Metaheuristic Techniques for Resource Provisioning in Fog Computing
Keywords:
Fog computing, Resource provisioning, Metaheuristic techniques, Cloud computing, Resource management, Internet of Things (IoT).Abstract
The use of metaheuristic resource provisioning techniques in fog computing is examined in this work, with a focus on how they could enhance performance in computationally demanding and latency-sensitive applications that are common in the Internet of Things (IoT). The low-latency needs of Internet of Things applications are mostly met by fog computing, which brings cloud computing capabilities to the network edge. However, the limited processing power, memory, and storage of fog nodes make effective resource management more difficult. The employment of several heuristic and metaheuristic methods to optimise resource provisioning in fog settings is examined in this research. These methods include distance-based strategies, greedy algorithms, and Cat Swarm Optimisation. For handling the intricate trade-offs between many performance indicators, including energy consumption, latency, cost, and Quality of Service (QoS), these strategies are very beneficial.
The article highlights how metaheuristics may improve the scalability, flexibility, and efficiency of fog infrastructures by conducting a thorough evaluation of the body of research on their application to resource allocation problems in fog computing. The flexibility of metaheuristics enables them to adapt dynamically to the ever-changing requirements of Internet of Things applications, guaranteeing that resources are distributed optimally to satisfy the diverse requirements of fog nodes. These tactics aid in avoiding problems like resource overloading and underuse, which are frequent in settings with limited resources. Even with the encouraging promise of metaheuristic approaches, fog computing resource provisioning still faces several obstacles. This research found that one of the main challenges is the absence of real-time testbeds for assessing fog system performance in extensive, real-world settings. The intricacies and actual diversity of fog computing systems are not well captured by simulation tools like iFogSim and CloudSim, which are presently used in several research. In addition, more reliable solutions are needed for security issues, including data privacy and multi-level authentication at the fog layer. Fog computing's hierarchical structure, which divides work between fog nodes and the cloud, also calls for more complex partitioning schemes and algorithms to distribute work effectively.
This study concludes by showing that metaheuristic methods, especially in Internet of Things situations, provide a viable way to manage fog computing resources. Although these tactics may greatly increase QoS, energy efficiency, and scalability, further study is needed to overcome the current drawbacks, such as the creation of real-time testbeds and the improvement of security protocols. For fog-IoT systems to become more effective, safe, and sustainable in the future, the research urges further innovation in metaheuristic techniques.
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A. Zanella, N. Bui, A. Castellani, L. Vangelista, and M. Zorzi, “Internet of things for smart cities,” IEEE Internet Things J., vol. 1, no. 1, pp. 22–32, 2014.
M. Chiang and T. Zhang, “Fog and IoT: An overview of research opportunities,” IEEE Internet things J., vol. 3, no. 6, pp. 854–864, 2016.
S. Tuli et al., “HealthFog: An ensemble deep learning based Smart Healthcare System for Automatic Diagnosis of Heart Diseases in integrated IoT and fog computing environments,” Futur. Gener. Comput. Syst., vol. 104, pp. 187–200, 2020.
I. Martinez, A. S. Hafid, and A. Jarray, “Design, resource management, and evaluation of fog computing systems: a survey,” IEEE Internet Things J., vol. 8, no. 4, pp. 2494–2516, 2020.
A. Mehta, W. Tärneberg, C. Klein, J. Tordsson, M. Kihl, and E. Elmroth, “How beneficial are intermediate layer data centers in mobile edge networks?,” in 2016 IEEE 1st International Workshops on Foundations and Applications of Self* Systems (FAS* W), 2016, pp. 222–229.
S. El Kafhali and K. Salah, “Efficient and dynamic scaling of fog nodes for IoT devices,” J. Supercomput., vol. 73, no. 12, pp. 5261–5284, 2017.
M. Etemadi, M. Ghobaei-Arani, and A. Shahidinejad, “Resource provisioning for IoT services in the fog computing environment: An autonomic approach,” Comput. Commun., vol. 161, pp. 109–131, 2020.
