دانشگاه تربیت دبیر شهید رجائی

  اخبار و اعلانات

 آمار

تعداد نشریات13
تعداد شماره‌ها200
تعداد مقالات2,008
تعداد مشاهده مقاله2,680,125
تعداد دریافت فایل اصل مقاله1,937,343
Hashemi, S. M., Sahafi, A., Rahmani, A. M., Bohlouli, M.. (1402). Service and Energy Management in Fog Computing: A Taxonomy Approaches and Future Directions. فناوری آموزش, 12(1), 15-38. doi: 10.22061/jecei.2023.9482.624
S. M. Hashemi; A. Sahafi; A. M. Rahmani; M. Bohlouli. "Service and Energy Management in Fog Computing: A Taxonomy Approaches and Future Directions". فناوری آموزش, 12, 1, 1402, 15-38. doi: 10.22061/jecei.2023.9482.624
Hashemi, S. M., Sahafi, A., Rahmani, A. M., Bohlouli, M.. (1402). 'Service and Energy Management in Fog Computing: A Taxonomy Approaches and Future Directions', فناوری آموزش, 12(1), pp. 15-38. doi: 10.22061/jecei.2023.9482.624
Hashemi, S. M., Sahafi, A., Rahmani, A. M., Bohlouli, M.. Service and Energy Management in Fog Computing: A Taxonomy Approaches and Future Directions. فناوری آموزش, 1402; 12(1): 15-38. doi: 10.22061/jecei.2023.9482.624

Service and Energy Management in Fog Computing: A Taxonomy Approaches and Future Directions

Journal of Electrical and Computer Engineering Innovations (JECEI)
مقاله 2، دوره 12، شماره 1، فروردین 2024، صفحه 15-38    اصل مقاله (1.26 M)
نوع مقاله: Review paper
شناسه دیجیتال (DOI): 10.22061/jecei.2023.9482.624
نویسندگان
S. M. Hashemi1؛ A. Sahafi* 2؛ A. M. Rahmani3؛ M. Bohlouli4
1Department of Computer Engineering, Qeshm branch, Islamic Azad university, Qeshm, Iran.
2Department of Computer Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran.
3Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
4Department of Computer Science and Information Technology, Institute for Advanced Studies in Basic Sciences, Zanjan, Iran and Research and Innovation Department, Petanux GmbH, Bonn, Germany.
تاریخ دریافت: 22 بهمن 1401،  تاریخ بازنگری: 01 خرداد 1402،  تاریخ پذیرش: 06 خرداد 1402 
چکیده
Background and Objectives: Today, the increased number of Internet-connected smart devices require powerful computer processing servers such as cloud and fog and necessitate fulfilling requests and services more than ever before. The geographical distance of IoT devices to fog and cloud servers have turned issues such as delay and energy consumption into major challenges. However, fog computing technology has emerged as a promising technology in this field.

Methods: In this paper, service/energy management approaches are generally surveyed. Then, we explain our motivation for the systematic literature review procedure (SLR) and how to select the related works.

Results: This paper introduces four domains of service management and energy management, including Architecture, Resource Management, Scheduling management, and Service Management. Scheduling management has been used in 38% of the papers. Therefore, they have the highest service management and energy management. Also, Resource Management is the second domain that has been able to attract about 26% of the papers in service management and energy management.

Conclusion: About 81% of the fog computing papers simulated their approaches, and the others implemented their schemes using a testbed in the real environment. Furthermore, 30% of the papers presented an architecture or framework for their research, along with their research. In this systematic literature review, papers have been extracted from five valid databases, including IEEE Xplore, Wiley, Science Direct (Elsevier), Springer Link, and Taylor & Francis, from 2013 to 2022. We obtained 1596 papers related to the discussed subject. We filtered them and achieved 47 distinct studies. In the following, we analyze and discuss these studies; then we review the parameters of service quality in the papers, and ultimately, we present the benefits, drawbacks, and innovations of each study.
کلیدواژه‌ها
Fog computing؛ Internet of Things (IoT)؛ Systematic literature review (SLR)؛ Service management؛ Energy management
مراجع
[1] M. H. Kashani, M. Madanipour, M. Nikravan, P. Asghari, E. Mahdipour, "A systematic review of IoT in healthcare: Applications, techniques, and trends," J. Network Comput. Appl., 192: 103164, 2021.

