Parallel Queuing Model in a Dynamic Cloud Environment-Study of Impact on QoS: An Analytical Approach

Afzal, Shahbaz; Kavitha, G.; Gull, Shabnum

doi:10.1007/978-981-15-1097-7_26

Cited by 1 publication

(1 citation statement)

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“…However, the current research has an advancement for direct allocation to the required machines with no need for migrating from machines which ensures less total execution time. Finally, the research in Afzal, Kavitha & Gull (2020) discussed the relation between different factors including the number of servers, number of tasks, and different time parameters such as waiting time and response time. The research concluded that following the parallel approach is satisfying when the allocation rate is higher than the tasks’ arrival rate.…”

Section: Introductionmentioning

confidence: 99%

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

Khedr

Idrees

Salem

2021

PeerJ Computer Science

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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.

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Section: Introductionmentioning

confidence: 99%