Cloud Service Selection Model Research Articles

A Fuzzy-Multi Attribute Decision Making approach for efficient service selection in cloud environments

In general, objective and subjective QoS assessment data forms the major data source for service evaluations. Especially, in the most dynamic service-oriented computing ecosystem like cloud environments, such assessment data is subjected to high uncertainty due to the dynamics in the number of cloud services and related QoS values. In such cases, the accuracy and reliability of the most efficient cloud service selection model remain questionable. Further, the complex interdependencies among the multiple QoS parameters and the trustworthiness of the cloud services complicate the process of handling uncertainty during cloud service selection. This work presents Fuzzy-Multi Attribute Decision Making (FMADM), a hybrid trust prediction model to provide accurate trust-based cloud service evaluations while efficiently handling the uncertainty in the cloud service assessment data. FMADM employs (i) Picture Fuzzy Sets (PFSs): to capture inconsistency, uncertainty, and ambiguity in the QoS data, (ii) Naïve Bayes: to recompute the weights of the QoS attributes, (iii) Measurement of Alternatives and Ranking according to COmpromise Solution (MARCOS): to model the non-linear relationship between the QoS values of cloud services and their corresponding trust result, and (iv) Random Forest Classifier (RFC): to predict the trust value of cloud services. The performance of FMADM was evaluated using Quality Web Service (QWS) v1.0 dataset under different scenarios using various quality metrics.

A dynamic cloud service selection model based on trust and SLA in cloud computing

For high quality and trusted service selection problem, we propose a Dynamic Cloud Services Selection Model (DCSSM). Cloud service resources are divided into different service levels by Service-Level Agreement Management (SLAM). Each SLAM manages some cloud service registration information. In order to make the final trust evaluation values more practical, the model performs a comprehensive trust, which consists of direct trust and indirect trust. Firstly, combining weights consist of subjective weight and objective weight. Using rough sets to calculate objective weight, an Analytic Hierarchy Process (AHP) method is used to calculate subjective weight. The direct trust also considers transaction time and transaction amount, and then gets an accurate direct trust. Secondly, indirect trust considers user trust evaluation similarity, and it contains indirect trust of friends and indirect trust of strangers. Finally, when the transaction was completed, a direct trust dynamic update was proposed. The model is simulated using CloudSim. It was compared with three other methods, and the experimental results showed that the DCSSM performs better than the other three methods.

Prioritizing the solution of cloud service selection using integrated MCDM methods under Fuzzy environment

Cloud service selection plays a crucial role in terms of on-demand service selection on a subscription basis. As a result of wide-range availability of cloud services with similar functionalities, it is very crucial to determine which service best addresses the user’s desires and objectives. This paper aims to design a new cloud service selection model under the fuzzy environment by utilizing the analytical hierarchy process (AHP) and fuzzy technique for order preference by similarity to ideal solution (TOPSIS). The AHP method is enforced to configure the structure of cloud service selection problem and to impel the criteria weight using the pairwise comparisons, and the TOPSIS method utilizes the final ranking of the solution. In our proposed model, the non-functional quality of service requirements is taken into consideration for selecting appropriate service. Furthermore, the proposed model exploits a set of pre-defined linguistic variables, parameterized by triangular fuzzy numbers for evaluating each criteria weights. The experimental results obtained using the real-time cloud service domains prove the efficacy of our proposed model and demonstrate the effectiveness by inducing better performance, when compared against other available cloud service selection algorithms. Finally, the sensitivity analysis is persuaded to confirm the robustness of our proposed model.

A cloud service selection model using improved ranked voting method

SummaryCloud computing is an upcoming and promising solution for utility computing that provides resources on demand. As it has grown into a business model, a large number of cloud service providers exist today in the cloud market, which further is expanding exponentially. Many cloud service providers, with almost similar functionality, pose a selection problem to the cloud users. To assist the users in the best service selection, as per its requirement, a framework has been developed in which users list their quality of service (QoS) expectation, while service providers express their offerings. Experience of the existing cloud users is also taken into account in order to select the best cloud service provider. This work identifies some new QoS metrics, besides few existing ones, and defines it in a way that eases both the user and the provider to express their expectations and offers, respectively, in a quantified manner. Further, a dynamic and flexible model, using a variant of ranked voting method, is proposed that considers users' requirement and suggests the best cloud service provider. Case studies affirm the correctness and the effectiveness of the proposed model. Copyright © 2016 John Wiley & Sons, Ltd.

Trust-Enhanced Cloud Service Selection Model Based on QoS Analysis.

