ANALYSIS OF WAYS TO REDUCE MICROFLOW DELAY IN COMPUTER SYSTEMS SUPPORTING IOT ON THE FOG PLATFORM
DOI:
https://doi.org/10.26906/SUNZ.2022.3.088Keywords:
Cloud computing, Fog computing, IoT, Internet of ThingsAbstract
As the Internet of Things (IoT) becomes a part of our daily lives, there is a rapid increase in the number of connected devices. The established approach based on cloud computing technologies cannot provide the required quality of service in such conditions, particularly in reducing the delay time in data transmission. Today, fog computing technology is considered a promising solution for processing a large volume of critical and time-sensitive data. This article examines the technology of cloud computing, and also analyzes ways to reduce the delay of microflows in computer systems supporting IoT on a fog platform. Analyzed architectures of IoT support fog platforms such as multi-layer architecture, OpenFog, and IFCIoT. As a result of the analysis, it was concluded that there was a need to create microflow management methods to reduce the delay of microflows of data in computer systems supporting IoT on a fog platform by increasing the efficiency of the allocation of computing resources to meet the quality of service requirements.Downloads
References
Ravandi B., Papapanagiotou I. A Self-Learning Scheduling in Cloud Software Defined Block Storage IEEE International Conference on Cloud Computing, CLOUD (Honolulu, Hawaii, USA, June, 25–July, 1, 2017). IEEE Computer Society, 2017. Р. 415–422. DOI: 10.1109/CLOUD.2017.60.
Zhang B., Mor N., Kolb J., et al. The Cloud is Not Enough: Saving IoT from the Cloud 7th USENIX Workshop on Hot Topics in Storage and File Systems, HotStorage 2015, 2020.
Bonomi F., Milito R., Zhu J., et al. Fog Computing and Its Role in the Internet of Things Proceedings of the 1st ACM Mobile Cloud Computing Workshop, MCC’12 (Helsinki, Finland, August, 17, 2012). ACM Press, 2012. Р. 13–15. DOI: 10.1145/2342509.2342513.
OpenFog Consortium Architecture Working Group OpenFog Reference Architecture for Fog Computing. 2017.
Aazam M., Huh E. Fog Computing Micro Datacenter Based Dynamic Resource Estimation and Pricing Model for IoT. 2015 IEEE 29th International Conference on Advanced In- formation Networking and Applications. Gwangiu, 2015. p. 687-694. (In Eng.) DOI: https://doi.org/10.1109/ AINA.2015.254
Arkian H.R., Diyanat A., Pourkhalili A. MIST: Fog-based data analytics scheme with cost-efficient resource provisioning for IoT crowdsensing applications. Journal of Network and Computer Applications. 2017; 82:152-165. (In Eng.) DOI: https://doi.org/10.1016/j.jnca.2017.01.012
Luan T.H., Gao L., Li Z., Xiang Y., Wei G., Sun L. Fog Computing: Focusing on Mobile Users at the Edge. arXiv:1502.01815. 2016. Available at: https://arxiv.org/abs/1502.01815 (accessed 01.05.2020). (In Eng.)
Dastjerdi A.V., Gupta H., Calheiros R.N., Ghosh S.K., Buyya R. Fog computing: Principles, architectures, and applications. In: Buyya R., Dastjerdi A. (ed.) Internet of Things: Principle & Paradigms. Morgan Kaufmann, Burlington, Massachusetts, USA; 2016. Available at: https://arxiv.org/abs/1601.02752 (accessed 01.05.2020). (In Eng.)
Munir A., Kansakar P., Khan S.U. IFCIoT: Integrated Fog Cloud IoT: A novel architectural paradigm for the future Internet of Things. IEEE Consumer Electronics Magazine. 2017; 6(3):74-82. (In Eng.) DOI: https://doi.org/10.1109/ MCE.2017.2684981
Kashif Munir and Lawan A. Mohammed, University of Hafr Al Batin, KSA: Biometric Smartcard Authentication for Fog Computing. International Journal of Network Security and Its Application (IJNSA) Vol. 10, No 6, November 2018. Available at: https://aircconline.com/abstract/ijnsa/v10n6/10618ijnsa04.html