[2] T. Samizadeh Nikoui, A. M. Rahmani, A. Balador, H. Seyyed Javadi, "Internet of things architecture challenges: A systematic review," Int. J. Commun. Syst., 34: e4678, 2021.

[3] H. Kashani, A. M. Rahmani, N. Jafari Navimipour, "Quality of service-aware approaches in fog computing," Int. J. Commun. Syst., 33: e4340, 2020.

[4] A. Sadri, A. M. Rahmani, M. Saberikamarposhti, M. Hosseinzadeh, "Fog data management: A vision, challenges, and future directions," J. Network Comput. Appl., 174: 102882, 2021.

[5] M. Rahimi, N. Jafari Navimipour, M. Hosseinzadeh, M. H. Moattar, A. Darwesh, "Toward the efficient service selection approaches in cloud computing," Kybernetes, 51: 1388-1412, 2022.

[6] M. Sheikh Sofla, H. Kashani, E. Mahdipour, R. Faghih Mirzaee, "Towards effective offloading mechanisms in fog computing," Multimedia Tools Appl., 81: 1997-2042, 2022.

[7] F. Davami, S. Adabi, A. Rezaee, A. M. Rahmani, "Fog-based architecture for scheduling multiple workflows with high availability requirement," Computing, 104: 169-208, 2022.

[8] N. S. Soulegan, B. Barekatain, B. S. Neysiani, "MTC: minimizing time and cost of cloud task scheduling based on customers and providers needs using genetic algorithm," Int. J. Intell. Syst. Appl. (IJISA), 13: 38-51, 2021.

[9] A. Najafizadeh, A. Salajegheh, A. M. Rahmani, A. Sahafi, "Task scheduling in fog computing: a survey," J. Adv. Comput. Res., 11: 33-56, 2020.

[10] A. Najafizadeh, A. Salajegheh, A. M. Rahmani, A. Sahafi, "Multi-objective task scheduling in cloud-fog computing using goal programming approach," Cluster Comput., 25: 141-165, 2022.

[11] M. Abbasi, E. Mohammadi-Pasand, M. R. Khosravi, "Intelligent workload allocation in IoT–Fog–cloud architecture towards mobile edge computing," Comput. Commun., 169: 71-80, 2021.

[12] M. Laroui, B. Nour, H. Moungla, M. A. Cherif, H. Afifi, M. Guizani, "Edge and fog computing for IoT: A survey on current research activities & future directions," Comput. Commun., 180: 210-231, 2021.

[13] D. Wang, D. Zhong, A. Souri, "Energy management solutions in the internet of things applications: Technical analysis and new research directions," Cognit. Syst. Res., 67: 33-49, 2021.

[14] A. Mijuskovic, A. Chiumento, R. Bemthuis, A. Aldea, P. Havinga, "Resource management techniques for cloud/fog and edge computing: An evaluation framework and classification," Sensors, 21: 1832, 2021.

[15] B. Costa, J. Bachiega, L. Rebouças, A. P. F. Araujo, "Orchestration in fog computing: A comprehensive survey," ACM Computing Surveys (CSUR), 55(2): 1-34, 2022.

[16] M. Gill, D. Singh, "A comprehensive study of simulation frameworks and research directions in fog computing," Comput. Sci. Rev., 40: 100391, 2021.

[17] M. R. Alizadeh, V. Khajehvand, A. M. Rahmani, E. Akbari, "Task scheduling approaches in fog computing: A systematic review," Int. J. Commun. Syst., 33: e4583, 2020.

