Resource Allocation for Improved User Satisfaction with Applications to LTE

Lima, F. Rafael M.; Rodrigues, Emanuel B.; Maciel, Tarcísio F.; Nordberg, Mats

doi:10.1007/978-1-4614-8057-0_2

Cited by 4 publications

(3 citation statements)

References 23 publications

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“…The main aim of the applied experiment is to evaluate the users’ satisfaction represented in the students as well as the enhancement of the e-learning system targeting to support the educational field. The positive relation between the system performance and user satisfaction has been illustrated in different research such as in Lima et al (2014) .…”

Section: Real Case Study Applied In E-learning System and Experimental Resultsmentioning

confidence: 94%

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: Real Case Study Applied In E-learning System and Experimental Resultsmentioning

confidence: 94%

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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“…Furthermore, most studies do not exclusively consider the issues of user mobility and mobility-aware scenarios. The authors in [7] considered the basic traffic model for real-time services and a full-buffer model for nonreal-time services, but the study was limited to static users only. In [8], the VoIP traffic model was examined for a static user scenario.…”

Section: Related Workmentioning

confidence: 99%

Mobility and traffic-aware resource scheduling for downlink transmissions inLTE-A systems

Yildiz¹,

Sokullu²

2019

Turk J Elec Eng & Comp Sci

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As new cellular networks support not only voice services but also many multimedia applications, the requirements for reliable data transmission at high speeds create heavy load on the system. Even though LTE/LTE-A technology takes action towards alleviating this load, it is still necessary to manage resources effectively because of the inadequacy of the available radio resources. Thus, the scheduler at the MAC layer of the base station plays a very important role in resource allocation to the user. In this study a novel algorithm for resource allocation in mobile environments is presented, with two variations addressing different input traffic. The idealized case (I-MAS algorithm) relates to the full-buffer model, while the realistic case (R-MAS algorithm) takes into consideration the specific characteristics of the incoming user traffic. The paper includes performance evaluation of the suggested algorithms in terms of mean and edge throughput, system fairness, and BLER and comparison with well-known algorithms like the round robin (RR) and best CQI (B-CQI) (full-buffer model) and their extensions for real-life traffic models, RR Traffic and B-CQI Traffic, respectively. When the simulation results are examined, it can be seen that the I-MAS and R-MAS algorithms maximize the throughput while at the same time distributing the resources fairly among the users. They also prove to be quite robust in mobile environments even at higher user speeds.

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“…Therefore, for efficient resource allocation and guarantee for QoS satisfaction, it is important that common network resources are allocated to different traffics using only the specific requirements of their applications. To satisfy this important requirement, Lima et al (2014) proposed two utility-based RRA policies, the Throughput-based Satisfaction Maximization (TSM) policy and the Delay-based Satisfaction Maximization (DSM) policy based on sigmoid utility function and both aimed at maximizing the number of satisfied users in the system. However, it uses a similar (bell-shaped) utility curve in both the TSM and DSM scheduling policies, thus making a guarantee of a higher user satisfaction index to a higher priority traffic flow highly improbable, especially when they have similar traffic model; because using the same utility curve for scheduling different traffic types may potentially achieve the same result as applying same QoS parameter to schedule different classes of service.…”

Section: Related Workmentioning

confidence: 99%

Utility-based Scheduling Frameworks to Provide Efficient Quality-of-Service Differentiation in a Mixture of Real-time and Non-real-time Traffics

Bello¹,

Zen²,

Othman³

et al. 2015

Journal of Computer Science

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This paper proposes a utility-based scheduling framework for efficient differentiation of users' Quality-of-Service (QoS) in a broadband wireless access system involving heterogeneous mixed traffic flows. The utility-based scheduling framework, called Maximum QoS Satisfaction (MQS), is based on three novel Radio Resource Allocation (RRA) techniques; delay-based scheduling policy for Real-Time (RT), minimumrate-based scheduling policy for Non-Real-Time (NRT) and a throughputbased scheduling policy for Best-Effort (BE) services. Simulation study shows that MQS achieves superior performances in terms of average system throughput and user satisfaction both in single and heterogeneous mixed traffic scenarios, when compared to some existing ones.

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Resource Allocation for Improved User Satisfaction with Applications to LTE

Cited by 4 publications

References 23 publications

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

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

Mobility and traffic-aware resource scheduling for downlink transmissions inLTE-A systems

Utility-based Scheduling Frameworks to Provide Efficient Quality-of-Service Differentiation in a Mixture of Real-time and Non-real-time Traffics

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