Deadline; Energy efficiency; Fog computing; Metaheuristic algorithm; Particle swarm optimization; Workflow scheduling; Annealing algorithm; Cloud environments; Makespan; Meta-heuristics algorithms; Particle swarm; Swarm optimization; Work-flows; Software; Theoretical Computer Science; Numerical Analysis; Computer Science Applications; Computational Theory and Mathematics; Computational Mathematics
Abstract :
[en] The Internet of Things (IoT) is constantly evolving. The variety of IoT applications has caused new demands to emerge on users’ part and competition between computing service providers. On the one hand, an IoT application may exhibit several important criteria, such as deadline and runtime simultaneously, and it is confronted with resource limitations and high energy consumption on the other hand. This has turned to adopting a computing environment and scheduling as a fundamental challenge. To resolve the issue, IoT applications are considered in this paper as a workflow composed of a series of interdependent tasks. The tasks in the same workflow (at the same level) are subject to priorities and deadlines for execution, making the problem far more complex and closer to the real world. In this paper, a hybrid Particle Swarm Optimization and Simulated Annealing algorithm (PSO–SA) is used for prioritizing tasks and improving fitness function. Our proposed method managed the task allocation and optimized energy consumption and makespan at the fog-cloud environment nodes. The simulation results indicated that the PSO–SA enhanced energy and makespan by 5% and 9% respectively on average compared with the baseline algorithm (IKH-EFT).
Disciplines :
Computer science
Author, co-author :
KHALEDIAN, Navid ✱; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > CritiX ; Department of Computer Engineering, Islamic Azad University, Sanandaj Branch, Sanandaj, Iran
Khamforoosh, Keyhan; Department of Computer Engineering, Islamic Azad University, Sanandaj Branch, Sanandaj, Iran
Akraminejad, Reza; Department of Computer Engineering and Information Technology, Payame Noor University, Tehran, Iran
Abualigah, Laith; Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, Jordan
Javaheri, Danial; Department of Computer Science and Engineering, Korea University, Seoul, South Korea
✱ These authors have contributed equally to this work.
External co-authors :
yes
Language :
English
Title :
An energy-efficient and deadline-aware workflow scheduling algorithm in the fog and cloud environment
Nazari A, Kordabadi M, Mohammadi R, Lal C (2023) EQRSRL: an energy-aware and QoS-based routing schema using reinforcement learning in IoMT. Wireless Netw 24:1–15
Mohammadi R, Nazari A, Daneshmand B (2023) An efficient routing schema for internet of underwater things/ocean of things. In: 2023 Wave electronics and its application in information and telecommunication systems (WECONF), pp. 1–8. IEEE
Nazari A, Tavassolian F, Abbasi M, Mohammadi R, Yaryab P (2022) An intelligent sdn-based clustering approach for optimizing iot power consumption in smart homes. Wireless Commun Mobile Comput. 10.1155/2022/8783380 DOI: 10.1155/2022/8783380
Samadi R, Nazari A, Seitz J (2023) Intelligent energy-aware routing protocol in mobile IoT networks based on SDN. IEEE Trans Green Commun Network. 10.1109/TGCN.2023.3296272 DOI: 10.1109/TGCN.2023.3296272
Cisco U (2020) Cisco annual internet report (2018–2023) white paper. Cisco: San Jose, CA, USA. 10(1):1–35
Goudarzi M, Wu H, Palaniswami M, Buyya R (2020) An application placement technique for concurrent IoT applications in edge and fog computing environments. IEEE Trans Mob Comput 20(4):1298–1311 DOI: 10.1109/TMC.2020.2967041
Nazari A, Mohammadi R, Niknami N, Jazaeri SS, Wu J (2023) The fuzzy-IAVOA energy-aware routing algorithm for SDN-based IoT networks. Int J Sensor Netw 42(3):156–169 DOI: 10.1504/IJSNET.2023.132543
Qiu H, Zhu K, Luong NC, Yi C, Niyato D, Kim DI (2022) Applications of auction and mechanism design in edge computing: a survey. IEEE Trans Cognit Commun Netw 8(2):1034–1058 DOI: 10.1109/TCCN.2022.3147196
Sadri AA, Rahmani AM, Saberikamarposhti M, Hosseinzadeh M (2022) Data reduction in fog computing and internet of things: a systematic literature survey. Internet of Things 13:100629 DOI: 10.1016/j.iot.2022.100629
