Computation Offloading and Scheduling in Edge-Fog Cloud Computing

Dadmehr Rahbari (University of Qom)
Mohsen Nickray (Department of Computer Engineering and Information Technology, University of Qom)

Abstract


Resource allocation and task scheduling in the Cloud environment faces many challenges, such as time delay, energy consumption, and security. Also, executing computation tasks of mobile applications on mobile devices (MDs) requires a lot of resources, so they can offload to the Cloud. But Cloud is far from MDs and has challenges as high delay and power consumption. Edge computing with processing near the Internet of Things (IoT) devices have been able to reduce the delay to some extent, but the problem is distancing itself from the Cloud. The fog computing (FC), with the placement of sensors and Cloud, increase the speed and reduce the energy consumption. Thus, FC is suitable for IoT applications. In this article, we review the resource allocation and task scheduling methods in Cloud, Edge and Fog environments, such as traditional, heuristic, and meta-heuristics. We also categorize the researches related to task offloading in Mobile Cloud Computing (MCC), Mobile Edge Computing (MEC), and Mobile Fog Computing (MFC). Our categorization criteria include the issue, proposed strategy, objectives, framework, and test environment. 


Keywords


Cloud computing; Edge computing; Fog computing; Offloading; Scheduling

Full Text:

PDF

References


Gupta, H. Vahid Dastjerdi, A. Ghosh, S. K. Buyya, R. ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments, Software: Practice and Experience, 2017, 47(9): 1275-1296.

Gusev, M. Dustdar, S. Going back to the roots-the evolution of edge computing, an iot perspective, IEEE Internet Computing, 2018, 22(2): 5-15.

Dizdarević, J., Carpio, F., Jukan, A., Masip-Bruin, X. A survey of communication protocols for internet of things and related challenges of fog and cloud computing

integration. ACM Computing Surveys (CSUR), 2019, 51(6): 116.

Gill, S. S., Chana, I., Singh, M., Buyya, R. RADAR: Self-configuring and self-healing in resource management for enhancing quality of cloud services. Concur rency and Computation: Practice and Experience, 2019, 31(1): e4834.

Aazam, M. St-Hilaire, M. Lung, C. H. Lambadaris, I. Pre-fog: Iot trace based probabilistic resource estimation at fog, in: Consumer Communications and Networking Conference (CCNC), 2016 13th IEEE Annual, Las Vegas, NV, USA, 9-12, IEEE, 2016: 12-17.

Mahmud, R. Kotagiri, R. Buyya, R. Fog computing: A taxonomy, survey and future directions, in: Internet of Everything, Springer, 2018: 103-130.

Rahmani, A. M. Gia, T. N. Negash, B. Anzanpour, A. Azimi, I. Jiang, M. Liljeberg, P. Exploiting smart e-health gateways at the edge of healthcare internet-of-things: a fog computing approach, Future Generation Computer Systems, 2017: 641-658.

Yousefpour, A., Fung, C., Nguyen, T., Kadiyala, K., Jalali, F., Niakanlahiji, A., Jue, J. P. All one needs to know about fog computing and related edge computing paradigms: A complete survey. Journal of Systems Architecture, 2019.

Satyanarayanan, M. Bah, P. Caceres, R. Davies, N. The case for vm-based cloudlets in mobile computing, IEEE pervasive Computing, 2009, 8(4): 14-23.

Gupta, P. Ghrera, S. P. Trust and deadline aware scheduling algorithm for cloud infrastructure using ant colony optimization, in: Innovation and Challenges in Cyber Security (ICICCS-INBUSH), International Conference on, Noida, India, 3-5. IEEE, 2016: 187-191.

Rodriguez, M. A. Buyya, R. Deadline based resource provisioning and scheduling algorithm for scientific workflows on clouds, IEEE Transactions on Cloud Computing, 2014, 2(2): 222-235.

Yakubu, J., Christopher, H. A., Chiroma, H., Abdullahi, M. Security challenges in fog-computing environment: a systematic appraisal of current developments.Journal of Reliable Intelligent Environments, 2019: 1-25.

