Enhancing the e-learning system based on a novel tasks’ classification load-balancing algorithm

Abstract

In the educational field, the system performance, as well as the stakeholders’ satisfaction, are considered a bottleneck in the e-learning system due to the high number of users who are represented in the educational system’s stakeholders including instructors and students. On the other hand, successful resource utilization in cloud systems is one of the key factors for increasing system performance which is strongly related to the ability for the optimal load distribution. In this study, a novel load-balancing algorithm is proposed. The proposed algorithm aims to optimize the educational system’s performance and, consequently, the users’ satisfaction in the educational field represented by the students. The proposed enhancement in the e-learning system has been evaluated by two methods, first, a simulation experiment for confirming the applicability of the proposed algorithm. Then a real-case experiment has been applied to the e-learning system at Helwan University. The results revealed the advantages of the proposed algorithm over other well-known load balancing algorithms. A questionnaire was also developed to measure the users’ satisfaction with the system’s performance. A total of 3,670 thousand out of 5,000 students have responded, and the results have revealed a satisfaction percentage of 95.4% in the e-learning field represented by the students.

Load Balancing Based on Multi-Agent Framework to Enhance Cloud燛nvironment

“…The utilities of any hosted machine in cloud computing have many parameters to be evaluated to avoid workload [1,28]. These parameters play an important role in proposing load balancing algorithms in a cloud environment which could be discussed as follows [2,9,29]: response time indicates the time from the user starts to request the task and is completed on the server. Latency is the delay time over the network that occurred during starting the task from a computer and completed on the server.…”

“…It could deliver all tasks then calculate the time of each task and select the maximum one to be assigned to the specific machine. It differs from MMLB which could deal with many tasks and keep high performance [2]. • Honey-Bee Load Balancing (HBLB) algorithm [35]: it is proposed to solve the issues of cloud computing environment based on the concept belonging to food process bees.…”

“…Load balancing algorithm-based task classification and implementation on e-learning system architecture in the educational sector was compared with other load balancing algorithms [2]. The authors present three experiments using a task classification load balancing algorithm and evaluated the metrics that affect the performance of their algorithm.…”

“…The algorithm is used to avoid the traffic flow, and decrease the response time, in addition, to sure the distribution of specific tasks for a specific server without overloading a machine. This algorithm is developed after implementing and testing the novel algorithm in [2] because it was implemented based on the history of (old and new) tasks and their weights for allocating and distributing them over low server utilities. The load balancing algorithm in [2] ignored the differences between servers in their implementation and applied the algorithm depending on the relation between old and new tasks.…”

“…Cloud Computing (CC) is a technology that can allow the migration of many virtual machines with quick access anywhere and anytime efficiently [1,2]. This technology needs a load balancing algorithm to balance the load on the servers to achieve the high performance of using CC [1].…”