M. Ghobaei-Arani, A. Souri, and A. A. Rahmanian, “Resource Management Approaches in Fog Computing: a Comprehensive Review,” J. Grid Comput., vol. 18, no. 1, pp. 1–42, 2020.
M. Faraji Mehmandar, S. Jabbehdari, and H. Haj Seyyed Javadi, “A dynamic fog service provisioning approach for IoT applications,” Int. J. Commun. Syst., vol. 33, no. 14, p. e4541, 2020.
S. K. Mishra, D. Puthal, J. J. P. C. Rodrigues, B. Sahoo, and E. Dutkiewicz, “Sustainable service allocation using a metaheuristic technique in a fog server for industrial applications,” IEEE Trans. Ind. Informatics, vol. 14, no. 10, pp. 4497–4506, 2018.
H. I. Kobo, A. M. Abu-Mahfouz, and G. P. Hancke, “A survey on software-defined wireless sensor networks: Challenges and design requirements,” IEEE access, vol. 5, pp. 1872–1899, 2017.
Samie, V. Tsoutsouras, L. Bauer, S. Xydis, D. Soudris, and J. Henkel, “Computation offloading and resource allocation for low-power IoT edge devices,” in 2016 IEEE 3rd world forum on internet of things (WF-IoT), 2016, pp. 7–12.
J. Santos, T. Wauters, B. Volckaert, and F. De Turck, “Resource provisioning in fog computing: From theory to practice†,” Sensors (Switzerland), vol. 19, no. 10, pp. 1–25, 2019.
K. Hong, D. Lillethun, U. Ramachandran, B. Ottenwälder, and B. Koldehofe, “Mobile fog: A programming model for large-scale applications on the internet of things,” in Proceedings of the second ACM SIGCOMM workshop on Mobile cloud computing, 2013, pp. 15–20.
M. Aazam and E.-N. Huh, “Dynamic resource provisioning through fog micro datacenter,” in 2015 IEEE international conference on pervasive computing and communication workshops (PerCom workshops), 2015, pp. 105–110.
M. Aazam and E.-N. Huh, “Fog computing micro datacenter based dynamic resource estimation and pricing model for IoT,” in 2015 IEEE 29th international conference on advanced information networking and applications, 2015, pp. 687–694.
O. Skarlat, S. Schulte, M. Borkowski, and P. Leitner, “Resource Provisioning for IoT Services in the Fog,” Comput. Commun., vol. 161, pp. 109–131, 2020.
I. Ben Lahmar and K. Boukadi, “Resource allocation in fog computing: A systematic mapping study,” in 2020 fifth international conference on fog and mobile edge computing (FMEC), 2020, pp. 86–93.
A. Kumar and S. Bawa, “A comparative review of meta-heuristic approaches to optimize the SLA violation costs for dynamic execution of cloud services,” Soft Comput., vol. 24, no. 6, pp. 3909–3922, 2020.
A. Singhrova and A. Anu, “Prioritized GA-PSO algorithm for efficient resource allocation in fog computing,” Indian J. Comput. Sci. Eng., vol. 11, no. 6, pp. 907–916, 2020.
J. Jijin, B.-C. Seet, and P. H. J. Chong, “Multi-objective optimization of task-to-node assignment in opportunistic fog RAN,” Electronics, vol. 9, no. 3, p. 474, 2020.
S. B. Akintoye and A. Bagula, “Improving quality-of-service in cloud/fog computing through efficient resource allocation,” Sensors, vol. 19, no. 6, p. 1267, 2019.
A. A. Aakizadeh and A. A. Aashi, “Distribution of virtual devices on the fog for delay and araffic aeduction,” in 2017 IEEE 4th International Conference on Knowledge-Based Engineering and Innovation (KBEI), 2017, pp. 492–496.
R. Eyckerman, S. Mercelis, J. Marquez-Barja, and P. Hellinckx, “Distributed task placement in the fog: a positioning paper,” in Advances on P2P, Parallel, Grid, Cloud and Internet Computing: Proceedings of the 14th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC-2019) 14, 2020, pp. 671–683.