Cloud computing technology plays a very important role in many areas, such as in the construction and development of the smart city. Meanwhile, numerous cloud services appear on the cloud-based platform. Therefore how to how to select trustworthy cloud services remains a significant problem in such platforms, and extensively investigated owing to the ever-growing needs of users. However, trust relationship in social network has not been taken into account in existing methods of cloud service selection and recommendation. In this paper, we propose a cloud service selection model based on the trust-enhanced similarity. Firstly, the direct, indirect, and hybrid trust degrees are measured based on the interaction frequencies among users. Secondly, we estimate the overall similarity by combining the experience usability measured based on Jaccard’s Coefficient and the numerical distance computed by Pearson Correlation Coefficient. Then through using the trust degree to modify the basic similarity, we obtain a trust-enhanced similarity. Finally, we utilize the trust-enhanced similarity to find similar trusted neighbors and predict the missing QoS values as the basis of cloud service selection and recommendation. The experimental results show that our approach is able to obtain optimal results via adjusting parameters and exhibits high effectiveness. The cloud services ranking by our model also have better QoS properties than other methods in the comparison experiments.

A Cloud Service Selection Model based on Analytic Network Process

A cloud computing service is the next-generation core service of green ICT for operational management of ICT resources and maximization of energy consumption efficiency. Cloud service is expected to rapidly increase in its market size in the future through its convenience in usage, efficiency of operational management and economic effects and so on. In users’ perspectives, the choice of the appropriative cloud service is very important to meet their business goals and strategies. A cloud service selection problem is a multiple-criteria decision analysis with feedback. An Analytical Network Process (ANP) is a popular decision analysis technique. This study proposes a cloud service selection model based on ANP. According to the proposed model, the criteria and sub-factors for cloud service selection are identified and then determined their weights. In this study, ANP based the multiple criteria decision-making model is applied to choose the best cloud services.

CCCloud: Context-Aware and Credible Cloud Service Selection Based on Subjective Assessment and Objective Assessment

Due to the diversity and dynamic nature of cloud services, it is usually hard for potential cloud consumers to select the most suitable cloud service. This paper proposes CCCloud: a context-aware and credible cloud service selection model based on the comparison and aggregation of subjective assessments extracted from ordinary cloud consumers and objective assessments from quantitative performance testing parties. We propose a novel approach to evaluate cloud users' credibility, which not only can accurately evaluate how truthfully they assess cloud services, but also resist user collusion. In addition, in our model, objective assessments are used as benchmarks to filter out potentially biased subjective assessments, and then objective assessments and subjective assessments are aggregated to evaluate the overall performance of a cloud service. Furthermore, our model takes the contexts of objective assessments and subjective assessments into account. By calculating the similarity between different contexts, the benchmark level of objective assessments is dynamically adjusted according to context similarity, and the aggregated final scores of alternative cloud services are weighted by the similarity between the contexts of a potential cloud consumer and every testing party. This makes our cloud service selection model reflect potential cloud consumers' customized requirements more effectively. Finally, our proposed model is evaluated through the experiments conducted under different conditions. The experimental results demonstrate that our model significantly outperforms the existing work, especially in the resistance of user collusion.

CC-PSM: A Preference-Aware Selection Model for Cloud Service Based on Consumer Community

In order to give full consideration to the consumer’s personal preference in cloud service selection strategies and improve the credibility of service prediction, a preference-aware cloud service selection model based on consumer community (CC-PSM) is presented in this work. The objective of CC-PSM is to select a service meeting a target consumer’s demands and preference. Firstly, the correlation between cloud consumers from a bipartite network for service selection is mined to compute the preference similarity between them. Secondly, an improved hierarchical clustering algorithm is designed to discover the consumer community with similar preferences so as to form the trusted groups for service recommendation. In the clustering process, a quantization function called community degree is given to evaluate the quality of community structure. Thirdly, a prediction model based on consumer community is built to predict a consumer’s evaluation on an unknown service. The experimental results show that CC-PSM can effectively partition the consumers based on their preferences and has good effectiveness in service selection applications.

Dynamic cloud service selection using an adaptive learning mechanism in multi-cloud computing

Cloud service selection in a multi-cloud computing environment is receiving more and more attentions. There is an abundance of emerging cloud service resources that makes it hard for users to select the better services for their applications in a changing multi-cloud environment, especially for online real time applications. To assist users to efficiently select their preferred cloud services, a cloud service selection model adopting the cloud service brokers is given, and based on this model, a dynamic cloud service selection strategy named DCS is put forward. In the process of selecting services, each cloud service broker manages some clustered cloud services, and performs the DCS strategy whose core is an adaptive learning mechanism that comprises the incentive, forgetting and degenerate functions. The mechanism is devised to dynamically optimize the cloud service selection and to return the best service result to the user. Correspondingly, a set of dynamic cloud service selection algorithms are presented in this paper to implement our mechanism. The results of the simulation experiments show that our strategy has better overall performance and efficiency in acquiring high quality service solutions at a lower computing cost than existing relevant approaches.