Wang, T., Liang, Y., Jia, W., Arif, M., Liu, A., Xie, M. Coupling resource management based on fog computing in smart city systems. Journal of Network and Computer Applications, 2019, 135: 11-19.

Chen, N. Chen, Y. Smart city surveillance at the network edge in the era of iot: Opportunities and challenges, in Smart Cities, , 2018: 153-176.

Hosseinian-Far, A. Ramachandran, M. Slack, C. L. Emerging trends in cloud computing, big data, fog computing, iot and smart living, in Technology for Smart Futures, Springer, 2018: 29-40.

Umang, N., Bierlaire, M., & Erera, A. L. Real-time management of berth allocation with stochastic arrival and handling times. Journal of Scheduling, 2017, 20(1): 67-83.

Zhen, L., Liang, Z., Zhuge, D., Lee, L. H., & Chew, E. P. Daily berth planning in a tidal port with channel flow control. Transportation Research Part B: Methodological, 2017, 106: 193-217.

Dulebenets, M. A. A comprehensive multi-objective optimization model for the vessel scheduling problem in liner shipping. International Journal of Production Economics, 2018, 196: 293-318. .

Xiang, X., Liu, C., & Miao, L. Reactive strategy for discrete berth allocation and quay crane assignment problems under uncertainty. Computers & Industrial Engineering, 2018, 126: 196-216.

Dulebenets, M.A. A Delayed Start Parallel Evolutionary Algorithm for Just-in-Time Truck Scheduling at a Cross-Docking Facility. International Journal of Production Economics. 2019, 212: 236-258.

Dulebenets, M.A. A Comprehensive Evaluation of Weak and Strong Mutation Mechanisms in Evolutionary Algorithms for Truck Scheduling at Cross-Docking Terminals. IEEE Access. 2018, 6: 65635-65650.

Serrano, C.; Delorme, X.; Dolgui, A. Scheduling of truck arrivals, truck departures and shop-floor operation in a cross-dock platform, based on trucks loading plans. International Journal of Production Economics. 2017, 194: 102–112.

Khalili-Damghani, K.; Tavana, M.; Santos-Arteaga, F.J.; Ghanbarzad-Dashti, M. A. A customized genetic algorithm for solving multi-period cross-dock truck scheduling problems. Measurement. 2017, 108: 101–118.

Ertem, M., Ozcelik, F., & Saraç, T. Single machine scheduling problem with stochastic sequence-dependent setup times. International Journal of Production Research, 2019, 1-17.

Dong, C.; Li, Q.; Shen, B.; Tong, X. Sustainability in Supply Chains with Behavioral Concerns. Sustainability, 2019, 11: 4071.

Rahbari, D., Kabirzadeh, S., and Nickray, M.. A security aware scheduling in fog computing by hyper heuristic algorithm. In Intelligent Systems and Signal Processing (ICSPIS), 2017 3rd Iranian Conference on. IEEE, 87-92.

Rahbari, D., Nickray, M. Low-latency and energy-efficient scheduling in fog-based IoT applications. Turkish Journal of Electrical Engineering & Computer Sciences, 2019, 27(2): 1406-1427.

Rodriguez, M. A. Buyya, R. A taxonomy and survey on scheduling algorithms for scientific workflows in iaas cloud computing environments, Concurrency and Computation: Practice and Experience, 2017, 29 (8): 1-23.

Raidl, G. R., Puchinger, J., Blum, C. Metaheuristic Hybrids. In Handbook of Metaheuristics. Springer, Cham, 2019: 385-417.

Frincu, M. E. Genaud, S. Gossa, J. Comparing provisioning and scheduling strategies for workflows on clouds, in: Parallel and Distributed Processing Symposium Workshops and PhD Forum (IPDPSW), 2013 IEEE 27th International, Cambridge, MA, USA, 20-24 May, IEEE, 2013, pp. 2101-2110.

Singh, S., & Chana, I. A survey on resource scheduling in cloud computing: Issues and challenges. Journal of grid computing, 2016, 14(2): 217-264.

Singh, P., Dutta, M., & Aggarwal, N. A review of task scheduling based on meta-heuristics approach in cloud computing. Knowledge and Information Systems, 2017, 52(1): 1-51.

