PQsel: combining privacy with quality of service in cloud service selection

2016 ◽  
Vol 3 (3) ◽  
pp. 202 ◽  
Author(s):  
Li Lin ◽  
Tingting Liu ◽  
Jian Hu ◽  
Jian Ni
Keyword(s):  
Quality Of Service ◽  
Service Selection ◽  
Cloud Service

Multi-criteria Web Services Selection: Balancing the Quality of Design and Quality of Service

2022 ◽  
Vol 22 (1) ◽  
pp. 1-31
Author(s):  
Marwa Daaji ◽  
Ali Ouni ◽  
Mohamed Mohsen Gammoudi ◽  
Salah Bouktif ◽  
Mohamed Wiem Mkaouer
Keyword(s):  
Quality Of Service ◽  
Web Services ◽  
Web Service ◽  
State Of The Art ◽  
Service Selection ◽  
Service Design ◽  
Design Quality ◽  
Trade Off ◽  
Selection Approach

Web service composition allows developers to create applications via reusing available services that are interoperable to each other. The process of selecting relevant Web services for a composite service satisfying the developer requirements is commonly acknowledged to be hard and challenging, especially with the exponentially increasing number of available Web services on the Internet. The majority of existing approaches on Web Services Selection are merely based on the Quality of Service (QoS) as a basic criterion to guide the selection process. However, existing approaches tend to ignore the service design quality, which plays a crucial role in discovering, understanding, and reusing service functionalities. Indeed, poorly designed Web service interfaces result in service anti-patterns, which are symptoms of bad design and implementation practices. The existence of anti-pattern instances in Web service interfaces typically complicates their reuse in real-world service-based systems and may lead to several maintenance and evolution problems. To address this issue, we introduce a new approach based on the Multi-Objective and Optimization on the basis of Ratio Analysis method (MOORA) as a multi-criteria decision making (MCDM) method to select Web services based on a combination of their (1) QoS attributes and (2) QoS design. The proposed approach aims to help developers to maintain the soundness and quality of their service composite development processes. We conduct a quantitative and qualitative empirical study to evaluate our approach on a Quality of Web Service dataset. We compare our MOORA-based approach against four commonly used MCDM methods as well as a recent state-of-the-art Web service selection approach. The obtained results show that our approach outperforms state-of-the-art approaches by significantly improving the service selection quality of top- k selected services while providing the best trade-off between both service design quality and desired QoS values. Furthermore, we conducted a qualitative evaluation with developers. The obtained results provide evidence that our approach generates a good trade-off for what developers need regarding both QoS and quality of design. Our selection approach was evaluated as “relevant” from developers point of view, in improving the service selection task with an average score of 3.93, compared to an average of 2.62 for the traditional QoS-based approach.

Dynamic QoS-Aware Cloud Service Selection Using Best-Worst Method and Timeslot Weighted Satisfaction Scores

2020 ◽  
Author(s):  
Falak Nawaz ◽  
Naeem Khalid Janjua
Keyword(s):  
Decision Making ◽  
Service Selection ◽  
Cloud Service ◽  
Cloud Services ◽  
Satisfaction Score ◽  
Analytic Hierarchy ◽  
Hierarchy Process ◽  
Best Worst Method ◽  
Selection Of

Abstract The number of cloud services has dramatically increased over the past few years. Consequently, finding a service with the most suitable quality of service (QoS) criteria matching the user’s requirements is becoming a challenging task. Although various decision-making methods have been proposed to help users to find their required cloud services, some uncertainties such as dynamic QoS variations hamper the users from employing such methods. Additionally, the current approaches use either static or average QoS values for cloud service selection and do not consider dynamic QoS variations. In this paper, we overcome this drawback by developing a broker-based approach for cloud service selection. In this approach, we use recently monitored QoS values to find a timeslot weighted satisfaction score that represents how well a service satisfies the user’s QoS requirements. The timeslot weighted satisfaction score is then used in Best-Worst Method, which is a multi-criteria decision-making method, to rank the available cloud services. The proposed approach is validated using Amazon’s Elastic Compute Cloud (EC2) cloud services performance data. The results show that the proposed approach leads to the selection of more suitable cloud services and is also efficient in terms of performance compared to the existing analytic hierarchy process-based cloud service selection approaches.

scholarly journals Service Association Factor (SAF) for Cloud Service Selection and Recommendation

2020 ◽  
Vol 49 (1) ◽  
pp. 113-126
Author(s):  
Imran Mujaddid Rabbani ◽  
Muhammad Aslam ◽  
Ana Maria Martinez Enriquez ◽  
Zeeshan Qudeer
Keyword(s):  
Recommendation System ◽  
Service Providers ◽  
Service Selection ◽  
Cloud Service ◽  
Base System ◽  
End User ◽  
Cloud Models ◽  
Science Domain ◽  
Quality Of Service Parameters

