, 4569, 10.1109/TWC.2013.072513.121842

Rimal, 2017, Cloudlet enhanced fiber-wireless access networks for mobile-edge computing, IEEE Trans. Wirel. Commun., PP, 1

Kao, 2015, Hermes: Latency optimal task assignment for resource-constrained mobile computing, 1894

Ra, 2011, Odessa: enabling interactive perception applications on mobile devices, 43

Jia, 2014, Heuristic offloading of concurrent tasks for computation-intensive applications in mobile cloud computing, 352

Guo, 2016, Energy-efficient dynamic offloading and resource scheduling in mobile cloud computing, 1

Kumar, 2013, A survey of computation offloading for mobile systems, Mobile Netw. Appl., 18, 129, 10.1007/s11036-012-0368-0

ETSI, S. Antipolis, France, Mobile-edge computing-introductory technical white paper, Sep. 2014. [Online]. Available: https://portal.etsi.org/portals/0/tbpages/mec/docs/mobile-edge_computing_introductory_technical_white_paper_v1%2018-09-14.pdf.

Intel, S. Clara, CA, USA, Real-world impact of mobile edge computing (MEC), Jan. 2016. [Online]. Available: https://builders.intel.com/docs/networkbuilders/Real-world-impact-of-mobile-edgecomputing-MEC.pdf.

Satyanarayanan, 2009, The case for VM-based cloudlets in mobile computing, IEEE Pervas. Comput., 8, 14, 10.1109/MPRV.2009.82

Kumar, 2010, Cloud computing for mobile users: Can offloading computation save energy?, Computer, 43, 51, 10.1109/MC.2010.98

Mahmoodi, 2015, Cloud offloading for multi-radio enabled mobile devices[C], 5473

Hong, 2016, Qoe-aware computation offloading scheduling to capture energy-latency tradeoff in mobile clouds, 1

Mao, 2017

Mao, 2017, Joint task offloading scheduling and transmit power allocation for mobile-edge computing systems, 1

Ge, 2012, A game theoretic resource allocation for overall energy minimization in mobile cloud computing system, 279

Ouyang, 2014, Hybrid particle swarm optimization for parameter estimation of muskingum model, Neural Compu. Appl., 25, 1785, 10.1007/s00521-014-1669-y

Ouyang, 2015, Parallel hybrid PSO with cuda for ld heat conduction equation, Comput. Fluids, 110, 198, 10.1016/j.compfluid.2014.05.020

Guo, 2018, An efficient computation offloading management scheme in the densely deployed small cell networks with mobile edge computing, IEEE/ACM Trans. Netw., 26, 2651, 10.1109/TNET.2018.2873002

Rodrigues, 2018, Cloudlets activation scheme for scalable mobile edge computing with transmission power control and virtual machine migration, IEEE Trans. Comput., 67, 1287, 10.1109/TC.2018.2818144

Miettinen, 2010, Energy efficiency of mobile clients in cloud computing, 4

Xiong, 2012, Energy-efficient resource allocation in OFDMA networks, IEEE Trans. Commun., 60, 3767, 10.1109/TCOMM.2012.082812.110639

Topcuoglu, 2002, Performance-effective and low-complexity task scheduling for heterogeneous computing, IEEE Transactions on Parallel & Distributed Systems, 13, 260, 10.1109/71.993206

Ilavarasan, 2007, Performance effective task scheduling algorithm for heterogeneous computing system, J. Comput. Sci., 3, 28

Bittencourt, 2010, Dag scheduling using a lookahead variant of the heterogeneous earliest finish time algorithm, 27

Zeng, 2018, Facial expression recognition via learning deep sparse autoencoders [J], Neurocomputing, 273, 643, 10.1016/j.neucom.2017.08.043

Nianyin, 2014, Image-based quantitative analysis of gold immunochromatographic strip via cellular neural network approach, IEEE Trans. Med. Imaging, 33, 1129, 10.1109/TMI.2014.2305394