Malawski, M. Figiela, K. Bubak, M. Deelman, E. Nabrzyski, J. Scheduling multi-level deadline-constrained scientific workflows on clouds based on cost optimization, Scientific Programming 2015, 5-5.

Durillo, J. J., Prodan, R. Multi-objective workflow scheduling in amazon ec2, Cluster computing, 2014, 17 (2): 169-189.

Poola, D., Ramamohanarao, K., Buyya, R. Fault-tolerant workflow scheduling using spot instances on clouds, Procedia Computer Science, 2014, 29: 523-533.

Zhu, X., Wang, J., Guo, H., Zhu, D., Yang, L. T., Liu, L. Fault-tolerant scheduling for real-time scientific workflows with elastic resource provisioning in virtualized

clouds, IEEE Transactions on Parallel and Distributed Systems, 2016, 27 (12): 3501-3517.

Bittencourt, L. F., Diaz-Montes, J., Buyya, R., Rana, O. F., Parashar, M. Mobility-aware application scheduling in fog computing, IEEE Cloud Computing, 2017, 4(2): 26-35.

Pham, X. Q., Huh, E. N. Towards task scheduling in a cloud-fog computing system, in: Network Operations and Management Symposium (APNOMS), 2016 18th AsiaPacific, Kanazawa, Japan, 5-7, IEEE, 2016: 1-4.

Zahaf, H. E., Benyamina, A. E. H., Olejnik, R., Lipari, G. Energy-efficient scheduling for moldable real-time tasks on heterogeneous computing platforms, Journal of Systems Architecture, 2017, 74: 46-60.

Lao, F. Zhang, X. Guo, Z. Parallelizing video transcoding using map-reduce-based cloud computing, in: Circuits and Systems (ISCAS), 2012 IEEE International Symposium on, Seoul, South Korea, 20-23 May, IEEE, 2012: 2905-2908.

Rahbari, D., Nickray, M. Scheduling of fog networks with optimized knapsack by symbiotic organisms search. In 2017 21st Conference of Open Innovations Association (FRUCT). IEEE, 2017: 278-283.

Rodriguez, M. A. Buyya, R. A responsive knapsack-based algorithm for resource provisioning and scheduling of scientific workflows in clouds, in: Parallel Processing (ICPP), 2015 44th International Conference on, Beijing, China, 1-4, IEEE, 2015: 839-848.

Guddeti, R.M., Buyya, R., et al. A hybrid bio-inspired algorithm for scheduling and resource management in cloud environment’, IEEE Transactions on Services Computing, 2017.

Rahim, S., Khan, S. A., Javaid, N., Shaheen, N., Iqbal, Z., Rehman, G. Towards multiple knapsack problem approach for home energy management in smart grid, in: Network-Based Information Systems (NBiS), 2015 18th International Conference on, Taipei, Taiwan, 2-4, IEEE, 2015: 48-52.

Chen, S. Wu, J. Lu, Z. A cloud computing resource scheduling policy based on genetic algorithm with multiple fitness, in: Computer and Information Technology (CIT), 2012 IEEE 12th International Conference on, Chengdu, China, 27-29, IEEE, 2012: 177-184.

Bitam, S. Zeadally, S. Mellouk, A. Fog computing job scheduling optimization based on bee’s swarm, Enterprise Information Systems 0, 2017: 1-25.

Sheff, I., Magrino, T., Liu, J., Myers, A. C., van Renesse, R. Safe serializable secure scheduling: Transactions and the trade-off between security and consistency, in: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, Vienna, Austria, 24-28, ACM, 2016: 229-241.

Sun, Y. Lin, F. Xu, H. Multi-objective optimization of resource scheduling in fog computing using an improved nsga-ii, Wireless Personal Communications, 2018: 1-17.

Calheiros, R. N., Ranjan, R., Beloglazov, A., De Rose, C. A., Buyya, R. Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evalu-ation of resource provisioning algorithms, Software: Practice and experience, 2011, 41 (1): 23-50.

Fernando, N. Loke, S. W. Rahayu, W. Mobile cloud computing: A survey, Future generation computer systems, , 2013, 29(1): 84-106.

