Deep Learning-based Resource Prediction and Mutated Leader Algorithm Enabled Load Balancing in Fog Computing

Deep Learning-based Resource Prediction and Mutated Leader Algorithm Enabled Load Balancing in Fog Computing

Автор: Shruthi G., Monica R. Mundada, S. Supreeth, Bryan Gardiner

Журнал: International Journal of Computer Network and Information Security @ijcnis

Статья в выпуске: 4 vol.15, 2023 года.

Бесплатный доступ

Load balancing plays a major part in improving the performance of fog computing, which has become a requirement in fog layer for distributing all workload in equal manner amongst the current Virtual machines (VMs) in a segment. The distribution of load is a complicated process as it consists of numerous users in fog computing environment. Hence, an effectual technique called Mutated Leader Algorithm (MLA) is proposed for balancing load in fogging environment. Firstly, fog computing is initialized with fog layer, cloud layer and end user layer. Then, task is submitted from end user under fog layer with cluster of nodes. Afterwards, load balancing process is done in each cluster and the resources for each VM are predicted using Deep Residual Network (DRN). The load balancing is accomplished by allocating and reallocating the task from the users to the VMs in the cloud based on the resource constraints optimally using MLA. Here, the load balancing is needed for optimizing resources and objectives. Lastly, if VMs are overloaded and then the jobs are pulled from associated VM and allocated to under loaded VM. Thus the proposed MLA achieved minimum execution time is 1.472ns, cost is $69.448 and load is 0.0003% respectively.

Еще

Fog Computing, Mutated Leader Algorithm (MLA), Virtual Machine (VM), Deep Residual Network (DRN), Load Balancing

Короткий адрес: https://sciup.org/15018636

IDR: 15018636   |   DOI: 10.5815/ijcnis.2023.04.08

Список литературы Deep Learning-based Resource Prediction and Mutated Leader Algorithm Enabled Load Balancing in Fog Computing

