Abstract
Computer based algorithms are used for monitoring and controlling a computers where human, physical, digital and analog interaction occurs. This scheme termed as cyber-physical systems (CPS) is being deployed in a wide array of applications due to its easy deployment feature and connectivity. CPS based on cloud computing technology offers wider application range with enormous amount of storage and computing resources. However, despite the wide application and deployment of CPS in combining the key technologies like big data analytics, cloud computing and IoT, its energy consumption is large. Optimization of this energy is a major field of research and is of due importance. For this purpose, in cloud environment, virtual machines (VMs) are used for hosting the applications and the resources are managed thereby optimizing the energy consumption. The security issues and the quality of service (QoS) requirements of the system is also met by optimizing the system design. The scheduling issue of the system is addressed by implementation of efficient memory-aware scheduling strategy and algorithms. The proposed technique is tested for performance and the results of simulation is presented.
References
Roy, D., Zhang, L., Chang, W., Mitter, S. K., & Chakraborty, S. (2017). Semantics-preserving cosynthesis of cyber-physical systems. Proceedings of the IEEE, 106(1), 171-200.
Toussaint, W., & Ding, A. Y. (2020). Machine Learning Systems for Smart Services in the Internet of Things. arXiv preprint arXiv:2006.04950.
Kurdi, H. A., Alismail, S. M., & Hassan, M. M. (2018). LACE: A locust-inspired scheduling algorithm to reduce energy consumption in cloud datacenters. IEEE Access, 6, 35435-35448.
AlIsmail, S. M., & Kurdi, H. A. (2016). Review of energy reduction techniques for green cloud computing. Int. J. Adv. Comput. Sci. Appl, 1, 189-195.
Vaquero, L. M., Cuadrado, F., Elkhatib, Y., Bernal-Bernabe, J., Srirama, S. N., & Zhani, M. F. (2019). Research challenges in nextgen service orchestration. Future Generation Computer Systems, 90, 20-38.
Kaur, T., & Chana, I. (2015). Energy efficiency techniques in cloud computing: A survey and taxonomy. ACM computing surveys (CSUR), 48(2), 1-46.
Alam, K. M., & El Saddik, A. (2017). C2PS: A digital twin architecture reference model for the cloud-based cyber-physical systems. IEEE access, 5, 2050-2062.
Majstorovic, V. D., Durakbasa, N. M., Mourtzis, D., & Vlachou, E. (2016). Cloud-based cyber-physical systems and quality of services. The TQM Journal.
Sanislav, T., Mois, G., Folea, S., Miclea, L., Gambardella, G., & Prinetto, P. (2014, June). A cloud-based Cyber-Physical System for environmental monitoring. In 2014 3rd Mediterranean Conference on Embedded Computing (MECO) (pp. 6-9). IEEE.
Reddy, Y. B. (2014, December). Cloud-based cyber physical systems: Design challenges and security needs. In 2014 10th International Conference on Mobile Ad-hoc and Sensor Networks (pp. 315-322). IEEE.
Shakya, S. (2020). Survey on Cloud Based Robotics Architecture, Challenges and Applications. Journal of Ubiquitous Computing and Communication Technologies (UCCT), 2(01), 10-18.
Karunakaran, V. (2019). A STOCHASTIC DEVELOPMENT OF CLOUD COMPUTING BASED TASK SCHEDULING ALGORITHM. Journal of Soft Computing Paradigm (JSCP), 1(01), 41-48.
Mugunthan, S. R. (2019). SOFT COMPUTING BASED AUTONOMOUS LOW RATE DDOS ATTACK DETECTION AND SECURITY FOR CLOUD COMPUTING. Journal of Soft Computing Paradigm (JSCP), 1(02), 80-90.
Mourtzis, D., & Vlachou, E. (2018). A cloud-based cyber-physical system for adaptive shop-floor scheduling and condition-based maintenance. Journal of manufacturing systems, 47, 179-198.