H. Sami and A. Mourad, “Dynamic on-demand fog formation offering on-the-fly IoT service deployment,” IEEE Trans. Netw. Serv. Manag., vol. 17, no. 2, pp. 1026–1039, 2020.
P. Hosseinioun, M. Kheirabadi, S. R. K. Tabbakh, and R. Ghaemi, “A new energy-aware tasks scheduling approach in fog computing using hybrid meta-heuristic algorithm,” J. Parallel Distrib. Comput., vol. 143, pp. 88–96, 2020.
K. P. N. Jayasena and B. S. Thisarasinghe, “Optimized task scheduling on fog computing environment using meta heuristic algorithms,” in 2019 IEEE International Conference on Smart Cloud (SmartCloud), 2019, pp. 53–58.
M. Abdel-Basset, G. Manogaran, A. Gamal, and V. Chang, “A novel intelligent medical decision support model based on soft computing and IoT,” IEEE Internet Things J., vol. 7, no. 5, pp. 4160–4170, 2019.
H. Ben Hassen, W. Dghais, and B. Hamdi, “An E-health system for monitoring elderly health based on Internet of Things and Fog computing,” Heal. Inf. Sci. Syst., vol. 7, no. 1, p. 24, 2019.
V. Porkodi et al., “Resource provisioning for cyber–physical–social system in cloud-fog-edge computing using optimal flower pollination algorithm,” ieee access, vol. 8, pp. 105311–105319, 2020.
M. Abdel-Basset, D. El-Shahat, M. Elhoseny, and H. Song, “Energy-aware metaheuristic algorithm for industrial-Internet-of-Things task scheduling problems in fog computing applications,” IEEE Internet Things J., vol. 8, no. 16, pp. 12638–12649, 2020.
T. Bahreini, H. Badri, and D. Grosu, “Energy-aware capacity provisioning and resource allocation in edge computing systems,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 11520 LNCS, no. June, pp. 31–45, 2019.
J. Wang, Z. Feng, S. George, R. Iyengar, P. Pillai, and M. Satyanarayanan, “Towards scalable edge-native applications,” in Proceedings of the 4th ACM/IEEE Symposium on Edge Computing, 2019, pp. 152–165.
A. Yousefpour et al., “FOGPLAN: A lightweight QoS-aware dynamic fog service provisioning framework,” IEEE Internet Things J., vol. 6, no. 3, pp. 5080–5096, 2019.
N. Madan, A. W. Malik, A. U. Rahman, and S. D. Ravana, “On-demand resource provisioning for vehicular networks using flying fog,” Veh. Commun., vol. 25, p. 100252, 2020.
F. Tonini, B. M. Khorsandi, E. Amato, and C. Raffaelli, “Scalable edge computing deployment for reliable service provisioning in vehicular networks,” J. Sens. Actuator Networks, vol. 8, no. 4, 2019.
S. Dehnavi, H. R. Faragardi, M. Kargahi, and T. Fahringer, “A reliability-aware resource provisioning scheme for real-time industrial applications in a Fog-integrated smart factory,” Microprocess. Microsyst., vol. 70, no. May, pp. 1–14, 2019.
T. Rahman, X. Yao, G. Tao, H. Ning, and Z. Zhou, “Efficient Edge Nodes Reconfiguration and Selection for the Internet of Things,” IEEE Sens. J., vol. 19, no. 12, pp. 4672–4679, 2019.
H. Santos et al., “A multi-tier fog content orchestrator mechanism with quality of experience support,” Comput. Networks, vol. 177, no. May, p. 107288, 2020.
S. Mishra, M. N. Sahoo, S. Bakshi, and J. J. P. C. Rodrigues, “Dynamic Resource Allocation in Fog-Cloud Hybrid Systems Using Multicriteria AHP Techniques,” IEEE Internet Things J., vol. 7, no. 9, pp. 8993–9000, 2020.