  • Devagnanam J,Elango N M, "Optimal Resource Allocation of Cluster using Hybrid Grey Wolf and Cuckoo Search Algorithm in Cloud Computing", Journal of Networking and Communication Systems, vol.3, no.1, pp.31-40, 2020.
  • Asif Ali Laghari, Awais Khan Jumani and Rashid Ali Laghari,R "Review and state of art of fog computing", Archives of Computational Methods in Engineering, vol.28, no.5, pp.3631-3643, 2021.
  • Xavi Masip-Bruin, Eva Marín-Tordera, Ghazal Tashakor, Admela Jukan and Guang-Jie Ren, "Foggy clouds and cloudy fogs: a real need for coordinated management of fog-to-cloud computing systems", IEEE Wireless Communications, vol.23, no.5, pp.120-128, 2016.
  • Vishal Kumar, Asif Ali Laghari, Shahid Karim, Muhammad Shakir and Ali Anwar Brohi, "Comparison of fog computing & cloud computing", International Journal of Mathematical Sciences and Computing, vol.1, pp.31-41, 2019.
  • Valeria Cardellini, Gabriele Mencagli, Domenico Talia and Massimo Torquati, "New landscapes of the data stream processing in the era of fog computing", Future Generation Computer Systems, vol.99, pp.646-650, 2019.
  • Cheng Huang, Rongxing Lu, and Kim-Kwang Raymond Choo, "Vehicular fog computing: architecture, use case, and security and forensic challenges", IEEE Communications Magazine, vol.55, no.11, pp.105-111, 2017.
  • Michaela Iorga, Larry Feldman, Robert Barton, Michael J. Martin, Nedim Goren and f Mahmoudi, "Fog computing conceptual model", National Institute of Standards and Technology Special Publication, 2018.
  • Mohammad Wazid, Ashok Kumar Das,Rasheed Hussain, Giancarlo Succi and Joel J. P. C. Rodrigues, "Authentication in cloud-driven IoT-based big data environment: Survey and outlook", Journal of systems architecture, vol. 97, pp.185-196, 2019.
  • Yang Liu, Jonathan E.Fieldsend and Geyong Min, G. "A framework of fog computing: Architecture, challenges, and optimization", IEEE Access, vol.5, pp.25445-25454, 2017.
  • Ashok Kumar C,Vimala R, "Load Balancing in Cloud Environment Exploiting Hybridization of Chicken Swarm and Enhanced Raven Roosting Optimization Algorithm", Multimedia Research, vol.3, no.1, pp.45-55, 2020.
  • Mohammad Riyaz Belgaum, Shahrulniza Musa, Muhammad Mansoor Alam and mazliham Mohd Suud, "A systematic review of load balancing techniques in software-defined networking" IEEE Access, vol. 8, pp.98612-98636, 2020.
  • Changlong Li, Hang Zhuang, Qingfeng Wang and Xuehai Zhou, "SSLB: self-similarity-based load balancing for large-scale fog computing", Arabian Journal for Science and Engineering, vol.43, no.12, pp.7487-7498, 2018.
  • Abuhamdah, A and Al-Shabi, M, "Hybrid Load Balancing Algorithm For Fog Computing Environment". International Journal of Software Engineering and Computer Systems, vol. 8, no.1, pp.11-21, 2022.
  • Muhammad Junaid, Adnan Sohail, Rao naveed Bin Rais, Adeel Ahmed, Osman Khalid, Imran Ali Khan, Syed Sajid Hussain and Naveed Ejaz, "Modeling an optimized approach for load balancing in cloud", IEEE Access, vol.8, pp.173208-173226, 2020.
  • Mandeep Kaur1 and Rajni Aron, “An Energy-Efficient Load Balancing Approach for Scientific Workflows in Fog Computing", Wireless Personal Communications, pp.1-25, 2022.
  • Daniel E. Eisenbud, Cheng Yi, Carlo Contavalli, Cody Smith, Roman Kononov, Eric Mann-Hielscher, Ardas Cilingiroglu, Bin Cheyney,Wentao Shang and Jinnah Dylan Hosein, "Maglev: A fast and reliable software network load balancer", In proceedings of 13th USENIX Symposium on Networked Systems Design and Implementation (NSDI 16), pp. 523-535, 2016.
  • Sambit Kumar Mishra, Bibhudatta Sahoo and Priti Paramita Parida, "Load balancing in cloud computing: a big picture", Journal of King Saud University-Computer and Information Sciences, vol.32, no.2, pp.149-158, 2020.
  • Hamza Sulimani, Wael Y.Alghamdi, Tony Jan, Gnana Bharathy and Mukesh Prasad, "Sustainability of Load Balancing Techniques in Fog Computing Environment", Procedia Computer Science, vol.191, pp.93-101, 2021.
  • Ibrahim Mahmood Ibrahim, Siddeeq Y. Ameen, Hajar Maseeh Yasin, Naaman Omar, Shakir Fattah Kak, Zryan Najat Rashid, Azar Abid Salih, Nareen O. M. Salim and Dindar Mikaeel Ahmed, "Web server performance improvement using dynamic load balancing techniques: A review", system, vol.19, p.21, 2021.
  • Gopala Krishna Sriram, "Challenges of cloud compute load balancing algorithms. International Research Journal of Modernization in Engineering Technology and Science, vol.4, no.1, pp.1186-1190, 2022.
  • Zhicong Chen, Yixiang Chen, Lijun Wu, Shuying Cheng and Peijie Lin, "Deep residual network based fault detection and diagnosis of photovoltaic arrays using current-voltage curves and ambient conditions", Energy Conversion and Management, vol.198, pp.111793, 2019.
  • Fatemeh Ahmadi Zeidabadi, Sajjad Amiri Doumari, Mohammad Dehghani, Zeinab Montazeri, Pavel Trojovský and Gaurav Dhiman, "MLA: a new mutated leader algorithm for solving optimization problems", Computers, Materials & Continua, vol.70, no.3, pp.5631-5649, 2022.
