Over the last decade, the usage of Internet of Things (IoT) enabled applications, such as healthcare, intelligent vehicles, and smart homes, has increased progressively. These IoT applications generate delayed- sensitive data and requires quick resources for execution. Recently, software-defined networks (SDN) offer an edge computing paradigm (e.g., fog computing) to run these applications with minimum end-to-end delays. Offloading and scheduling are promising schemes of edge computing to run delay-sensitive IoT applications while satisfying their requirements. However, in the dynamic environment, existing offloading and scheduling techniques are not ideal and decrease the performance of such applications. This article formulates joint and scheduling problems into combinatorial integer linear programming (CILP). We propose a joint task offloading and scheduling (JTOS) framework based on the problem. JTOS consists of task offloading, sequencing, scheduling, searching, and failure components. The study's goal is to minimize the hybrid delay of all applications. The performance evaluation shows that JTOS outperforms all existing baseline methods in hybrid delay for all applications in the dynamic environment. The performance evaluation shows that JTOS reduces the processing delay by 39% and the communication delay by 35% for IoT applications compared to existing schemes.

College of Agriculture Al Muthanna University Samawah 66001 Iraq

College of Computer Science and Information Technology University of Anbar Anbar 31001 Iraq

College of Engineering University of Warith Al Anbiyaa Karbala 56001 Iraq

Department of Computer Science Dijlah University College Baghdad 00964 Iraq

Department of Cybernetics and Biomedical Engineering VSB Technical University of Ostrava 708 00 Ostrava Czech Republic

Department of Cybersecurity and Computer Science Dawood University of Engineering and Technology Karachi City 74800 Sindh Pakistan

Department of Medical Instruments Engineering Techniques Al Farahidi University Baghdad 10021 Iraq

Department of Telecommunications VSB Technical University of Ostrava 708 00 Ostrava Czech Republic

Industrial Engineering Brose Group Prumyslovy Park 302 742 21 Koprivnice Czech Republic

Institute of Artificial intelligence and Blockchain Guangzhou University Waihuan West Road University Town Guangzhou 510006 China

See more in PubMed

De D., Mukherjee A., Roy D.G. Power and Delay Efficient Multilevel Offloading Strategies for Mobile Cloud Computing. Wirel. Pers. Commun. 2020;112:2159–2186. doi: 10.1007/s11277-020-07144-1. DOI

Shahryari O.K., Pedram H., Khajehvand V., TakhtFooladi M.D. Energy-Efficient and Delay-Guaranteed Computation Offloading for Fog-Based IoT Networks. Comput. Netw. 2020;182:107511. doi: 10.1016/j.comnet.2020.107511. DOI

Aburukba R.O., AliKarrar M., Landolsi T., El-Fakih K. Scheduling Internet of Things requests to minimize latency in hybrid Fog–Cloud? computing. Future Gener. Comput. Syst. 2020;111:539–551. doi: 10.1016/j.future.2019.09.039. DOI

Lin C., Han G., Qi X., Guizani M., Shu L. A Distributed Mobile Fog Computing Scheme for Mobile Delay-Sensitive Applications in SDN-Enabled Vehicular Networks. IEEE Trans. Veh. Technol. 2020;69:5481–5493. doi: 10.1109/TVT.2020.2980934. DOI

Fan Q., Ansari N. Application aware workload allocation for edge computing-based IoT. IEEE Internet Things J. 2018;5:2146–2153. doi: 10.1109/JIOT.2018.2826006. DOI

Kavitha B., Vallikannu R., Sankaran K.S. Delay-aware concurrent data management method for IoT collaborative mobile edge computing environment. Microprocess. Microsystems. 2020;74:103021. doi: 10.1016/j.micpro.2020.103021. DOI

Chanyour T., El Ghmary M., Hmimz Y., Cherkaoui Malki M.O. Energy-efficient and delay-aware multitask offloading for mobile edge computing networks. Trans. Emerg. Telecommun. Technol. 2019;33:e3673. doi: 10.1002/ett.3673. DOI

Chamola V., Tham C.K., Gurunarayanan S., Ansari N. An optimal delay aware task assignment scheme for wireless SDN networked edge cloudlets. Future Gener. Comput. Syst. 2020;102:862–875.