[18] M. Ghobaei-Arani, A. Souri, A. A. Rahmanian, "Resource management approaches in fog computing: a comprehensive review," J. Grid Comput., 18: 1-42, 2020.

[19] M. Mukherjee, L. Shu, D. Wang, "Survey of fog computing: Fundamental, network applications, and research challenges," IEEE Commun. Surv. Tutorials, 20: 1826-1857, 2018.

[20] A. Dasgupta, A. Gill, "Fog computing challenges: a systematic review," in Proc. ACIS 2017: 79, 2017.

[21] V. Pande, C. Marlecha, S. Kayte, "A review-fog computing and its role in the internet of things," Int. J. Eng. Res. Appl., 6: 2248-96227, 2016.

[22] P. Hu, S. Dhelim, H. Ning, T. Qiu, "Survey on fog computing: architecture, key technologies, applications and open issues," J. Network Comput. Appl., 98: 27-42, 2017.

[23] G. Caiza, M. Saeteros, W. Oñate, M. V. Garcia, "Fog computing at industrial level, architecture, latency, energy, and security: A review," Heliyon, 6: e03706, 2020.

[24] R. Zolfaghari, A. Sahafi, A. M. Rahmani, R. Rezaei, "Application of virtual machine consolidation in cloud computing systems," Sustainable Comput. Inf. Syst., 30: 100524, 2021.

[25] M. Effatparvar, M. Dehghan, A. M. Rahmani, "A comprehensive survey of energy-aware routing protocols in wireless body area sensor networks," J. Med. Syst., 40: 1-27, 2016.

[26] J. Ghomi, A. M. Rahmani, N. N. Qader, "Load-balancing algorithms in cloud computing: A survey," J. Network Comput. Appl., 88: 50-71, 2017.

[27] B. Kitchenham et al., "Systematic literature reviews in software engineering–a tertiary study," Information and software technology, vol. 52, pp. 792-805, 2010.

[28] A. Vakili, N. J. Navimipour, "Comprehensive and systematic review of the service composition mechanisms in the cloud environments," J. Network Comput. Appl., 81: 24-36, 2017.

[29] F. Tao, L. Zhang, Y. Liu, Y. Cheng, L. Wang, X. Xu, "Manufacturing service management in cloud manufacturing: Overview and future research directions," J. Manuf. Sci. Eng., 137(4): 040912, 2015.

[30] Y. Sun, Z. Xiao, D. Bao, J. Zhao, "An architecture model of management and monitoring on cloud services resources," in Proc. 2010 3rd International Conference on Advanced Computer Theory and Engineering (ICACTE), 2010.

[31] R. Mahmud, R. Kotagiri, R. Buyya, "Fog computing: A taxonomy, survey and future directions," Internet of Everything: Algorithms, Methodologies, Technologies and Perspectives, Springer, 2018.

[32] E. Casalicchio, L. Silvestri, "Architectures for autonomic service management in cloud-based systems," presented at the 2011 IEEE Symposium on Computers and Communications (ISCC), Kerkyra, Greece, 2011.

[33] B. Shojaiemehr, A. M. Rahmani, N. N. Qader, "Cloud computing service negotiation: A systematic review," Comput. Stand. Interfaces, 55: 196-206, 2018.

[34] E. Jafarnejad Ghomi, M. Rahmani, N. Qader, "Service load balancing, task scheduling and transportation optimisation in cloud manufacturing by applying queuing system," Enterp. Inf. Syst., 13: 865-894, 2019.

[35] L. Liu, D. Qi, N. Zhou, Y. Wu, "A task scheduling algorithm based on classification mining in fog computing environment," Wireless Commun. Mobile Comput., 2102348: 1-12, 2018.

[36] R. M. B. Abadi, A. M. Rahmani, S. H. Alizadeh, "Challenges of server consolidation in virtualized data centers and open research issues: a systematic literature review," J. Supercomput., 76: 2876-2927, 2020.

