An Efficient Approach for Resource Allocation in Fog Computing Considering Request Congestion Conditions
Subject Areas : electrical and computer engineering
Samira
Ansari Moghaddam
1
سميرا
نوفرستي
2
مهري
رجايي
3
(University of Sistan and Baluchestan )
Keywords: Resource allocation, scheduling, fog computing, request placement, request congestion,
Abstract :
Cloud data centers often fail to cope with the millions of delay-sensitive storage and computational requests due to their long distance from end users. A delay-sensitive request requires a response before its predefined deadline expires, even when the network has a high load of requests. Fog computing architecture, which provides computation, storage and communication services at the edge of the network, has been proposed to solve these problems. One of the fog computing challenges is how to allocate cloud and fog nodes resources to user requests in congestion conditions to achieve a higher acceptance rate of user requests and minimize their response time. Fog nodes have limited storage and computational power, and hence their performance is significantly reduced due to high load of user requests. This paper proposes an efficient resource allocation method in fog computing that decides where (fog or cloud) to process the requests considering the available resources of fog nodes and congestion conditions. According to the experimental results, the performance of the proposed method is better compared with existing methods in terms of average response time and percentage of failed requests.
[1] K. Ashton, "That 'internet of things' thing," RFID J., vol. 22, no. 7, pp. 97-114, Jun. 2009.
[2] M. Bahrami and M. Singhal, "The role of cloud computing architecture in big data," in Information Granularity, Big Data, and Computational Intelligence, Springer, Switzerland, Cham, vol. 8, pp. 275-295, 2015.
[3] R. Xu, et al., "Improved particle swarm optimization based workflow scheduling in cloud-fog environment," in Proc. Int. Conf. on Business Process Management, BPM’21, vol. 342, pp. 337-347, Rome, Italy, 6-10 Sept. 2018.
[4] 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. on Industrial Informatics, vol. 14, no. 10, pp. 4497-4506, Oct. 2018.
[5] L. F. Bittencourt, J. D. Montes, R. Buyya, O. F. Rana, and M. Parashar, "Mobility-aware application scheduling in fog computing," IEEE Cloud Computing, vol. 4, no. 2, pp. 26-35, Mar./Apr. 2017.
[6] M. Verma, N. Bhardwaj, and A. K. Yadav, "Real time efficient scheduling algorithm for load balancing in fog computing environment," International Journal of Information Technology and Computer Science, vol. 8, no. 4, pp. 1-10, 2016.
[7] V. B. C. Souza, et al., "Handling service allocation in combined fog-cloud scenarios," in Proc. IEEE Int. Conf. on Communications, ICC’16, 5 pp., Kuala Lumpur, Malaysia, 22-27 May 2016.
[8] Y. Sun, T. Dang, and J. Zhou, "User scheduling and cluster formation in fog computing based radio access networks," in Proc. IEEE Int. Conf. on Ubiquitous Wireless Broadband, ICUWB’16, 4 pp., Nanjing, China, 16-19 Oct. 2016.
[9] R. Deng, R. Lu, C. Lai, and T. H. Luan, "Towards power consumption-delay tradeoff by workload allocation in cloud-fog computing," in Proc. IEEE Int. Conf. on Communications ICC’15, pp. 3909-3914, London, UK, 8-12 Jun. 2015.
[10] R. Deng, R. Lu, C. Lai, T. H. Luan, and H. Liang, "Optimal workload allocation in fog-cloud computing toward balanced delay and power consumption," IEEE Internet of Things J., vol. 6, no. 3, pp. 1171-1181, Dec. 2016.
[11] L. Liu, D. Qi, N. Zhou, and Y. Wu, "A task scheduling algorithm based on classification mining in fog computing environment," Wireless Communications and Mobile Computing, vol. 2018, Article No. 2102348, 11 pp., 2018.
[12] م. اسدی و آ. طباطبایی، " روش پیشنهادی برای کاهش استفاده پهنای باند در مهاجرت زنده کانتینر در لایه مه،" مجموعه مقالات ششمین کنفرانس وب¬پژوهی، 7 صص.، تهران، ایران، 22-22 خرداد 1399.
