An Efficient Algorithm for Optimization of Service Locating in Fog Computing Architectures

Tofigh Asbaghi, Mohsen Tarighi, Mohsen Bagheri Zefrei

Abstract


In this paper, the optimizing of Fog computing service quality is addressed regarding the service locating using proposing an efficient algorithm. The cost, response time, and reliability parameters are used in the optimization process through three categories, sequential, conditional, and parallel. The multi-criteria decision-making process of this paper is performed by the multi-objective optimization programming method. The algorithm consists of Fog service selection based on the quality evaluation. Then, the cloud management system interacts with Fog nodes using cloud providers to improve the quality and priority of services that are chosen for a specific user request. 


Keywords


Smart City, Cloud Computing, Fog Computing, Edge Computing, Big Data Analysis

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Abstract

References


Ahmadi, H., Arji, G., Shahmoradi, L., Safdari, R., Nilashi, M., & Alizadeh, M. (2019). The application of internet of things in healthcare: a systematic literature review and classification. Universal Access in the Information Society, 18(4), 837-869.

Asadi, S., Nilashi, M., & Yadegaridehkordi, E. (2017). Customers perspectives on adoption of cloud computing in banking sector. Information Technology and Management, 18(4), 305-330.

Collins, E. (2014). Intersection of the cloud and big data. IEEE Cloud Computing, 1(1), 84-85.

Dolui, K., & Datta, S. K. (2017). Comparison of edge computing implementations: Fog computing, cloudlet and mobile edge computing. Paper presented at the 2017 Global Internet of Things Summit (GIoTS).

Khaneghah, E. M., Nezhad, N. O., Mirtaheri, S. L., Sharifi, M., & Shirpour, A. (2011). An efficient live process migration approach for high performance cluster computing systems. Paper presented at the International Conference on Innovative Computing Technology.

Kirimtat, A., Krejcar, O., Kertesz, A., & Tasgetiren, M. F. (2020). Future trends and current state of smart city concepts: A survey. IEEE access, 8, 86448-86467.

Linthicum, D. S. (2017). Connecting fog and cloud computing. IEEE Cloud Computing, 4(2), 18-20.

Martinez, I., Hafid, A. S., & Jarray, A. (2020). Design, Resource Management, and Evaluation of Fog Computing Systems: A Survey. IEEE Internet of Things Journal, 8(4), 2494-2516.

Martinez, I., Jarray, A., & Hafid, A. S. (2020). Scalable design and dimensioning of fog-computing infrastructure to support latency-sensitive IoT applications. IEEE Internet of Things Journal, 7(6), 5504-5520.

Mirtaheri, S. L., Khaneghah, E. M., Sharifi, M., Minaei-Bidgoli, B., Raahemi, B., Arab, M. N., & Ardestani, A. S. (2013). Four-dimensional model for describing the status of peers in peer-to-peer distributed systems. Turkish Journal of Electrical Engineering & Computer Sciences, 21(6), 1646-1664.

Mohammed, F., Ibrahim, O., Nilashi, M., & Alzurqa, E. (2017). Cloud computing adoption model for e-government implementation. Information Development, 33(3), 303-323.

Raza, M. R., Varol, A., & Varol, N. (2020). Cloud and fog computing: A survey to the concept and challenges. Paper presented at the 2020 8th International Symposium on Digital Forensics and Security (ISDFS).

Talia, D. (2012). Clouds meet agents: Toward intelligent cloud services. IEEE Internet Computing, 16(2), 78-81.

Wang, L., Von Laszewski, G., Younge, A., He, X., Kunze, M., Tao, J., & Fu, C. (2010). Cloud computing: a perspective study. New generation computing, 28(2), 137-146.

Wu, H., Zhang, Z., Guan, C., Wolter, K., & Xu, M. (2020). Collaborate edge and cloud computing with distributed deep learning for smart city internet of things. IEEE Internet of Things Journal, 7(9), 8099-8110.

Yadegaridehkordi, E., Nilashi, M., Shuib, L., Asadi, S., & Ibrahim, O. (2019). Development of a SaaS adoption decision-making model using a new hybrid MCDM approach. International Journal of Information Technology & Decision Making, 18(06), 1845-1874.

Yadegaridehkordi, E., Nilashi, M., Shuib, L., & Samad, S. (2020). A behavioral intention model for SaaS-based collaboration services in higher education. Education and Information Technologies, 25(2), 791-816.

Yadegaridehkordi, E., Shuib, L., Nilashi, M., & Asadi, S. (2019). Decision to adopt online collaborative learning tools in higher education: A case of top Malaysian universities. Education and Information Technologies, 24(1), 79-102.

Yannuzzi, M., van Lingen, F., Jain, A., Parellada, O. L., Flores, M. M., Carrera, D., . . . Corsaro, A. (2017). A new era for cities with fog computing. IEEE Internet Computing, 21(2), 54-67.

Yu, C., Lin, B., Guo, P., Zhang, W., Li, S., & He, R. (2018). Deployment and dimensioning of fog computing-based internet of vehicle infrastructure for autonomous driving. IEEE Internet of Things Journal, 6(1), 149-160.

Yuan, X., He, Y., Fang, Q., Tong, X., Du, C., & Ding, Y. (2017). An improved fast search and find of density peaks-based fog node location of fog computing system. Paper presented at the 2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData).


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