Cloud computing is one of the leading technology in IT and computer science domain. Business IT infrastructures are equipping themselves with modern regime of clouds. In the presence of several opportunities, selection criteria decision becomes vital when there is no supporting information available. Global clouds also need evaluation and assessment from its users that what they think about and how new ones could make their selection as per their needs. Recommended systems were built to propose best services using customer's feedback, applying quality of service parameters, assigning scores, trust worthiness and clustering in different forms and models. These techniques did not record and use interrelationships between the services that is true impact of service utilization. In the proposed approach, service association factor calculates value of interrelations among services used by the end user. An intelligent leaning based recommendation system is developed for assisting users to select services on their respective preferences. This technique is evaluated on leading service providers and results show that learning base system performs well on all types of cloud models.

scholarly journals Model-Based Cloud Service Deployment Optimisation Method For Minimisation of Application Service Operational Cost

2021 ◽  
Author(s):  
Ivana Stupar ◽  
Darko Huljenić
Keyword(s):  
Quality Of Service ◽  
Cloud Service ◽  
Operational Cost ◽  
Cloud Infrastructure ◽  
Application Service ◽  
Cloud Application ◽  
Wide Range ◽  
Service Deployment ◽  
Cloud Applications

Abstract Many of the currently existing solutions for cloud cost optimisation are aimed at cloud infrastructure providers, and they often deal only with specific types of application services, leaving the providers of cloud applications without the suitable cost optimization solution, especially concerning the wide range of different application types. In this paper, we present an approach that aims to provide an optimisation solution for the providers of applications hosted in the cloud environments, applicable at the early phase of a cloud application lifecycle and for a wide range of application services. The focus of this research is development of the method for identifying optimised service deployment option in available cloud environments based on the model of the service and its context, with the aim of minimising the operational cost of the cloud service, while fulfilling the requirements defined by the service level agreement. A cloud application context metamodel is proposed that includes parameters related to both the application service and the cloud infrastructure relevant for the cost and quality of service. By using the proposed optimisation method, the knowledge is gained about the effects that the cloud application context parameters have on the service cost and quality of service, which is then used to determine the optimised service deployment option. The service models are validated using cloud application services deployed in laboratory conditions, and the optimisation method is validated using the simulations based on proposed cloud application context metamodel. The experimental results based on two cloud application services demonstrate the ability of the proposed approach to provide relevant information about the impact of cloud application context parameters on service cost and quality of service, and use this information in the optimisation aimed at reducing service operational cost while preserving the acceptable service quality level. The results indicate the applicability and relevance of the proposed approach for cloud applications in the early service lifecycle phase since application providers can gain useful insights regarding service deployment decision without acquiring extensive datasets for the analysis.

scholarly journals Performance Evaluation of Quality of Service (QoS) based Web Service Selection Methods for Healthcare Product Shopping Domain Using Analysis of Variance (ANOVA)

2021 ◽  
Author(s):  
Maheswari S ◽  
Pitchai R ◽  
Supraja P ◽  
Babu S
Keyword(s):  
Quality Of Service ◽  
Web Service ◽  
Analysis Of Variance ◽  
Service Selection ◽  
Fuzzy Topsis ◽  
Selection Methods ◽  
Healthcare Product ◽  
Web Service Selection ◽  
Using Data

Abstract Guaranteeing Quality of Service (QoS) concerning web administrations is the capacity to respond to the necessities and comprehend the prerequisites according to the tendencies of a client. It is resolved subject to the non-practical properties of the web administrations. Dependent upon the conditions, administrations clients may have an extent of tendencies for the non utilitarian principles. This made a way for different QoS based web administrations assurance segments which along these lines require an evaluation plot for grasping a particular methodology. Investigation of change is a particular kind of verifiable hypothesis testing used for making decisions using data. The goal of the paper is to dismember the plans specifically Analytical Hierarchical preparing (AHP), Logical Scoring Preference (LSP) and Fuzzy Topsis reliant on extent of customer tendency norms and investigate them using Analysis of Variance (ANOVA). QWS dataset has been used for exploring recently referenced three plans. Preliminary outcomes show that the Fuzzy Topsis contrive beats the other web organization assurance techniques.

Optimizing Complex Service-Based Workflows for Stochastic QoS Parameters

2013 ◽  
Vol 10 (4) ◽  
pp. 1-38
Author(s):  
Dieter Schuller ◽  
Ulrich Lampe ◽  
Julian Eckert ◽  
Ralf Steinmetz ◽  
Stefan Schulte
Keyword(s):  
Quality Of Service ◽  
Service Selection ◽  
Case Analysis ◽  
Selection Problem ◽  
Worst Case Analysis ◽  
Average Case Analysis ◽  
Worst Case ◽  
Average Case ◽  
Qos Parameters

The challenge of optimally selecting services from a set of functionally appropriate ones under Quality of Service (QoS) constraints – the Service Selection Problem – has been extensively addressed in the literature based on deterministic parameters. In practice, however, Quality of Service QoS parameters rather follow a stochastic distribution. In the work at hand, we present an integrated approach which addresses the Service Selection Problem for complex structured as well as unstructured workflows in conjunction with stochastic Quality of Service parameters. Accounting for penalty cost which accrue due to Quality of Service violations, we perform a worst-case analysis as opposed to an average-case analysis aiming at avoiding additional penalties. Although considering conservative computations, QoS violations due to stochastic QoS behavior still may occur resulting in potentially severe penalties. Our proposed approach reduces this impact of stochastic QoS behavior on total cost significantly.

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