[13] D. Goncalves, K. Velasquez, M. Curado, L. Bittencourt, and E. Madeira, "Proactive virtual machine migration in fog environments," in Proc. IEEE Symp. on Computers and Communications, ISCC’18, pp. 742-745, Natal, Brazil, 25-28 Jun. 2018.
[14] X. Q. Pham and E. N. Huh, "Towards task scheduling in a cloud-fog computing system," in Proc. IEEE 18th Asia-Pacific Network Operations and Management Symposium, APNOMS’16, 4 pp., Kanazawa, Japan, 5- 7 Oct. 2016.
[15] S. Bitam, S. Zeadally, and A. Mellouk, "Fog computing job scheduling optimization based on bees swarm," Enterprise Information Systems, vol. 12, no. 4, pp. 373-397, 2018.
[16] J. Fan, X. Wei, T. Wang, T. Lan, and S. Subramaniam, "Deadline-aware task scheduling in a tiered IoT infrastructure," in Proc. IEEE Global Communications Conf., GLOBECOM’17, 7 pp., Singapore, Singapore, 4-8 Dec. 2017.
[17] S. Kabirzadeh, D. Rahbari, and M. Nickray, "A security aware scheduling in fog computing by hyper heuristic algorithm," in Proc. 3rd Iranian Conf. on Intelligent Systems and Signal Processing, ICSPIS’17, pp. 87-92, Shahrood, Iran, 20-21 Dec. 2017.
[18] S. Kabirzadeh, D. Rahbari, and M. Nickray, "A hyper heuristic algorithm for scheduling of fog networks," in Proc. 21st Conf. of Open Innovations Association, FRUCT’17, pp. 148-155, Helsinki, Finland, 6-10 Nov. 2017.
[19] T. Choudhari, M. Moh, and T. S. Moh, "Prioritized task scheduling in fog computing," in Proc. of the ACMSE Conf., 8 pp., New York, NY, USA, 29-31 Mar. 2018.
[20] H. Zhang, Y. Xiao, S. Bu, D. Niyato, F. R. Yu, and Z. Han, "Computing resource allocation in three-tier IoT fog networks: a joint optimization approach combining Stackelberg game and matching," IEEE Internet of Things J., vol. 4, no. 5, pp. 1204-1215, 2017.
[21] H. Gupta, A. V. Dastjerdi, S. K. Ghosh, and R. Buyya, "iFogSim: a toolkit for modeling and simulation of resource management techniques in the Internet of Things, edge and fog computing environments," Software: Practice and Experience, vol. 47, no. 9, pp. 1275-1296, Jun. 2017.
[22] H. E. Refaat and M. A. Mead, "DLBS: decentralize load-balance scheduling algorithm for real-time IoT services in mist computing," International J. of Advanced Computer Science and Applications, vol. 10, no. 9, pp. 92-100, Sept. 2019.
[23] A. Khalid and M. Shahbaz, "Service architecture models for fog computing: a remedy for latency issues in data access from clouds," Trans. on Internet and Information Systems, vol. 11, no. 5, pp. 2310-2345, 2017
. [24] D. Rathod and C. Girish, "Load balancing of fog computing centers: minimizing response time of high priority requests," International Journal of Innovative Technology and Exploring Engineering, vol. 8, no. 11, pp. 2713-2716, Sep. 2019.
[25] S. N. Srirama, K. Ramamohanarao, R. Buyya, and M. R. Mahmud, "Quality of Experience (QoE)-aware placement of applications in fog computing environments," Journal of Parallel and Distributed Computing, vol. 132, pp. 190-203, Oct. 2018.
[26] B. Dewulf, T. Neutens, M. Vanlommel, and S. Logghe, "Examining commuting patterns using Floating Car Data and circular statistics: exploring the use of new methods and visualizations to study travel times," Journal of Transport Geography, vol. 48, pp. 41-51, Oct. 2015.