Roy P., Sarker S., Razzaque M.A., Hassan M.M., AlQahtani S.A., Aloi G., Fortino G. AI-enabled mobile multimedia service instance placement scheme in mobile edge computing. Comput. Netw. 2020;182:107573. doi: 10.1016/j.comnet.2020.107573. DOI

Gu X., Zhang G., Cao Y. Cooperative mobile edge computing-cloud computing in Internet of vehicle: Architecture and energy-efficient workload allocation. Trans. Emerg. Telecommun. Technol. 2020;32:e4095. doi: 10.1002/ett.4095. DOI

Zhang L., Ansari N. Latency-aware IoT Service Provisioning in UAV-aided Mobile Edge Computing Networks. IEEE Internet Things J. 2020;7:10573–10580. doi: 10.1109/JIOT.2020.3005117. DOI

Xia Q., Lou Z., Xu W., Xu Z. Near-Optimal and Learning-Driven Task Offloading in a 5G Multi-Cell Mobile Edge Cloud. Comput. Netw. 2020;176:107276. doi: 10.1016/j.comnet.2020.107276. DOI

Abbasi M., Pasand E.M., Khosravi M.R. Workload Allocation in IoT-Fog-Cloud Architecture Using a Multi-Objective Genetic Algorithm. J. Grid Comput. 2020;18:43–56. doi: 10.1007/s10723-020-09507-1. DOI

Ying Wah T., Gopal Raj R., Lakhan A. A novel cost-efficient framework for critical heartbeat task scheduling using the Internet of medical things in a fog cloud system. Sensors. 2020;20:441. PubMed PMC

Arikumar K., Natarajan V. Evolution in Computational Intelligence. Springer; Berlin/Heidelberg, Germany: 2021. FIoT: A QoS-Aware Fog-IoT Framework to Minimize Latency in IoT Applications via Fog Offloading; pp. 551–559.

Siasi N., Jasim M., Aldalbahi A., Ghani N. Delay-Aware SFC Provisioning in Hybrid Fog-Cloud Computing Architectures. IEEE Access. 2020;8:167383–167396. doi: 10.1109/ACCESS.2020.3021354. DOI

Naha R.K., Garg S., Chan A., Battula S.K. Deadline-based dynamic resource allocation and provisioning algorithms in fog-cloud environment. Future Gener. Comput. Syst. 2020;104:131–141. doi: 10.1016/j.future.2019.10.018. DOI

Lakhan A., Li X. Transient fault aware application partitioning computational offloading algorithm in microservices based mobile cloudlet networks. Computing. 2020;102:105–139. doi: 10.1007/s00607-019-00733-4. DOI

Lakhan A., Khan F.A., Abbasi Q.H. Dynamic Content and Failure Aware Task Offloading in Heterogeneous Mobile Cloud Networks; Proceedings of the 2019 International Conference on Advances in the Emerging Computing Technologies (AECT); Al Madinah Al Munawwarah, Saudi Arabia. 10 February 2020; pp. 1–6.

Lakhan A., Sajnani D.K., Tahir M., Aamir M., Lodhi R. Delay sensitive application partitioning and task scheduling in mobile edge cloud prototyping; Proceedings of the International Conference on 5G for Ubiquitous Connectivity; Levi, Finland. 26–27 November 2014; Berlin/Heidelberg, Germany: Springer; 2018. pp. 59–80.

Mahesar A.R., Lakhan A., Sajnani D.K., Jamali I.A. Hybrid delay optimization and workload assignment in mobile edge cloud networks. Open Access Libr. J. 2018;5:1–12. doi: 10.4236/oalib.1104854. DOI

Lakhan A., Xiaoping L. Energy aware dynamic workflow application partitioning and task scheduling in heterogeneous mobile cloud network; Proceedings of the 2018 International Conference on Cloud Computing, Big Data and Blockchain (ICCBB); Fuzhou, China. 15–17 November 2018; pp. 1–8.

Lakhan A., Li X. Content Aware Task Scheduling Framework for Mobile Workflow Applications in Heterogeneous Mobile-Edge-Cloud Paradigms: CATSA Framework; Proceedings of the 2019 IEEE International Conference on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom); Xiamen, China. 16–18 December 2019; pp. 242–249.

