Dynamic offloading for energy-aware scheduling in a mobile cloud

Akherfi, 2018, Mobile cloud computing for computation offloading: Issues and challenges, Appl. Comput. Informatics, 14, 1, 10.1016/j.aci.2016.11.002 Aljabri, 2013, Scheduling Manager for Mobile Cloud Using, 02, 451 Armstrong, P., et al., 2010. Cloud Scheduler: a resource manager for distributed compute clouds, pp. 1–10. Baidas, 2021, Resource allocation for offloading-efficiency maximization in clustered NOMA-enabled mobile edge computing networks, Comput. Netw., 189, 107919, 10.1016/j.comnet.2021.107919 Cao, 2019, Joint computation and communication cooperation for energy-efficient mobile edge computing, IEEE Internet Things J., 6, 4188, 10.1109/JIOT.2018.2875246 O. Chabbouh, S. Ben Rejeb, Z. Choukair, and N. Agoulmine, “Offloading decision algorithm for 5G/HetNets cloud RAN,” 2016 24th Int. Conf. Software, Telecommun. Comput. Networks, SoftCOM 2016, 2016, doi: 10.1109/SOFTCOM.2016.7772164. Chen, 2021, Robust Computation Offloading and Resource Scheduling in Cloudlet-Based Mobile Cloud Computing, IEEE Trans. Mob. Comput., 20, 2025, 10.1109/TMC.2020.2973993 Chen, 2018, Computation Peer Offloading for Energy-Constrained Mobile Edge Computing in Small-Cell Networks, IEEE/ACM Trans. Netw., 26, 1619, 10.1109/TNET.2018.2841758 Dai, 2021, Vehicle Assisted Computing Offloading for Unmanned Aerial Vehicles in Smart City, IEEE Trans. Intell. Transp. Syst., 22, 1932, 10.1109/TITS.2021.3052979 Ekman, 2019, Trials of 60 GHz Radio for a Future 5G New Radio (NR) Solution for High Capacity CCTV Offload and Multimedia Transfer, IEEE Int. Symp. Broadband Multimed. Syst. Broadcast. BMSB, vol, 2019-June Ec. Ervo, A. Wolman, L. Cox, S. Saroiu, M. Musuvathi, and A. Razeen, “Demo: Kahawai: High-quality mobile gaming using GPU offload,” MobiSys 2014 - Proc. 12th Annu. Int. Conf. Mob. Syst. Appl. Serv., p. 345, 2014, doi: 10.1145/2594368.2601482. Fernando, N., Loke, S.W., Rahayu, W., 2013. Honeybee: A programming framework for mobile crowd computing. Lect. Notes Inst. Comput. Sci. Soc. Telecommun. Eng. LNICST, 120 LNICST (January), 224–236. doi: 10.1007/978-3-642-40238-8_19. Guo, 2019, Energy-Efficient Dynamic Computation Offloading and Cooperative Task Scheduling in Mobile Cloud Computing, IEEE Trans. Mob. Comput., 18, 319, 10.1109/TMC.2018.2831230 Hao, 2015, Evaluation of nine heuristic algorithms with data-intensive jobs and computing-intensive jobs in a dynamic environment, IET Softw., 9, 7, 10.1049/iet-sen.2014.0014 Hao, 2015, Performance Analysis of Gang Scheduling in a Grid, J. Netw. Syst. Manag., 23, 650, 10.1007/s10922-014-9312-x Hao, 2018, Energy Efficient Task Caching and Offloading for Mobile Edge Computing, IEEE Access, 6, 11365, 10.1109/ACCESS.2018.2805798 Hao, 2019, Adaptive energy-aware scheduling method in a meteorological cloud, Futur. Gener. Comput. Syst., 101, 1142, 10.1016/j.future.2019.07.061 Hao, 2021, Energy-aware offloading based on priority in mobile cloud computing, Sustain. Comput. Informatics Syst., 31, 100563, 10.1016/j.suscom.2021.100563 Hosseini, 2022, Optimized task scheduling for cost-latency trade-off