U. Tadakamalla and D. A. Menasce, “Autonomic resource management using analytic models for fog/cloud computing,” Proc. - 2019 IEEE Int. Conf. Fog Comput. ICFC 2019, no. 2, pp. 69–79, 2019.
N. Siasi, M. Jasim, A. Aldalbahi, and N. Ghani, “Delay-aware SFC provisioning in hybrid fog-cloud computing architectures,” IEEE Access, vol. 8, pp. 167383–167396, 2020.
H. Baghban, C. Y. Huang, and C. H. Hsu, “Resource provisioning towards OPEX optimization in horizontal edge federation,” Comput. Commun., vol. 158, no. March, pp. 39–50, 2020.
F. H. Tseng, M. S. Tsai, C. W. Tseng, Y. T. Yang, C. C. Liu, and L. Der Chou, “A Lightweight Autoscaling Mechanism for Fog Computing in Industrial Applications,” IEEE Trans. Ind. Informatics, vol. 14, no. 10, pp. 4529–4537, 2018.
A. Zanni, S. Forsstrom, U. Jennehag, and P. Bellavista, “Elastic Provisioning of Internet of Things Services Using Fog Computing: An Experience Report,” Proc. - 6th IEEE Int. Conf. Mob. Cloud Comput. Serv. Eng. MobileCloud 2018, vol. 2018-January, pp. 17–22, 2018.
[68] P. G. Vinueza Naranjo, E. Baccarelli, and M. Scarpiniti, “Design and energy-efficient resource management of virtualized networked Fog architectures for the real-time support of IoT applications,” J. Supercomput., vol. 74, no. 6, pp. 2470–2507, 2018.
L. Peng, A. R. Dhaini, and P. H. Ho, “Toward integrated Cloud–Fog networks for efficient IoT provisioning: Key challenges and solutions,” Futur. Gener. Comput. Syst., vol. 88, pp. 606–613, 2018.
C. Li, J. Bai, and Y. Luo, “Efficient resource scaling based on load fluctuation in edge-cloud computing environment,” J. Supercomput., vol. 76, no. 9, pp. 6994–7025, 2020.
S. Rahman, T. Ahmed, M. Huynh, M. Tornatore, and B. Mukherjee, “Auto-Scaling VNFs Using Machine Learning to Improve QoS and Reduce Cost,” IEEE Int. Conf. Commun., vol. 2018-May, no. October 2020, pp. 1–6, 2018.
L. Chen, P. Zhou, L. Gao, and J. Xu, “Adaptive Fog Configuration for the Industrial Internet of Things,” IEEE Trans. Ind. Informatics, vol. 14, no. 10, pp. 4656–4664, 2018.
J. Santos, T. Wauters, B. Volckaert, and F. De Turck, “Towards Dynamic Fog Resource Provisioning for Smart City Applications,” 14th Int. Conf. Netw. Serv. Manag. CNSM 2018 Work. 1st Int. Work. High-Precision Networks Oper. Control. HiPNet 2018 1st Work. Segm. Routing Serv. Funct. Chain. SR+SFC 2018, pp. 290–294, 2018.
J. Yao and N. Ansari, “QoS-Aware Fog Resource Provisioning and Mobile Device Power Control in IoT Networks,” IEEE Trans. Netw. Serv. Manag., vol. 16, no. 1, pp. 167–175, 2019.
S. El Kafhali and K. Salah, “] S. El Kafhali, K. Salah, Performance modelling and analysis of Internet of Things enabled healthcare monitoring systems, IET Netw. 8 (1) (2018) 48–58.,” IET Networks, vol. 8, no. 1, pp. 48–58, 2019.
U. Tadakamalla and D. A. Menasc, “FogQN : An Analytic Model for Fog / Cloud Computing,” 2018.
M. Kalra and S. Singh, “A review of metaheuristic scheduling techniques in cloud computing,” Egypt. Informatics J., vol. 16, no. 3, pp. 275–295, 2015.
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