[37] S. Gu, Q. Zhuge, J. Yi, J. Hu, E. H. M. Sha, "Optimizing task and data assignment on multi-core systems with multi-port SPMs," IEEE Trans. Parallel Distrib. Syst., 26: 2549-2560, 2014.

[38]W. Lin, S. Xu, L. He, J. Li, "Multi-resource scheduling and power simulation for cloud computing," Inf. Sci., 397: 168-186, 2017.

[39] M. A. A. Faruque, F. Ahourai, "A model-based design of cyber-physical energy systems," presented at the 2014 19th Asia and South Pacific Design Automation Conference (ASP-DAC), Singapore, 2014.

[40] F. Faruque, K. Vatanparvar, "Energy management-as-a-service over fog computing platform," IEEE Internet Things J., 3: 161-169, 2015.

[41] H. Barati, A. Movaghar, A. M. Rahmani, "EACHP: Energy aware clustering hierarchy protocol for large scale wireless sensor networks," Wireless Pers. Commun., 85: 765-789, 2015.

[42] S. E. Dashti, A. M. Rahmani, "Dynamic VMs placement for energy efficiency by PSO in cloud computing," J. Exp. Theor. Artif. Intell., 28: 97-112, 2016.

[43] K. Chronaki et al., "Task scheduling techniques for asymmetric multi-core systems," IEEE Trans. Parallel Distrib. Syst., 28: 2074-2087, 2016.

[44] Z. Sun et al., "An energy-efficient cross-layer-sensing clustering method based on intelligent fog computing in WSNs," IEEE Access, 7: 144165-144177, 2019.

[45] N. Kaur, S. K. Sood, "An energy-efficient architecture for the Internet of Things (IoT)," IEEE Syst. J., 11: 796-805, 2015.

[46] A. Rafiq, W. Ping, W. Min, M. Saleh, "Fog assisted 6TiSCH tri-layer network architecture for adaptive scheduling and energy-efficient offloading using rank-based Q-learning in smart industries," IEEE Sensors J., 21: 25489-25507, 2021.

[47] M. Dabbaghjamanesh, A. Kavousi-Fard, Z. Y. Dong, "A novel distributed cloud-fog based framework for energy management of networked microgrids," IEEE Trans. Power Syst., 35: 2847-2862, 2020.

[48] G. Rathee, R. Sandhu, H. Saini, M. Sivaram, V. Dhasarathan, "A trust computed framework for IoT devices and fog computing environment," Wireless Networks, 26: 2339-2351, 2020.

[49] Z. Ren, T. Lu, X. Wang, W. Guo, G. Liu, S. Chang, "Resource scheduling for delay-sensitive application in three-layer fog-to-cloud architecture," Peer-to-Peer Networking Appl., 13: 1474-1485, 2020.

[50] S. K. Mani, I. Meenakshisundaram, "Improving quality-of-service in fog computing through efficient resource allocation," Comput. Intell., 36: 1527-1547, 2020.

[51] X. Chen, Y. Zhou, L. Yang, L. Lv, "User satisfaction oriented resource allocation for fog computing: A mixed-task paradigm," IEEE Trans. Commun., 68: 6470-6482, 2020.

[52] L. Ni, J. Zhang, C. Jiang, C. Yan, K. Yu, "Resource allocation strategy in fog computing based on priced timed petri nets," IEEE Internet Things J., 4: 1216-1228, 2017.

[53] L. Gu, D. Zeng, S. Guo, A. Barnawi, Y. Xiang, "Cost efficient resource management in fog computing supported medical cyber-physical system," IEEE Trans. Emerging Top. Comput., 5: 108-119, 2015.

[54] R. Basir, S. Qaisar, M. Ali, M. Naeem, A. Anpalagan, "Energy efficient resource allocation in cache-enabled fog networks," Trans. Emerging Telecommun. Technol., 32: e4343, 2021.

