Geographic Location-Based Network-Aware QoS Prediction for Service Composition

2013 ◽  
Author(s):  
Yuanhong Shen ◽  
Jianke Zhu ◽  
Xinyu Wang ◽  
Liang Cai ◽  
Xiaohu Yang ◽  
...  
Keyword(s):  
Service Composition ◽  
Geographic Location ◽  
Qos Prediction

scholarly journals A QoS-Oriented Novel Optimization Schemes for Web Service Composition for Improved Healthcare

2019 ◽  
Vol 8 (12) ◽  
pp. 3442-3447
Keyword(s):  
Web Service ◽  
Service Composition ◽  
Target Location ◽  
Optimal Solution ◽  
Service Selection ◽  
Web Service Composition ◽  
Qos Prediction ◽  
Web Service Selection ◽  
Evidenced Based ◽  
Optimization Schemes

Due to massive increase in Web service in order to provide users an improved service it is necessary to develop a dynamic approach based on multi constraint Quality-of-Service (QoS) driven web service composition model to recommend suitable services to intended users is a big challenge. QoS plays a prominent role in selecting a Web Service. QoS is mainly determined by several non-functional parameters like availability, reliability, robustness, integrity, accessibility, interoperability, accuracy and security. It is evidenced based on experimentation that the proposed Improved Rider Optimization scheme achieves near optimal solution where group of riders racing towards a target location and attains adaptability and scalability by making a more confident web service selection with QoS prediction.

scholarly journals End-to-end QoS computation for vertical service composition in the cloud

2021 ◽  
Author(s):  
Raed Karim
Keyword(s):  
Service Composition ◽  
Cloud Service ◽  
Cloud Services ◽  
Prior History ◽  
Functional Requirements ◽  
Active System ◽  
Qos Prediction ◽  
Cloud Providers ◽  
End To End ◽  
Prediction Approach

Cloud services are designed to provide users with different computing models such as software-as-a-Services (SaaS), Infrastructure-as-a-Service (IaaS), Data-as-a-Service (DaaS), and other IT related services (denoted as XaaS). Easy, scalable and on-demand cloud services are offered by cloud providers to users. With the prevalence of different types of cloud services, the task of selecting the best cloud service solution has become more and more challenging. Cloud service solutions are offered through a collaboration of different cloud services at different cloud layers. This type of collaborations is denoted as vertical service composition. Quality of Service (QoS) properties are used as differentiating factors for selecting the best services among functionally equivalent services. In this thesis, we introduce a new service selection framework for the cloud which vertically matches services offered by different cloud providers based on users’ end-to-end QoS requirements. Functional requirements can be satisfied by the required cloud service (software service, platform service, etc) alone. However, users’ QoS requirements must be satisfied using all involved cloud services in a service composition. Therefore, in order to select the best cloud service compositions for users, QoS values of these compositions must be end-to-end. To tackle the problem of computing unknown end-to-end QoS values of vertical cloud service compositions for target users (for whom these values are computed), we propose two strategies: QoS mapping and aggregation and QoS prediction. The former deals with new cloud service compositions with no prior history. Using this strategy, we can map users’ QoS requirements onto different cloud layers and then we aggregate QoS values guaranteed by cloud providers to estimate end-to-end QoS values. The latter deals with cloud service compositions for which QoS data have been recorded in an active system. Using the QoS prediction strategy, we utilize historical QoS data of previously invoked service compositions and other service and user information to predict end-to-end QoS values. The presented experimental results demonstrate the importance of considering vertically composed cloud services when computing end-to-end QoS values as opposed to traditional prediction approaches. Our QoS prediction approach outperforms other prediction approaches in terms of the prediction accuracy by at least 20%.

scholarly journals End-to-end QoS computation for vertical service composition in the cloud

2021 ◽  
Author(s):  
Raed Karim
Keyword(s):  
Service Composition ◽  
Cloud Service ◽  
Cloud Services ◽  
Prior History ◽  
Functional Requirements ◽  
Active System ◽  
Qos Prediction ◽  
Cloud Providers ◽  
End To End ◽  
Prediction Approach

Cloud services are designed to provide users with different computing models such as software-as-a-Services (SaaS), Infrastructure-as-a-Service (IaaS), Data-as-a-Service (DaaS), and other IT related services (denoted as XaaS). Easy, scalable and on-demand cloud services are offered by cloud providers to users. With the prevalence of different types of cloud services, the task of selecting the best cloud service solution has become more and more challenging. Cloud service solutions are offered through a collaboration of different cloud services at different cloud layers. This type of collaborations is denoted as vertical service composition. Quality of Service (QoS) properties are used as differentiating factors for selecting the best services among functionally equivalent services. In this thesis, we introduce a new service selection framework for the cloud which vertically matches services offered by different cloud providers based on users’ end-to-end QoS requirements. Functional requirements can be satisfied by the required cloud service (software service, platform service, etc) alone. However, users’ QoS requirements must be satisfied using all involved cloud services in a service composition. Therefore, in order to select the best cloud service compositions for users, QoS values of these compositions must be end-to-end. To tackle the problem of computing unknown end-to-end QoS values of vertical cloud service compositions for target users (for whom these values are computed), we propose two strategies: QoS mapping and aggregation and QoS prediction. The former deals with new cloud service compositions with no prior history. Using this strategy, we can map users’ QoS requirements onto different cloud layers and then we aggregate QoS values guaranteed by cloud providers to estimate end-to-end QoS values. The latter deals with cloud service compositions for which QoS data have been recorded in an active system. Using the QoS prediction strategy, we utilize historical QoS data of previously invoked service compositions and other service and user information to predict end-to-end QoS values. The presented experimental results demonstrate the importance of considering vertically composed cloud services when computing end-to-end QoS values as opposed to traditional prediction approaches. Our QoS prediction approach outperforms other prediction approaches in terms of the prediction accuracy by at least 20%.

Network-Aware QoS Prediction for Service Composition Using Geolocation

2015 ◽  
Vol 8 (4) ◽  
pp. 630-643 ◽  
Author(s):  
Xinyu Wang ◽  
Jianke Zhu ◽  
Yuanhong Shen
Keyword(s):  
Service Composition ◽  
Qos Prediction

Diminishing Barriers to the Praxis Examination in Communication Sciences and Disorders

2011 ◽  
Vol 1 (2) ◽  
pp. 64-71
Author(s):  
Luis F. Riquelme
Keyword(s):  
Geographic Location ◽  
The United States ◽  
Success For All ◽  
Speech Language Pathology ◽  
Barriers To Success ◽  
Call To Action ◽  
Language Pathology ◽  
National Certification ◽  
Race Ethnicity ◽  
Passing Score

Abstract Passing the Praxis Examination in speech-language pathology or audiology can be a difficult task. A passing score is the entry to a list of requirements for national certification (CCC-SLP, CCC-A) and for state licensure in the United States. This article will provide current information on the examination and address barriers to success that have been identified over the years. A call to action may serve to refocus efforts on improving access to success for all test-takers regardless of race/ethnicity, ability, or geographic location.

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