Pham Q.V., Fang F., Ha V.N., Piran M.J., Le M., Le L.B., Hwang W.J., Ding Z. A survey of multi-access edge computing in 5G and beyond: Fundamentals, technology integration, and state-of-the-art. IEEE Access. 2020;8:116974–117017. doi: 10.1109/ACCESS.2020.3001277. DOI

Sajnani D.K., Mahesar A.R., Lakhan A., Jamali I.A. Latency Aware and Service Delay with Task Scheduling in Mobile Edge Computing. Commun. Netw. 2018;10:127. doi: 10.4236/cn.2018.104011. DOI

Ma X., Wang S., Zhang S., Yang P., Lin C., Shen X.S. Cost-efficient resource provisioning for dynamic requests in cloud assisted mobile edge computing. IEEE Trans. Cloud Comput. 2019;9:968–980. doi: 10.1109/TCC.2019.2903240. DOI

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. doi: 10.1109/ACCESS.2018.2819690. DOI

Hossain M.D., Sultana T., Nguyen V., Nguyen T.D., Huynh L.N., Huh E.N. Fuzzy Based Collaborative Task Offloading Scheme in the Densely Deployed Small-Cell Networks with Multi-Access Edge Computing. Appl. Sci. 2020;10:3115. doi: 10.3390/app10093115. DOI

Dab B., Aitsaadi N., Langar R. A novel joint offloading and resource allocation scheme for mobile edge computing; Proceedings of the 2019 16th IEEE Annual Consumer Communications & Networking Conference (CCNC); Las Vegas, NV, USA. 11–14 January 2019; pp. 1–2.

Mohan N., Kangasharju J. Edge-Fog cloud: A distributed cloud for Internet of Things computations; Proceedings of the 2016 Cloudification of the Internet of Things (CIoT); Paris, France. 23–25 November 2016; pp. 1–6.

Brogi A., Forti S. QoS-aware deployment of IoT applications through the fog. IEEE Internet Things J. 2017;4:1185–1192. doi: 10.1109/JIOT.2017.2701408. DOI

Brogi A., Forti S., Ibrahim A. How to best deploy your fog applications, probably; Proceedings of the 2017 IEEE 1st International Conference on Fog and Edge Computing (ICFEC); Madrid, Spain. 14–15 May 2017; pp. 105–114.

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. Softw. Pract. Exp. 2017;47:1275–1296. doi: 10.1002/spe.2509. DOI

Forti S., Pagiaro A., Brogi A. Simulating FogDirector Application Management. Simul. Model. Pract. Theory. 2020;101:102021. doi: 10.1016/j.simpat.2019.102021. DOI

Qayyum T., Malik A.W., Khattak M.A.K., Khalid O., Khan S.U. FogNetSim++: A toolkit for modeling and simulation of distributed fog environment. IEEE Access. 2018;6:63570–63583. doi: 10.1109/ACCESS.2018.2877696. DOI

Liu X., Fan L., Xu J., Li X., Gong L., Grundy J., Yang Y. FogWorkflowSim: An automated simulation toolkit for workflow performance evaluation in fog computing; Proceedings of the 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE); San Diego, CA, USA. 11–15 November 2019; pp. 1114–1117.

Lera I., Guerrero C., Juiz C. YAFS: A simulator for IoT scenarios in fog computing. IEEE Access. 2019;7:91745–91758. doi: 10.1109/ACCESS.2019.2927895. DOI

Forti S., Ibrahim A., Brogi A. Mimicking FogDirector application management. SICS Softw.-Intensive -Cyber-Phys. Syst. 2019;34:151–161. doi: 10.1007/s00450-019-00403-y. DOI

Tuli S., Mahmud R., Tuli S., Buyya R. Fogbus: A blockchain-based lightweight framework for edge and fog computing. J. Syst. Softw. 2019;154:22–36. doi: 10.1016/j.jss.2019.04.050. DOI

Calvo-Fullana M., Mox D., Pyattaev A., Fink J., Kumar V., Ribeiro A. ROS-NetSim: A Framework for the Integration of Robotic and Network Simulators. IEEE Robot. Autom. Lett. 2021;6:1120–1127. doi: 10.1109/LRA.2021.3056347. DOI