Developing a performance aware cloud infrastructure in e-learning system
Subject Areas :
Multimedia Processing, Communications Systems, Intelligent Systems
nafiseh fareghzadeh
1
,
Nasser Modiri
2
1 - Islamic Azad University, Khodabandeh Branch, Iran
2 - Department of Computer Engineering, Assistant Professor, Zanjan Branch, Islamic Azad University, Iran.
Received: 2020-12-09
Accepted : 2021-03-28
Published : 2021-03-21
Keywords:
learning management system,
infrastructure,
Performance-awareness,
cloud computing,
E-Learning,
Abstract :
Introduction: The sharing of resources and the dynamics of workloads in e-learning systems cause competition between users and loss of efficiency. Awareness of efficiency as a key solution can protect workloads from each other's negative effects. Currently, the lack of such an infrastructure causes fluctuations in efficiency and unfavorable service quality in these systems. The purpose of the current research is to take steps to compensate for the aforementioned shortcomings and one of the most important achievements is the development of an efficient cloud infrastructure in electronic education systems. Unlike existing approaches, the proposed infrastructure does not depend on a specific execution mechanism and independently of the service level and actors, it monitors the performance of the entire cloud stack in an integrated and dynamic manner.
Method: The proposed approach displays e-learning systems based on sets of departments and operational units, components, operational dependencies, and required functional connections. The description of operational units and their duties and functions are as follows: shared knowledge repository, recovery and synchronization unit, audit and monitoring unit, inference and notification unit, control and execution unit.
Findings: The results of the implementation and evaluation of different scenarios show the superiority of the proposed infrastructure and the improvement of performance criteria.
Discussion: The proposed infrastructure uses performance values and criteria as raw data for dynamic control and performance management. Therefore, this operational framework can be used for different efficiency measures. The proposed approach is able to support performance optimization and management of its requirements for a multi-tenant environment. The approach of the present research, considering the aspects of awareness of efficiency in electronic education systems, can be used as an effort to create a general roadmap and a perspective to achieve awareness of efficiency in basic cloud services
References:
[1] Chang, Review and discussion: E-learning for academia and industry, International Journal of Information Management, vol. 36, no. 3, pp. 476-485, 2016.
https://doi.org/10.1016/j.ijinfomgt.2015.12.007
[2] Al-Samarraie, N. Saeed, A systematic review of cloud computing tools for collaborative learning: Opportunities and challenges to the blended-learning environment, Journal of Computers & Education, vol. 124, pp. 77-91, 2018.
https://doi.org/10.1016/j.compedu.2018.05.016.
[3] H. Su, G. H. Tzeng, S. K. Hu, Cloud e-learning service strategies for improving e-learning innovation performance in a fuzzy environment by using a new hybrid fuzzy multiple attribute decision-making model, Journal of Interactive Learning Environments, vol. 24, no. 8, pp. 1812-1835, 2016.
https://doi.org/10.1080/10494820.2015.1057742
[4] R. Muhammad, S. M. Abdulrahman, Cloud Computing Based e-Learning: Opportunities and Challenges for Tertiary Institutions in Nigeria, International Journal of e-Education, e-Business, e-Management and e-Learning, vol. 5, no. 3, pp. 144-152, 2015.
[5] Rezaei, B. Karimi, H. Jamalodin, Effect of Cloud Computing Systems in Terms of Service Quality of Knowledge Management Systems, Lecture Notes on Software Engineering, vol. 4, no. 1, pp. 73-76, 2016.
[6] A. Shahoseini, F. Narenji Sani, R. Ebadi, H. Roodbari, Evaluation of E-Learning Teaching Service Quality in Higher Education, Academic Librarianship and Information Research, vol. 49, no. 2, pp. 277-303, 2015.
[7] Hussein, M. Omar, Cloud Computing and its effects on performance excellence at Higher Education Institutions in Egypt (An analytical study), European Scientific Journal, vol. Special, no. Edition, pp. 163-176, 2015.
[8] Q. Qwaider, A Cloud Computing Based Learning Management Systems (LMSs) Architecture, International Journal of Computing and Network Technology, vol. 5, no. 2, pp. 51-58, 2017.
http://dx.doi.org/10.12785/IJCNT/050202
[9] Tamang, A. Alsadoon, C. Withana, L. S. Hoe, and A. Elchouemi, A model to improve quality of service (QoS) in cloud based virtual lab framework, in Proc. Int. Conf. Workshop Comput. Commun. (IEMCON), pp. 1-5, 2015 .
[10] Zaharescu, G. A. Zaharescu, Enhanced Virtual E-Learning Environments Using Cloud Computing Architectures, International Journal of Computer Science Research and Application, vol. 2, no. 1, pp. 31-41, 2012.
[11] Singh, A. Bhasin, Efficient Resource Management Technique for Performance Improvement in Cloud Computing, Indian Journal of Comp. Sci. & Eng, vol. 8, no. 1, pp. 33-39, 2017.
[12] D. Rossi, M. G. Xavier, C. A. De Rose, R. N. Calheiros and R. Buyya, E-eco: performance-aware energy-efficient cloud data center orchestration, J. Netw. Comput. Appl, vol. 78, pp. 83-96, 2017.
https://doi.org/10.1016/j.jnca.2016.10.024
[13] Suresh and S. Sakthivel, A novel performance constrained power management framework for cloud computing using an adaptive node scaling approach, J Computers& Electrical Engineering, vol. 60, pp. 30-44, 2017.
https://doi.org/10.1016/j.compeleceng.2017.04.018
[14] D. Rossi, M. G. Xavier, C. A. F. De Rose, R. N. Calheiros and R. Buyya, E-eco: Performance-aware energy-efficient cloud data center orchestration, J. Netw. Comput. Appl., vol. 78, pp. 83-96, 2017.
[15] Antonov, N. Popova and V. Voevodin, Computational science and HPC education for graduate students: Paving the way to exascale, J. Parallel Distr. Com., vol. 118, pp. 157-165, 2018.
[16] Pireva, P. Kefalas and I. Stamatopoulou, Representation of learning objects in cloud e-learning, 8th International Conference on Information, Intelligence, Systems & Applications (IISA), 2017.
DOI: 10.1109/IISA.2017.8316369
[17] Smith WD (2000) TPC-W: benchmarking an ecommerce solution
[18] J. Navimipour, A. M. Rahmani, A. H. Navin and M. Hosseinzadeh, Expert cloud: a cloud-based framework to share the knowledge and skills of human resources, Computers in Human Behavior, vol. 46, pp. 57-74, 2015.
https://doi.org/10.1016/j.chb.2015.01.001
[19] Lehrig, R. Sanders, G. Brataas, M. Cecowski, S. Ivanšek and J. Polutnik, CloudStore - towards scalability, elasticity, and efficiency benchmarking and analysis in Cloud computing, Futur. Gener. Comput. Syst., vol. 78, pp. 115-126, 2018.
[20] https://tomcat.apache.org/tomcat-7.0-doc/config/valve.html last accessed: 11. Aug 2016.
[21] Agrawal, U. Vyas, V. Bhatia and S. Prakash, SLA-aware differentiated QoS in elastic optical networks, Optical Fiber Technology, vol. 36, pp. 41-50, 2017.
[22] Tian, M. Xu, A. Chen, G. Li, X. Wang and Y. Chen, Open-source simulators for cloud computing: Comparative study and challenging issues, Simulation Modelling Practice and Theory, vol. 58, pp. 239-254, 2015.
_||_