Kumari N, Yadav A, Jana PK (2022) Task offloading in fog computing: a survey of algorithms and optimization techniques. Comput Netw 214:109137 DOI: 10.1016/j.comnet.2022.109137
Bansal S, Aggarwal H, Aggarwal M (2022) A systematic review of task scheduling approaches in fog computing. Trans Emerg Telecommun Technol 33(9):e4523 DOI: 10.1002/ett.4523
Nayak SC, Parida S, Tripathy C, Pattnaik PK (2022) An enhanced deadline constraint based task scheduling mechanism for cloud environment. J King Saud Univ Comput Inf Sci 34(2):282–294
Zhou G, Tian W, Buyya R (2023) Multi-search-routes-based methods for minimizing makespan of homogeneous and heterogeneous resources in Cloud computing. Future Gener Comput Syst 141:414–432 DOI: 10.1016/j.future.2022.11.031
Versluis L, Iosup A (2021) A survey of domains in workflow scheduling in computing infrastructures: community and keyword analysis, emerging trends, and taxonomies. Future Gener Comput Syst 123:156–177 DOI: 10.1016/j.future.2021.04.009
Chen G, Qi J, Sun Y, Hu X, Dong Z, Sun Y (2023) A collaborative scheduling method for cloud computing heterogeneous workflows based on deep reinforcement learning. Future Gener Comput Syst 141:284–297 DOI: 10.1016/j.future.2022.11.032
Ghafari R, Kabutarkhani FH, Mansouri N (2022) Task scheduling algorithms for energy optimization in cloud environment: a comprehensive review. Cluster Comput 25:1035 DOI: 10.1007/s10586-021-03512-z
Ijaz S, Munir EU, Ahmad SG, Rafique MM, Rana OF (2021) Energy-makespan optimization of workflow scheduling in fog–cloud computing. Computing 103(9):2033–2059 DOI: 10.1007/s00607-021-00930-0
Ajmal MS, Iqbal Z, Khan FZ, Bilal M, Mehmood RM (2021) Cost-based energy efficient scheduling technique for dynamic voltage and frequency scaling system in cloud computing. Sustain Energy Technol Assess 45:101210
Xu M, Buyya R (2020) Managing renewable energy and carbon footprint in multi-cloud computing environments. J Parallel Distrib Comput 135:191–202 DOI: 10.1016/j.jpdc.2019.09.015
Dayarathna M, Wen Y, Fan R (2015) Data center energy consumption modeling: a survey. IEEE Commun Surv Tutor 18(1):732–794 DOI: 10.1109/COMST.2015.2481183
Hussain M, Wei L-F, Rehman A, Abbas F, Hussain A, Ali M (2022) Deadline-constrained energy-aware workflow scheduling in geographically distributed cloud data centers. Future Gener Comput Syst 132:211–222 DOI: 10.1016/j.future.2022.02.018
Li H, Xu G, Wang D, Zhou M, Yuan Y, Alabdulwahab A (2022) Chaotic-nondominated-sorting owl search algorithm for energy-aware multi-workflow scheduling in hybrid clouds. IEEE Trans Sustain Comput 7:595 DOI: 10.1109/TSUSC.2022.3144357
Saurav SK, Benedict S (2021) A taxonomy and survey on energy-aware scientific workflows scheduling in large-scale heterogeneous architecture. In: 2021 6th international conference on inventive computation technologies (ICICT), 2021: IEEE, pp. 820–826
Azizi S, Shojafar M, Abawajy J, Buyya R (2022) Deadline-aware and energy-efficient IoT task scheduling in fog computing systems: a semi-greedy approach. J Netw Comput Appl 201:103333 DOI: 10.1016/j.jnca.2022.103333
Kishor A, Chakarbarty C (2022) Task offloading in fog computing for using smart ant colony optimization. Wireless Pers Commun 127(2):1683–1704 DOI: 10.1007/s11277-021-08714-7
Abd Elaziz M, Abualigah L, Attiya I (2021) Advanced optimization technique for scheduling IoT tasks in cloud-fog computing environments. Future Gener Comput Syst 124:142–154 DOI: 10.1016/j.future.2021.05.026
Abd Elaziz M, Abualigah L, Ibrahim RA, Attiya I (2021) IoT workflow scheduling using intelligent arithmetic optimization algorithm in fog computing. Comput Intell Neurosci. 10.1155/2021/9114113 DOI: 10.1155/2021/9114113
Sellami B, Hakiri A, Yahia SB, Berthou P (2022) Energy-aware task scheduling and offloading using deep reinforcement learning in SDN-enabled IoT network. Comput Netw 210:108957 DOI: 10.1016/j.comnet.2022.108957
Jayanetti A, Halgamuge S, Buyya R (2022) Deep reinforcement learning for energy and time optimized scheduling of precedence-constrained tasks in edge–cloud computing environments. Future Gener Comput Syst 137:14–30 DOI: 10.1016/j.future.2022.06.012
Tuli S, Poojara SR, Srirama SN, Casale G, Jennings NR (2021) COSCO: Container orchestration using co-simulation and gradient based optimization for fog computing environments. IEEE Trans Parallel Distrib Syst 33(1):101–116 DOI: 10.1109/TPDS.2021.3087349