Mach, P. Becvar, Z. Mobile edge computing: A survey on architecture and computation offloading, IEEE Communications Surveys & Tutorials, 2017, 19(3): 1628-1656.

Li, C., Xue, Y., Wang, J., Zhang, W., Li, T. Edge-oriented computing paradigms: A survey on architecture design and system management, ACM Computing Surveys (CSUR), 2018, 51(2): 39.

Liu, L., Chang, Z., Guo, X., Mao, S., Ristaniemi, T. Multiobjective optimization for computation offloading in fog computing, IEEE Internet of Things Journal, 2018, 5(1): 283-294.

Roman, R., Lopez, J., Mambo, M. Mobile edge computing, fog et al.: A survey and analysis of threats and challenges, Future Generation Computer Systems, 2018, 78: 680-698.

Tang, C., Wei, X., Xiao, S., Chen, W., Fang, W., Zhang, W., Hao, M. A mobile cloud-based scheduling strategy for industrial internet of things, IEEE Access, 2018, 6: 7262-7275.

Shah-Mansouri, H., Wong, V. W., Schober, R. Joint optimal pricing and task scheduling in mobile cloud computing systems, IEEE Transactions on Wireless Communications, 2017, 16(8): 5218-5232.

Zhang, J., Zhou, Z., Li, S., Gan, L., Zhang, X., Qi, L., Xu, X. Dou, W. Hybrid computation offloading for smart home automation in mobile cloud computing, Personal and Ubiquitous Computing, 2018, 22(1): 121-134.

Wang, T., Wei, X., Tang, C., Fan, J. Efficient multi-tasks scheduling algorithm in mobile cloud computing with time constraints’, Peer-to-Peer Networking and Applications, 2017, 11(4): 793-807.

Wang, Z., Zhao, Z., Min, G., Huang, X., Ni, Q., Wang, R. User mobility aware task assignment for mobile edge computing, Future Generation Computer Systems, 2018, 85: 1-8.

Zhang, J., Xia, W., Yan, F., Shen, L. Joint computation offloading and resource allocation optimization in heterogeneous networks with mobile edge computing, IEEE Access, 2018, 6: 19324-19337.

Elazhary, H. H., Sabbeh, S. F. The w5 framework for computation offloading in the internet of things, IEEE Access, 2018.

Chen, W., Wang, D. Li, K. Multi-user multi-task computation offloading in green mobile edge cloud computing, IEEE Transactions on Services Computing, 2018.

Wu, S., Mei, C., Jin, H. Wang, D. Android unikernel: Gearing mobile code offloading towards edge computing, Future Generation Computer Systems, 2018.

Liu, L. Chang, Z. Guo, X. Socially-aware dynamic computation offloading scheme for fog computing system with energy harvesting devices, IEEE Internet of Things Journal, 2018.

Rahbari, D., Nickray, M. Task offloading in mobile fog computing by classification and regression tree. Peer-to-Peer Networking and Applications, 2019, 1-19.

Tang, Z., Zhou, X., Zhang, F., Jia, W., Zhao, W. Migration modeling and learning algorithms for containers in fog computing, IEEE Transactions on Services Computing, 2018.

Mohan, N. Kangasharju, J. Placing it right! optimizing energy, processing, and transport in edge-fog clouds, Annals of Telecommunications, 2018: 1-12.

Lyu, X., Tian, H., Jiang, L., Vinel, A., Maharjan, S., Gjessing, S., Zhang, Y.Selective offloading in mobile edge computing for the green internet of things, IEEE Network, 2018, 32(1): 54-60.

Du, J., Zhao, L., Feng, J., Chu, X. Computation offloading and resource allocation in mixed fog/cloud computing systems with min-max fairness guarantee, IEEE Transactions on Communications, 2017.

Shuja, J., Gani, A., Ko, K., So, K., Mustafa, S., Madani, S.A. Khan, M.K. Sim-dom: A framework for simd instruction translation and offloading in heterogeneous mobile architectures, Transactions on Emerging Telecommunications Technologies, 2018, 29(4): e3174.



DOI: https://doi.org/10.30564/jeisr.v1i1.1135

Refbacks

  • There are currently no refbacks.
Copyright © 2019 Dadmehr Rahbari, Mohsen Nickray Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.