IMPROVING RESPONSE TIME OF TASK OFFLOADING BY RANDOM FOREST, EXTRA-TREES AND ADABOOST CLASSIFIERS IN MOBILE FOG COMPUTING

(Received: 27-May-2020, Revised: 7-Aug.-2020 , Accepted: 26-Aug.-2020)

Authors Elham Darbanian, Dadmehr Rahbari, Roghayeh Ghanizadeh, Mohsen Nickray,

Keywords #Fog computing #Decision tree classifier #Random forest classifier #Extra-trees classifier #AdaBoost classifier #Offloading #Machine learning

Abstract The application of computing resources through mobile devices (MDs) is called Mobile Computing; between cloud datacentres and devices, it is known as (Mobile) Fog Computing (MFC). We ran Cloudsim simulator to offload tasks in suitable Fog Devices (FDs), cloud or mobile. We stored the outputs of the simulator as a dataset with features and a target class. A target class is a device in which tasks are offloaded and features of tasks are authentication, confidentiality, integrity, availability, capacity, speed and cost. Decision Tree (DT), Random Forest (RF), Extra-trees and AdaBoost classifiers were classified based on attribute values and the plot of trees was drawn. According to the plot of these classifiers, we extracted each sequential condition from root to leaves and inserted it into the simulator. What these classifiers do is to improve the conditions that should be inserted in the corresponding section of the simulator. We improved the response time of offloading by Random Forest, Extra-trees and AdaBoost over Decision Tree.

References

[1] T. H. Noor, S. Zeadally, A. Alfazi and Q. Z. Sheng, "Mobile Cloud Computing: Challenges and Future Research Directions," Journal of Network and Computer Applications, vol. 115, pp. 70-85, 2018.

[2] R. Mahmud, R. Kotagiri and R. Buyya, "Fog Computing: A Taxonomy, Survey and Future Directions," Internet of Everything, pp. 103-130, Springer, Singapore, 2018.

[3] R. Roman, J. Lopez and M. Mambo, "Mobile Edge Computing, Fog et al.: A Survey and Analysis of Security Threats and Challenges," Future Generation Computer Systems, vol. 78, pp. 680-698, 2018.

[4] F. Gu, J. Niu, Z. Qi and M. Atiquzzaman, "Partitioning and Offloading in Smart Mobile Devices for Mobile Cloud Computing: State-of-the-art and Future Directions," Journal of Network and Computer Applications, vol. 119, pp. 83-96, 2018.

[5] C. Li, Y. Xue, J. Wang, W. Zhang and T. Li, "Edge-oriented Computing Paradigms: A Survey on Architecture Design and System Management," ACM Computing Surveys (CSUR), vol. 51, no. 2, pp. 1-34, 2018.

[6] S. Fletcher and Md. Z. Islam, "Decision Tree Classification with Differential Privacy: A Survey," ACM Computing Surveys (CSUR), vol. 52, no. 4, pp. 1-33, 2019.

[7] Scikit-learn, "Decision Trees (DTs)," [Online], Available: https://scikit-learn.org/stable/modules/tree. html#tree-algorithms.

[8] Scikit-learn, "Random Forest Classifier," [Online], Available: https://scikit-learn.org/stable/modules/ generated/sklearn.ensemble.RandomForestClassifier.html?highlight=random%20forest#sklearn.ensemb le.RandomForestClassifier.

[9] A. B. Shaik and S. Srinivasan, "A Brief Survey on Random Forest Ensembles in Classification Model," Proc. of the International Conference on Innovative Computing and Communications, pp. 253-260, Springer, Singapore, 2019.

[10] Scikit-learn, "Extra Trees Classifier," [Online], Available: https://scikit-learn.org/stable/modules/ generated/sklearn.ensemble.ExtraTreesClassifier.html

[11] Scikit-learn, "AdaBoost Classifier," [Online], Available: https://scikit-learn.org/stable/modules/ generated/sklearn.ensemble.AdaBoostClassifier.html

[12] Towards Data Science, "AdaBoost Classifier Example in Python," [Online], Available: https://towardsdatascience.com/machine-learning-part-17-boosting-algorithms-adaboost-in-python- d00faac6c464

[13] S. Wu, C. Mei, H. Jin and D. Wang, "Android Unikernel: Gearing Mobile Code Offloading Towards Edge Computing," Future Generation Computer Systems, vol. 86, pp. 694-703, 2018.