Javaheri D, Gorgin S, Lee J-A, Masdari M (2022) An improved discrete harris hawk optimization algorithm for efficient workflow scheduling in multi-fog computing. Sustain Comput Inform Syst 36:100787
Ghobaei-Arani M, Shahidinejad A (2022) A cost-efficient IoT service placement approach using whale optimization algorithm in fog computing environment. Expert Syst Appl 200:117012 DOI: 10.1016/j.eswa.2022.117012
Ramzanpoor Y, Hosseini Shirvani M, Golsorkhtabaramiri M (2022) Multi-objective fault-tolerant optimization algorithm for deployment of IoT applications on fog computing infrastructure. Complex Intell Syst 8(1):361–392 DOI: 10.1007/s40747-021-00368-z
Al-Araji ZJ, Ahmad SSS, Kausar N, Farhani A, Ozbilge E, Cagin T (2022) Fuzzy theory in fog computing: review, taxonomy, and open issues. IEEE Access 10:126931–126956. 10.1109/ACCESS.2022.3225462 DOI: 10.1109/ACCESS.2022.3225462
Varmaghani A, Matin Nazar A, Ahmadi M, Sharifi A, Jafarzadeh Ghoushchi S, Pourasad Y (2021) DMTC: optimize energy consumption in dynamic wireless sensor network based on fog computing and fuzzy multiple attribute decision-making. Wireless Commun Mobile Comput. 10.1155/2021/9953416 DOI: 10.1155/2021/9953416
Taghizadeh J, Ghobaei-Arani M, Shahidinejad A (2021) An efficient data replica placement mechanism using biogeography-based optimization technique in the fog computing environment. J Ambient Intell Humaniz Comput 14:3691 DOI: 10.1007/s12652-021-03495-0
Iftikhar S et al (2023) HunterPlus: AI based energy-efficient task scheduling for cloud–fog computing environments. Internet of Things 21:100667 DOI: 10.1016/j.iot.2022.100667
Ahmed OH, Lu J, Xu Q, Ahmed AM, Rahmani AM, Hosseinzadeh M (2021) Using differential evolution and Moth-Flame optimization for scientific workflow scheduling in fog computing. Appl Soft Comput 112:107744 DOI: 10.1016/j.asoc.2021.107744
Kaur M, Aron R (2022) An energy-efficient load balancing approach for scientific workflows in fog computing. Wireless Person Commun 125:3549 DOI: 10.1007/s11277-022-09724-9
Hosseini Shirvani M, Noorian Talouki R (2022) Bi-objective scheduling algorithm for scientific workflows on cloud computing platform with makespan and monetary cost minimization approach. Complex Intell Syst 8(2):1085–1114 DOI: 10.1007/s40747-021-00528-1
Mokni M, Yassa S, Hajlaoui JE, Chelouah R, Omri MN (2022) Cooperative agents-based approach for workflow scheduling on fog-cloud computing. J Ambient Intell Humaniz Comput 13(10):4719–4738 DOI: 10.1007/s12652-021-03187-9
Han P, Du C, Chen J, Ling F, Du X (2021) Cost and makespan scheduling of workflows in clouds using list multiobjective optimization technique. J Syst Archit 112:101837 DOI: 10.1016/j.sysarc.2020.101837
Khaledian N, Khamforoosh K, Azizi S, Maihami V (2023) IKH-EFT: an improved method of workflow scheduling using the krill herd algorithm in the fog-cloud environment. Sustain Comput Inform Syst 37:100834
Delavar AG, Akraminejad R, Mozafari S (2022) HDECO: a method for Decreasing energy and cost by using virtual machine migration by considering hybrid parameters. Comput Commun 195:49–60 DOI: 10.1016/j.comcom.2022.08.006
Idrees AK, Al-Yaseen WL (2021) Distributed genetic algorithm for lifetime coverage optimisation in wireless sensor networks. Int J Adv Intell Paradig 18(1):3–24
Hazra A, Rana P, Adhikari M, Amgoth T (2023) Fog computing for next-generation internet of things: fundamental, state-of-the-art and research challenges. Comput Sci Rev 48:100549 DOI: 10.1016/j.cosrev.2023.100549
Laroui M, Nour B, Moungla H, Cherif MA, Afifi H, Guizani M (2021) Edge and fog computing for IoT: a survey on current research activities & future directions. Comput Commun 180:210–231 DOI: 10.1016/j.comcom.2021.09.003
Guevara JC, da Fonseca NL (2021) Task scheduling in cloud-fog computing systems. Peer-to-Peer Netw Appl 14(2):962–977 DOI: 10.1007/s12083-020-01051-9
Peng L, Dhaini AR, Ho P-H (2018) Toward integrated cloud-fog networks for efficient IoT provisioning: key challenges and solutions. Future Gener Comput Syst 88:606–613 DOI: 10.1016/j.future.2018.05.015
Nabi S, Ahmed M (2022) PSO-RDAL: particle swarm optimization-based resource-and deadline-aware dynamic load balancer for deadline constrained cloud tasks. J Supercomput 78:4624 DOI: 10.1007/s11227-021-04062-2
Auluck N, Azim A, Fizza K (2019) Improving the schedulability of real-time tasks using fog computing. IEEE Trans Serv Comput 15:372