in mobile fog computing using fuzzy analytical hierarchy process, Comput. Networks, 206, 108752, 10.1016/j.comnet.2021.108752 Huda, 2022, Survey on computation offloading in UAV-Enabled mobile edge computing, J. Netw. Comput. Appl., 201, 103341, 10.1016/j.jnca.2022.103341 Khan, 2015, A survey of computation offloading strategies for performance improvement of applications running on mobile devices, J. Netw. Comput. Appl., 56, 28, 10.1016/j.jnca.2015.05.018 Khan, 2012, A goal programming based energy efficient resource allocation in data centers, J. Supercomput., 61, 502, 10.1007/s11227-011-0611-7 Kim, 2020, Signal Strength-Aware Adaptive Offloading with Local Image Preprocessing for Energy Efficient Mobile Devices, IEEE Trans. Comput., 69, 99, 10.1109/TC.2019.2939239 Kim, 2018, An Optimal Pricing Scheme for the Energy-Efficient Mobile Edge Computation Offloading with OFDMA, IEEE Commun. Lett., 22, 1922, 10.1109/LCOMM.2018.2849401 J. Kołodziej et al., “An application of Markov jump process model for activity-based indoor Mobility Prediction in wireless networks,” Proc. - 2011 9th Int. Conf. Front. Inf. Technol. FIT 2011, pp. 51–56, 2011, doi: 10.1109/FIT.2011.17. Li, C., Li, L., 2011. Tradeoffs between energy consumption and QoS in mobile grid, 55(3). Li, 2020, Energy-Efficient UAV-Assisted Mobile Edge Computing: Resource Allocation and Trajectory Optimization, IEEE Trans. Veh. Technol., 69, 3424, 10.1109/TVT.2020.2968343 Li, 2015, Heuristics to allocate high-performance cloudlets for computation offloading in mobile ad hoc clouds, J. Supercomput., 71, 3009, 10.1007/s11227-015-1425-9 Li, 2019, Offloading and system resource allocation optimization in TDMA based wireless powered mobile edge computing, J. Syst. Archit., 98, 221, 10.1016/j.sysarc.2019.07.009 Lin, 2015, Time-and-energy-aware computation offloading in handheld devices to coprocessors and clouds, IEEE Syst. J., 9, 393, 10.1109/JSYST.2013.2289556 Lin, 2021, A Novel Lyapunov based Dynamic Resource Allocation for UAVs-assisted Edge Computing, Comput. Netw., 205, 2022 Lindberg, 2012, “Comparison and Analysis of Greedy Energy-Efficient Scheduling Algorithms for Computational Grids”, Energy-Efficient Distrib, Comput. Syst., 189 Lu, 2020, Mildip: An energy efficient code offloading framework in mobile cloudlets, Inf. Sci. (Ny), 513, 84, 10.1016/j.ins.2019.10.008 Lyu, 2018, Energy-Efficient Admission of Delay-Sensitive Tasks for Mobile Edge Computing, IEEE Trans. Commun., 66, 2603, 10.1109/TCOMM.2018.2799937 Ma, 2021, Poster: Adaptive video offloading in mobile edge computing, Proc. - Int. Conf. Distrib. Comput. Syst., vol. 2021-July, 1130 Masoudi, 2021, Device vs Edge Computing for Mobile Services: Delay-Aware Decision Making to Minimize Power Consumption, IEEE Trans. Mob. Comput., 20, 3324, 10.1109/TMC.2020.2999784 N. Min-allah, Y. Wang, J. Xing, W. Nisar, and A. Kazmi, “Towards Dynamic Voltage Scaling in Real-Time Systems-A Survey,” Int. J. Comput. Sci. Eng. Syst., vol. 1, no. 2, pp. 93–104, 2007, [Online]. Available: http://en.scientificcommons.org/42383068. Opadere, J., Liu, Q., Zhang, N., Han, T., 2019. Joint Computation