[14] L. Liu, Z. Chang and X. Guo, "Socially Aware Dynamic Computation Offloading Scheme for Fog Computing System with Energy Harvesting Devices," IEEE Internet of Things Journal, vol. 5, no. 3, pp. 1869-1879, 2018.

[15] L. Liu, Z. Chang, X. Guo, S. Mao and T. Ristaniemi, "Multi-objective Optimization for Computation Offloading in Fog Computing," IEEE Internet of Things Journal, vol. 5, no. 1, pp. 283-294, 2017.

[16] Z. Tang, X. Zhou, F. Zhang, W. Jia and W. Zhao, "Migration Modeling and Learning Algorithms for Containers in Fog Computing," IEEE Trans. on Services Computing, vol. 12, no. 5, pp. 712-725, 2018.

[17] J. Du, L. Zhao, J. Feng and X. Chu, "Computation Offloading and Resource Allocation in Mixed Fog/Cloud Computing Systems with Min-max Fairness Guarantee," IEEE Transactions on Communications, vol. 66, no. 4, pp. 1594-1608, 2018.

[18] J. Shuja, A. Gani, K. Ko, K. So, S. Mustafa, S. A. Madani and M. K. Khan, "SIMDOM: A Framework for SIMD Instruction Translation and Offloading in Heterogeneous Mobile Architectures," Transactions on Emerging Telecommunication Technologies, vol. 29, no. 4, p. e3174, 2018.

[19] H. Yan, X. Zhang, H. Chen, Y. Zhou, W. Bao and L. T. Yang, "DEED: Dynamic Energy-efficient Data Offloading for IoT Applications under Unstable Channel Conditions," Future Generation Computer Systems, vol. 96, pp. 425-437, 2019.

[20] Md. G. R. Alam, M. M. Hassan, Md. ZIa Uddin, A. Almogren and G. Fortino, "Autonomic Computation Offloading in Mobile Edge for IoT Applications," Future Generation Computer Systems, vol. 90, pp. 149-157, 2019.

[21] M. Aazam, S. Zeadally and K. A. Harras, "Offloading in Fog Computing for IoT: Review, Enabling Technologies and Research Opportunities," Future Generation Computer Systems, vol. 87, pp. 278-289, 2018.

[22] H. Lu, C. Gu, F. Luo, W. Ding and X. Liu, "Optimization of Lightweight Task Offloading Strategy for Mobile Edge Computing Based on Deep Reinforcement Learning," Future Generation Computer Systems, vol. 102, pp. 847-861, 2020.

[23] A. Jaddoa, G. Sakellari, E. Panaousis, G. Loukas and P. G. Sarigiannidis, "Dynamic Decision Support for Resource Offloading in Heterogeneous Internet of Things Environments," Simulation Modeling Practice and Theory, vol. 101, p.102019, [Online], Available: https://doi.org/10.1016/j.simpat.2019. 102019, 2020.

[24] C. Zhang, H.-H. Cho and C.-Y. Chen, "Emergency-level-based Healthcare Information Offloading over Fog Network," Peer-to-Peer Networking and Applications, vol. 13, no. 1, pp. 16-26, 2020.

[25] K. Xiao, Z. Gao, W. Shi, X. Qiu, Y. Yang and L. Rui, "EdgeABC: An Architecture for Task Offloading and Resource Allocation in the Internet of Things," Future Generation Computer Systems, vol. 107, pp. 498-508, 2020.

[26] I. A. Elgendy, W. Zhang, Y.-C. Tian and K. Li, "Resource Allocation and Computation Offloading with Data Security for Mobile Edge Computing," Future Generation Computer Systems, vol. 100, pp. 531- 541, 2019.

[27] W. Tang, X. Zhao, W. Rafique, L. Qi, W. Dou and Q. Ni, "An Offloading Method Using Decentralized P2P-enabled Mobile Edge Servers in Edge Computing," Journal of Systems Architecture, vol. 94, pp. 1- 13, 2019.

