A Hybrid Multiple Parallel Queuing Model to Enhance QoS in Cloud Computing

Afzal, Shahbaz; Kavitha, G.

doi:10.4018/ijghpc.2020010102

Cited by 8 publications

(5 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [51], the authors suggested a DRL-based service composition model for cloud computing that was also QoS-aware, considering logistics, reliability, cost, and time as the QoS parameters. The authors of [52] took a slightly different approach and recommended improving these metrics using a Hybrid Multiple Parallel Queuing Approach. The arrival rate, service rate, and the number of servers were the QoS parameters in this study.…”

Section: Related Workmentioning

confidence: 99%

“…In the following section, we review the proposed models in relation to the techniques and list most of the QoS parameters that have been used in prior related works. [12], [21], [24], [27], [29], [30], [31], [34], [35], [38], [39], [43], [44], [46], [48], [51], [52] 16%…”

Section: Related Workmentioning

confidence: 99%

“…2 Time [10], [21], [25], [26], [29], [32], [35], [37], [39], [40], [41], [45], [47], [48], [50], [51], [52] 15%…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

A Survey on Improving QoS in Service Level Agreement for Cloud Computing Environment

Edinat

Al-Sayyed

Hudaib

2021

Int. J. Interact. Mob. Technol.

View full text Add to dashboard Cite

Cloud computing is considered one of the most important techniques in the field of distributed computing which contributes to maintain increased scalability and flexibility in computer processing. This is achieved because it, using the Internet, provides different resources and shared services with minimum costs. Cloud service providers (CSPs) offer many different services to their customers, where the customers’ needs are met seeking the highest levels of quality at the lowest considerate prices. The relationship between CSPs and customers must be determined in a formal agreement, and to ensure how the QoS between them will be fulfilled, a clear Service Level Agreement (SLA) must be called for. Several previously-proposed models used in the literature to improve the QoS in the SLA for cloud computing and to face many of the challenges in the SLA are reviewed in this paper. We also addressed the challenges that are related to the violations of SLAs, and how overcoming them will enhance customers’ satisfaction. Furthermore, we proposed a model based on Deep Reinforcement Learning (DRL) and an enhanced DRL agent (EDRLA). In this model, and by optimizing the learning process in EDRLA, proposed agents would be able to have optimal CSPs by improving the learning process in EDRLA. This improvement will be reflected in the agent's performance and considerably affect it, especially in identifying cloud computing requirements based on the QoS metrics.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A Survey on Improving QoS in Service Level Agreement for Cloud Computing Environment

Edinat

Al-Sayyed

Hudaib

2021

Int. J. Interact. Mob. Technol.

View full text Add to dashboard Cite

show abstract

“…But, it does not consider the traffic flow during migration to solve any conflicts. Afzal & Kavitha (24) developed a hybrid multiple parallel queuing methods for improving the Quality-of-Service (QoS) in cloud computing. But, it assumes the traffic arrival rate and service rate were constant.…”

Section: Literature Surveymentioning

confidence: 99%

Resource utilization prediction with multipath traffic routing for congestion-aware VM migration in cloud computing

Gowriprakash¹,

Shankar²,

Duraisamy³

2021

IJST

View full text Add to dashboard Cite

Background: The Load Balancing (LB) schemes in cloud computing consider both current and future utilization of resources to decide the most suitable Virtual Machines (VMs) to be migrated to the most appropriate Physical Machines (PMs). But, the possibility of network congestion occurrence was high when increasing the bandwidth use between VMs within the cloud data centers. Also, a less-than-optimal migration of VMs can lead to high network traffic since it causes inter-VM traffic for traversing the bottleneck network routes. Objective: To enhance the efficiency of LB and reduce the possibility of congestion occurrence during VM migration in cloud data centers. Methods: Osmotic Hybrid artificial Bee and Ant Colony with Future Utilization Prediction with Multipath Traffic Routing (OH-BAC-FUP-MTR) mechanism are presented in this article to achieve the above objective. Initially, the OH-BAC-FUP mechanism is performed to decide the most suitable VMs to be migrated to the most suitable PMs. During VM migration, if any congestion exists due to high bandwidth use or traffic flows, then the MTR algorithm is applied to partition the flows and route them through multiple link-disjoint routes. Based on this, the congestion is avoided while ensuring the bandwidth and security grade demands. Also, the highest traffic on the path is applied as a congestion factor. Moreover, the current and future network states are taken into account for MTR to select the most optimal route from multiple routes with no consideration of the past use of the paths. Findings: The simulation outcomes demonstrate the OH-BAC-FUP-MTR mechanism consumes 8.74% of overall energy, 6.8% of Service Level Agreement violation Time per Active Host (SLATAH), 27.63% of Performance Degradation due to Migration (PDM), 22.98% of SLA Violation (SLAV), 27.27% of VM migrations and 30.77% of hosts shutdown compared to the OH-BAC-FUP using Linear Regression (LR) and Optimal Piecewise LR (OPLR).

show abstract

“…Different load balancing algorithms have been proposed such as in Afzal & Abdur Rahman (2020) which proposed a load balancing approach that aimed at minimizing the whole task’s executing time and reached a satisfactory result. However, it did not consider the migration time of the task through the network.…”

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

View full text Add to dashboard Cite

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.

show abstract

A Hybrid Multiple Parallel Queuing Model to Enhance QoS in Cloud Computing

Cited by 8 publications

References 17 publications

A Survey on Improving QoS in Service Level Agreement for Cloud Computing Environment

A Survey on Improving QoS in Service Level Agreement for Cloud Computing Environment

Resource utilization prediction with multipath traffic routing for congestion-aware VM migration in cloud computing

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

Contact Info

Product

Resources

About