and Communication Resource Allocation for Energy-Efficient Mobile Edge Networks. IEEE Int. Conf. Commun., 2019-May (3), 4188–4200. doi: 10.1109/ICC.2019.8761886. Páll, S., Schultz, R., 2019. Advances in the OpenCL offload support in GROMACS. In: ACM Int. Conf. Proceeding Ser., p. 3318176, 2019, doi: 10.1145/3318170.3318176. Papathanail, G., et al., 2020. COSMOS: An Orchestration Framework for Smart Computation Offloading in Edge Clouds. Proc. IEEE/IFIP Netw. Oper. Manag. Symp. 2020 Manag. Age Softwarization Artif. Intell. NOMS 2020, April, 2020. doi: 10.1109/NOMS47738.2020.9110294. Salmani, 2020, Uplink resource allocation for multiple access computational offloading, Signal Process., 168, 107322, 10.1016/j.sigpro.2019.107322 Y. Tao, Y. Zhang, and Y. Ji, “Efficient computation offloading strategies for mobile cloud computing,” Proc. - Int. Conf. Adv. Inf. Netw. Appl. AINA, vol. 2015-April, pp. 626–633, 2015, doi: 10.1109/AINA.2015.246. Tuli, 2020, Shared data-aware dynamic resource provisioning and task scheduling for data intensive applications on hybrid clouds using Aneka, Futur. Gener. Comput. Syst., 106, 595, 10.1016/j.future.2020.01.038 Wan, 2021, Joint computation offloading and resource allocation for NOMA-based multi-access mobile edge computing systems, Comput. Netw., 196, 108256, 10.1016/j.comnet.2021.108256 Wang, 2021, Collaborative Mobile Computation Offloading to Vehicle-Based Cloudlets, IEEE Trans. Veh. Technol., 70, 768, 10.1109/TVT.2020.3043296 Wang, 2022, Computation offloading and resource allocation based on distributed deep learning and software defined mobile edge computing, Comput. Netw., 205, 108732, 10.1016/j.comnet.2021.108732 Wang, 2022, Computation offloading and resource allocation based on distributed deep learning and software defined mobile edge computing, Comput. Netw., 205, 108732, 10.1016/j.comnet.2021.108732 Wang, 2018, Efficient multi-tasks scheduling algorithm in mobile cloud computing with time constraints, Peer-to-Peer Netw. Appl., 11, 793, 10.1007/s12083-017-0561-9 Wu, 2018, Android Unikernel: Gearing mobile code offloading towards edge computing, Futur. Gener. Comput. Syst., 86, 694, 10.1016/j.future.2018.04.069 Xu, 2021, Energy-Aware Inference Offloading for DNN-Driven Applications in Mobile Edge Clouds, IEEE Trans. Parallel Distrib. Syst., 32, 799, 10.1109/TPDS.2020.3032443 Yang, 2021, Offloading Time Optimization via Markov Decision Process in Mobile-Edge Computing, IEEE Internet Things J., 8, 2483, 10.1109/JIOT.2020.3033285 Yu, 2020, A Socially-Aware Hybrid Computation Offloading Framework for Multi-Access Edge Computing, IEEE Trans. Mob. Comput., 19, 1247, 10.1109/TMC.2019.2908154 Zhang, 2022, Optimal pricing-based computation offloading and resource allocation for blockchain-enabled beyond 5G networks, Comput. Netw., 203, 108674, 10.1016/j.comnet.2021.108674 Zhang, 2013, Energy-efficient scheduling policy for collaborative execution in mobile cloud computing, Proc. - IEEE INFOCOM, 190 Zhang, 2014, Toward transcoding as a service: Energy-efficient offloading policy for green mobile cloud, IEEE Netw., 28, 67, 10.1109/MNET.2014.6963807