[55] S. Lu, J. Wu, Y. Duan, N. Wang, J. Fang, "Towards cost-efficient resource provisioning with multiple mobile users in fog computing," J. Parallel Distrib. Computing, 146: 96-106, 2020.

[56] Y. Sun, N. Zhang, "A resource-sharing model based on a repeated game in fog computing," Saudi J. Biol. Sci., 24: 687-694, 2017.

[57] S. Kalantary, J. Akbari Torkestani, A. Shahidinejad, "Resource discovery in the internet of things integrated with fog computing using Markov learning model," J. Supercomput., 77: 13806-13827, 2021.

[58] S. Rodrigo, N. Ls, "Location of fog nodes for reduction of energy consumption of end-user devices," IEEE Trans. Green Commun. Networking, 4: 593-605, 2020.

[59] M. Guo, L. Li, Q. Guan, "Energy-efficient and delay-guaranteed workload allocation in IoT-edge-cloud computing systems," IEEE Access, 7: 78685-78697, 2019.

[60] N. Mazumdar, A. Nag, J. P. Singh, "Trust-based load-offloading protocol to reduce service delays in fog-computing-empowered IoT," Comput. Electr. Eng., 93: 107223, 2021.

[61] D. Tychalas, H. Karatza, "A scheduling algorithm for a fog computing system with bag-of-tasks jobs: Simulation and performance evaluation," Simul. Modell. Pract. Theory, 98: 101982, 2020.

[62] S. Bitam, S. Zeadally, A. Mellouk, "Fog computing job scheduling optimization based on bees swarm," Enterp. Inf. Syst., 12: 373-397, 2018.

[63] R. O. Aburukba, M. AliKarrar, T. Landolsi, K. El-Fakih, "Scheduling internet of things requests to minimize latency in hybrid Fog–Cloud computing," Future Gener. Comput. Syst., 111: 539-551, 2020.

[64] S. Chouikhi, M. Esseghir, L. Merghem-Boulahia, "Energy consumption scheduling as a fog computing service in smart grid," IEEE Trans. Serv. Comput., 16: 1144-1157, 2022.

[65] R. Xie, Q. Tang, C. Liang, F. R. Yu, T. Huang, "Dynamic computation offloading in IoT fog systems with imperfect channel-state information: A POMDP approach," IEEE Internet Things J., 8: 345-356, 2020.

[66] X. Li, Z. Zang, F. Shen, Y. Sun, "Task offloading scheme based on improved contract net protocol and beetle antennae search algorithm in fog computing networks," Mobile Networks Appl., 25:  2517-2526, 2020.

[67] H. Mahini, A. M. Rahmani, S. M. Mousavirad, "An evolutionary game approach to IoT task offloading in fog-cloud computing," J. Supercomput., 77: 5398-5425, 2021.

[68] D. Wang, Z. Liu, X. Wang, Y. Lan, "Mobility-aware task offloading and migration schemes in fog computing networks," IEEE Access, 7: 43356-43368, 2019.

[69] C. Ren, X. Lyu, W. Ni, H. Tian, and R. P. Liu, "Distributed online learning of fog computing under nonuniform device cardinality," IEEE Internet Things J., 6: 1147-1159, 2018.

[70] O. K. Shahryari, H. Pedram, V. Khajehvand, M. D. TakhtFooladi, "Energy and task completion time trade-off for task offloading in fog-enabled IoT networks," Pervasive Mob. Comput., 74: 101395, 2021.

[71] M. Yang, H. Ma, S. Wei, Y. Zeng, Y. Chen, Y. Hu, "A multi-objective task scheduling method for fog computing in cyber-physical-social services," IEEE Access, 8: 65085-65095, 2020.

[72] J. Xu, Z. Hao, R. Zhang, X. Sun, "A method based on the combination of laxity and ant colony system for cloud-fog task scheduling," IEEE Access, 7: 116218-116226, 2019.