  • G. Shruthi, Monica R. Mundada, B. J. Sowmya, S. Supreeth, "Mayfly Taylor Optimisation-Based Scheduling Algorithm with Deep Reinforcement Learning for Dynamic Scheduling in Fog-Cloud Computing", Applied Computational Intelligence and Soft Computing, vol. 2022, Article ID 2131699, 17 pages, 2022. https://doi.org/10.1155/2022/2131699.
  • Mandeep Kaur and Rajni Aron, "FOCALB: Fog Computing Architecture of Load Balancing for Scientific Workflow Applications", Journal of Grid Computing, vol.19, no.4, pp.1-22, 2021.
  • Naranjo, P.G.V., Pooranian, Z., Shojafar, M., Conti, M. and Buyya, R., 2019. FOCAN: A Fog-supported smart city network architecture for management of applications in the Internet of Everything environments. Journal of parallel and distributed computing, 132, pp.274-283.
  • Mandeep Kaur and Rajni Aron "Energy-aware load balancing in fog cloud computing", Materials Today: Proceedings, 2020.
  • Singh, S.P., Kumar, R., Sharma, A. et al. Energy efficient load balancing hybrid priority assigned laxity algorithm in fog computing. Cluster Comput 25, 3325–3342 (2022). https://doi.org/10.1007/s10586-022-03554-x.
  • Singh, Jagdeep, Parminder Singh, El Mehdi Amhoud, and Mustapha Hedabou. 2022. "Energy-Efficient and Secure Load Balancing Technique for SDN-Enabled Fog Computing" Sustainability 14, no. 19: 12951. https://doi.org/10.3390/su141912951.
  • Simar Preet Singh, “Effective load balancing strategy using fuzzy golden eagle optimization in fog computing environment”, Sustainable Computing: Informatics and Systems, Volume 35, 2022, 100766, ISSN 2210-5379, https://doi.org/10.1016/j.suscom.2022.100766.
  • Abuhamdah, A., & Al-Shabi, M. (2022). HYBRID LOAD BALANCING ALGORITHM FOR FOG COMPUTING ENVIRONMENT. International Journal of Software Engineering and Computer Systems, 8(1), 11–21. https://doi.org/10.15282/ijsecs.8.1.2022.2.0092.
  • Albalawi, Mona, Entisar Alkayal, Ahmed Barnawi, and Mehrez Boulares. "Load Balancing Based on Many-objective Particle Swarm Optimization Algorithm with Support Vector Regression in Fog Computing." Journal of Engineering and Applied Sciences Technology. SRC/JEAST-170. DOI: doi. org/10.47363/JEAST/2022 (4) 138 (2022).
  • A. J. Kadhim and J. I. Naser, "Proactive load balancing mechanism for fog computing supported by parked vehicles in IoV-SDN," in China Communications, vol. 18, no. 2, pp. 271-289, Feb. 2021, doi: 10.23919/JCC.2021.02.019.
  • Baburao, D., Pavankumar, T. & Prabhu, C.S.R. Load balancing in the fog nodes using particle swarm optimization-based enhanced dynamic resource allocation method. Appl Nanosci (2021). https://doi.org/10.1007/s13204-021-01970-w.
  • G., Shruthi, Monica R. Mundada, and Supreeth S. 2022. “The Resource Allocation Using Weighted Greedy Knapsack Based Algorithm in an Educational Fog Computing Environment”. International Journal of Emerging Technologies in Learning (iJET) 17 (18):pp. 261-274. https://doi.org/10.3991/ijet.v17i18.32363.
  • Hasan Ali Khattak, Hafsa Arshad, Saif ul Islam, Ghufran Ahmed, Sohail Jabbar, Abdullahi Mohamud Sharif and Shehzad Khalid, "Utilization and load balancing in fog servers for health applications", EURASIP Journal on Wireless Communications and Networking, vol.2019, no.1, pp.1-12, 2019.
  • Min Xia, Wanan Liu, Bicheng Shi, Liguo Weng and Jia Liu, “Cloud/snow recognition for multispectral satellite imagery based on a multidimensional deep residual network”, International journal of remote sensing, vol.40, no.1, pp.156-170, 2019.
  • S. Supreeth, K. Patil, S. D. Patil, S. Rohith, Y. Vishwanath, and K. S. V. Prasad, “An Efficient Policy-Based Scheduling and Allocation of Virtual Machines in Cloud Computing Environment,” Journal of Electrical and Computer Engineering, vol. 2022. Hindawi Limited, pp. 1–12, Sep. 24, 2022. doi: 10.1155/2022/5889948.
  • S. S and Kirankumari Patil, “Hybrid Genetic Algorithm and Modified-Particle Swarm Optimization Algorithm (GA-MPSO) for Predicting Scheduling Virtual Machines in Educational Cloud Platforms,” International Journal of Emerging Technologies in Learning (iJET), vol. 17, no. 07. International Association of Online Engineering (IAOE), pp. 208–225, Apr. 12, 2022. doi: 10.3991/ijet.v17i07.29223.
  • S. Supreeth, K. Patil, S. D. Patil, and S. Rohith, “Comparative approach for VM Scheduling using Modified Particle Swarm Optimization and Genetic Algorithm in Cloud Computing,” 2022 IEEE International Conference on Data Science and Information System (ICDSIS). IEEE, Jul. 29, 2022. doi: 10.1109/icdsis55133.2022.9915907.
  • Supreeth S, Kirankumari Patil, “VM Scheduling for Efficient Dynamically Migrated Virtual Machines (VMS-EDMVM) in Cloud Computing Environment,” KSII Transactions on Internet and Information Systems, vol. 16, no. 6. Korean Society for Internet Information (KSII), Jun. 30, 2022. doi: 10.3837/tiis.2022.06.007.
Еще
Статья научная
QR-код для установки приложения SciUp Наведите камеру и скачайте бесплатное приложение SciUp