Blue Pages: Software as a Service Data Set

Alkalbani, Asma Musabah; Ghamry, Ahmed Mohamed; Hussain, Farookh Khadeer; Hussain, Omar Khadeer

doi:10.1109/bwcca.2015.83

Cited by 7 publications

(6 citation statements)

References 3 publications

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“…A synthetically generated QoS dataset was used because a real QoS dataset for cloud services that perfectly fit the context of our experiment could not be found. Alkalbani et al [39] alluded to the paucity of viable datasets for cloud services. The Blue Pages dataset in [39] is the closest dataset on cloud services that we got, but it is not based on QoS cloud services.…”

Section: Experimental Evaluation and Resultsmentioning

confidence: 99%

“…Alkalbani et al [39] alluded to the paucity of viable datasets for cloud services. The Blue Pages dataset in [39] is the closest dataset on cloud services that we got, but it is not based on QoS cloud services. Rather, it provides data on different service offerings such as service name, the date the service was founded, service category, free trial (yes/ no), mobile app (yes/no), starting price, service description, service type, and provider link as extracted from two cloud services review sitesgetapp.com, and cloudreviews.com, which does not fit perfectly for the purpose of this study.…”

Section: Experimental Evaluation and Resultsmentioning

confidence: 99%

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QoS-based ranking and selection of SaaS applications using heterogeneous similarity metrics

2018

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The plethora of cloud application services (Apps) in the cloud business apps e-marketplace often leads to service choice overload. Meanwhile, existing SaaS e-marketplaces employ keyword-based inputs that do not consider both the quantitative and qualitative quality of service (QoS) attributes that characterise cloud-based services. Also, existing QoS-based cloud service ranking approaches rank cloud application services are based on the assumption that the services are characterised by quantitative QoS attributes alone, and have employed quantitative-based similarity metrics for ranking. However, the dimensions of cloud service QoS requirements are heterogeneous in nature, comprising both quantitative and qualitative QoS attributes, hence a cloud service ranking approach that embrace core heterogeneous QoS dimensions is essential in order to engender more objective cloud selection. In this paper, we propose the use of heterogeneous similarity metrics (HSM) that combines quantitative and qualitative dimensions for QoS-based ranking of cloud-based services. By using a synthetically generated cloud services dataset, we evaluated the ranking performance of five HSM using Kendall tau rank coefficient and precision as accuracy metrics benchmarked with one HSM. The results show significant rank order correlation of Heterogeneous Euclidean-Eskin Metric, Heterogeneous Euclidean-Overlap Metric, and Heterogeneous Value Difference Metric with human similarity judgment, compared to other metrics used in the study. Our results confirm the applicability of HSM for QoS ranking of cloud services in cloud service e-marketplace with respect to users' heterogeneous QoS requirements.

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Section: Experimental Evaluation and Resultsmentioning

confidence: 99%

Section: Experimental Evaluation and Resultsmentioning

confidence: 99%

QoS-based ranking and selection of SaaS applications using heterogeneous similarity metrics

2018

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“…These parameters were security, user-friendliness, quality, availability and technology. The authors discussed availability and cost as QoS parameters in paper [43]. Paper [44] introduced CloudExp; an environment for modeling and simulating cloud computing.…”

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%

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

Edinat

Al-Sayyed

Hudaib

2021

Int. J. Interact. Mob. Technol.

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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.

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Dynamic Ranking System of Cloud SaaS Based on Consumer Preferences - Find SaaS M2NFCP

Ikram

Sharma

Raza

et al. 2019

Advanced Information Networking and Applications

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Blue Pages: Software as a Service Data Set

Cited by 7 publications

References 3 publications

QoS-based ranking and selection of SaaS applications using heterogeneous similarity metrics

QoS-based ranking and selection of SaaS applications using heterogeneous similarity metrics

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

Dynamic Ranking System of Cloud SaaS Based on Consumer Preferences - Find SaaS M2NFCP

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