Abstract
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.
Highlights
Cloud computing is a model of service provisioning in which dynamically scalable and virtualized resources, that includes infrastructure, platform, and software, are delivered and accessed as services over the internet [1, 2]
Despite the fact that existing cloud e-marketplaces do not consider user’s quality of service (QoS) requirements, the search results are presented as an unordered list of icons making it difficult for users to discriminate among services shown
We demonstrated the plausibility of applying heterogeneous similarity metrics in ranking cloud services and evaluated the performance of five heterogeneous similarity metrics using rankings produced by the human judgement as a benchmark
Summary
Cloud computing is a model of service provisioning in which dynamically scalable and virtualized resources, that includes infrastructure, platform, and software, are delivered and accessed as services over the internet [1, 2]. Most cloud service e-marketplaces in existence do not consider QoS information from the users but rely on keyword matching, and the results are not ranked in a manner that makes the differences among the services to be obvious with respect to users’ requirements. This leads to service choice overload because a large number of services are presented as an unordered list of icons that require the user to further investigate the differences between the services by checking them one after the other. One of the most comprehensive International Standard Organization (ISO) certified QoS model for cloud services is the Service Measurement Index (SMI) [13]
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