[28] Q. Wang, S. Guo, J. Liu and Y. Yang, "Energy-efficient Computation Offloading and Resource Allocation for Delay-sensitive Mobile Edge Computing," Sustainable Computing: Informatics and Systems, vol. 21, pp. 154-164, 2019.

[29] X. Xu, Y. Xue, L. Qi, Y. Yuan, X. Zhang, T. Umer and S. Wan, "An Edge Computing-enabled Computation Offloading Method with Privacy Preservation for Internet of Connected Vehicles," Future Generation Computer Systems, vol. 96, pp. 89-100, 2019.

[30] M. Adhikari and H. Gianey, "Energy Efficient Offloading Strategy in Fog/Cloud Environment for IoT Applications," Internet of Things, vol. 6, p. 100053, 2019.

[31] A. Bozorgchenani, S. Disabato, D. Tarchi and M. Roveri, "An Energy Harvesting Solution for Computation Offloading in Fog Computing Networks," Computer Communications, vol. 160, pp. 577- 587, 2020.

[32] Y. E. M. Hamouda, "Modified Random Bit Climbing Λ-MRBC) for Task Mapping and Scheduling in Wireless Sensor Networks," Jordanian Journal of Computers and Information Technology (JJCIT), vol. 5, no. 01, pp. 17-33, 2019.

[33] M. Zeng, Y. Li, K. Zhang, M. Waqas and D. Jin, "Incentive Mechanism Design for Computation Offloading in Heterogeneous Fog Computing: A Contract-based Approach," Proc. of IEEE International Conference on Communications (ICC), pp. 1-6, Kansas City, MO, USA, 2018.

[34] X. Zhu, S. Chen and G. Yang, "Energy and Delay Co-aware Computation Offloading with Deep Learning in Fog Computing Networks," Proc. of the 38th IEEE International Performance Computing and Communications Conference (IPCCC), pp. 1-6, London, UK, 2019.

[35] X. Zhao, L. Zhao and K. Liang, "An Energy Consumption Oriented Offloading Algorithm for Fog Computing," Proc. of the International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness, pp. 293-301, Springer, Cham, 2016.

[36] J. Shuja, S. Mustafa, R. W. Ahmad, S. A. Madani, A. Gani and M. K. Khan, "Analysis of Vector Code Offloading Framework in Heterogeneous Cloud and Edge Architectures," IEEE Access, vol. 5, pp. 24542-24554, 2017.

[37] M. Othman, A. N. Khan, J. Shuja and S. Mustafa, "Computation Offloading Cost Estimation in Mobile Cloud Application Models," Wireless Personal Communications, vol. 97, no. 3, pp. 4897-4920, 2017.

[38] J. Shuja, A. Gani, M. Habib ur Rehman, E. Ahmed, S. A. Madani, M. K. Khan and K. Ko, "Towards Native Code Offloading Based MCC Frameworks for Multimedia Applications: A Survey," Journal of Network and Computer Applications, vol. 75, pp. 335-354, 2016.

[39] X. Chen, "Decentralized Computation Offloading Game for Mobile Cloud Computing," IEEE Transactions on Parallel and Distributed Systems, vol. 26, no. 4, pp. 974-983, 2014.

[40] S. Sardellitti, G. Scutari and S. Barbarossa, "Joint Optimization of Radio and Computational Resources for Multicell Mobile-edge Computing," IEEE Transactions on Signal and Information Processing over Networks, vol. 1, no. 2, pp. 89-103, 2015.

[41] D. Rahbari and M. Nickray, "Task Offloading in Mobile Fog Computing by Classification and Regression Tree," Peer-to-Peer Networking and Applications, vol. 13, pp. 104-122, 2019.

[42] A. Yousefpour, C. Fung, T. Nguyen, K. Kadiyala, F. Jalali, A. Niakanlahiji, J. Kong and J. P. Jue, "All One Needs to Know about Fog Computing and Related Edge Computing Paradigms: A Complete Survey," Journal of Systems Architecture, vol. 98, pp. 289-330, 2019.