[73] R. Zhu, S. Li, P. Wang, M. Xu, S. Yu, "Energy-efficient deep reinforced traffic grooming in elastic optical networks for cloud–fog computing," IEEE Internet Things J., 8: 12410-12421, 2021.

[74] M. Hussain, L. F. Wei, A. Lakhan, S. Wali, S. Ali, A. Hussain, "Energy and performance-efficient task scheduling in heterogeneous virtualized cloud computing," Sustainable Comput. Inf. Syst., vol. 30: 100517, 2021.

[75] M. Abd Elaziz, L. Abualigah, I. Attiya, "Advanced optimization technique for scheduling IoT tasks in cloud-fog computing environments," Future Gener. Comput. Syst., 124: 142-154, 2021.

[76] M. Abdel-Basset, R. Mohamed, R. K. Chakrabortty, M. J. Ryan, "IEGA: an improved elitism-based genetic algorithm for task scheduling problem in fog computing," Int. J. Intell. Syst., 36: 4592-4631, 2021.

[77] Z. A. Khan et al., "Energy management in smart sectors using fog-based environment and meta-heuristic algorithms," IEEE Access, 7:  157254-157267, 2019.

[78] L. Stavrinides, H. D. Karatza, "A hybrid approach to scheduling real-time IoT workflows in fog and cloud environments," Multimedia Tools Appl., 78: 24639-24655, 2019.

[79] M. Ayoubi, M. Ramezanpour, R. Khorsand, "An autonomous IoT service placement methodology in fog computing," Softw: Pract. Exper., 51: 1097-1120, 2021.

[80] V. B. Souza et al., "Towards a proper service placement in combined Fog-to-Cloud (F2C) architectures," Future Gener. Comput. Syst., 87: 1-15, 2018.

[81] J. Li et al., "Service migration in fog computing enabled cellular networks to support real-time vehicular communications," IEEE Access, 7: 13704-13714, 2019.

[82] D. Zhao, G. Sun, D. Liao, S. Xu, V. Chang, "Mobile-aware service function chain migration in cloud–fog computing," Future Gener. Comput. Syst., 96: 591-604, 2019.

[83] O. Skarlat, M. Nardelli, S. Schulte, M. Borkowski, P. Leitner, "Optimized IoT service placement in the fog," Serv. Oriented Comput. Appl., 11: 427-443, 2017.

[84] B. V. Natesha, G. Ram, "Adopting elitism-based Genetic Algorithm for minimizing multi-objective problems of IoT service placement in fog computing environment," J. Network Comput. Appl., 178: 102972, 2021.

[85] S. O. Ogundoyin, I. A. Kamil, "A trust management system for fog computing services," Internet of Things, 14: 100382, 2021.

[86] M. Ghobaei-Arani, A. Shahidinejad, "A cost-efficient IoT service placement approach using whale optimization algorithm in fog computing environment," Expert Syst. Appl., 200: 117012, 2022.

[87] N. Sarrafzade, R. Entezari-Maleki, L. Sousa, "A genetic-based approach for service placement in fog computing," J. Supercomput., 78: 10854-10875, 2022.

[88] M. Salimian, M. Ghobaei-Arani, A. Shahidinejad, "An evolutionary multi-objective optimization technique to deploy the IoT Services in fog-enabled Networks: an autonomous approach," Appl. Artif. Intell., 36: 2008149, 2022.

[89] A. Yousefpour et al., "Fogplan: A lightweight qos-aware dynamic fog service provisioning framework," IEEE Internet Things J., 6: 5080-5096, 2019.

[90] Y. Chen, Y. Wang, D. Gong, "Fog computing support scheme based on fusion of location service and privacy preservation for QoS enhancement," Peer-to-Peer Networking Appl., 12: 1480-1488, 2019.

 

آمار
تعداد مشاهده مقاله: 820
تعداد دریافت فایل اصل مقاله: 406


سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب