Context-Aware Prediction of QoS and QoE Properties for Web Services

Baraki, Harun; Comes, Diana Elena; Geihs, Kurt

doi:10.1109/netsys.2013.14

Cited by 6 publications

(5 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similarly, Baraki et al applied an MLPNN to predict three QoS values that are respectively, the response time, the throughput and the reputation for each Web Service. The predicted values are personalized for users since the context data are taken into account [4]. This work focuses only on the user context like geographical distance to perform predictions.…”

Section: Service Selection Based On Non-functional Propertiesmentioning

confidence: 99%

Scalable and Self-Adaptive Service Selection Method for the Internet of Things

Mejri¹,

Azzouna²

2017

IJCA

View full text Add to dashboard Cite

The Internet of Things goes beyond the regular Internet by offering new functionalities and creating new range of services provided by the deployed objects. Therefore, one of the most challenging issues is to select the best service among similar functionally available ones. In this paper, we propose to involve both artifcial intelligence through the use of Artifcial Neural Network (ANN) and multi criteria analysis through the use of Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) model in order to return the best service to the requestor. First, The ANN is introduced as a predictive model to estimate the Qualities of services (QoS) according to user context, service context and network context. Second, the TOPSIS model evaluates, then aggregates these QoS values in order to provide the best service according to user preferences. To improve the scalability of the proposed service selection system we conduct a parallel implementation of the prototype. General TermsService selection in the IoT

show abstract

Section: Service Selection Based On Non-functional Propertiesmentioning

confidence: 99%

Scalable and Self-Adaptive Service Selection Method for the Internet of Things

Mejri¹,

Azzouna²

2017

IJCA

View full text Add to dashboard Cite

show abstract

“…The research problem of QoS prediction is how to accurately predict the missing QoS values by employing the available previous collected QoS values from other users' experience (Zheng et al, 2014). There are many works in QoS prediction for new users (Wang et al, 2013;Baraki et al, 2013;Ge et al, 2010;Yu, 2012). For example, Wang et al (2013) propose a model that integrates QoS prediction and leads to a composition result that fulfills and maintains the user requirement.…”

Section: Related Workmentioning

confidence: 99%

“…For example, Wang et al (2013) propose a model that integrates QoS prediction and leads to a composition result that fulfills and maintains the user requirement. Baraki et al (2013) present two algorithms to predict QoS and Quality of Experience (QoE), where the predictions are based solely on previously collected QoS and QoE data. Yu (2012) presents a strategy that uses decision tree learning to bootstrap service recommendation systems.…”

Section: Related Workmentioning

confidence: 99%

Bootstrapping quality of Web Services

Aljazzaf

2015

Journal of King Saud University - Computer and Information Scie

View full text Add to dashboard Cite

A distributed application may be composed of global services provided by different organizations and having different properties. To select a service from many similar services, it is important to distinguish between them. Quality of services (QoS) has been used as a distinguishing factor between similar services and plays an important role in service discovery, selection, and composition. Moreover, QoS is an important contributing factor to the evolution of distributed paradigms, such as service-oriented computing and cloud computing. There are many research works that assess services and justify the QoS at the finding, composition, or binding stages of services. However, there is a need to justify the QoS once new services are registered and before any requestors use them; this is called bootstrapping QoS. Bootstrapping QoS is the process of evaluating the QoS of the newly registered services at the time of publishing the services. Thus, this paper proposes a QoS bootstrapping solution for Web Services and builds a QoS bootstrapping framework. In addition, Service Oriented Architecture (SOA) is extended and a prototype is built to support QoS bootstrapping. Experiments are conducted and a case study is presented to test the proposed QoS bootstrapping solution. ª 2015 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

show abstract

“…The goal is to discover promising evolution patterns by fostering successful and proven evolution procedures and preventing unsuccessful ones. Success does not only depend on smooth running in a technical sense, but has to consider 4 …”

Section: Evolution Analyticsmentioning

confidence: 99%

“…Here, Evolution Analytics has to weigh the reputation against other factors like the costs for updates and the future revenue. To estimate reputation, costs and QoS, we will make use of our two prediction algorithms presented in [4].…”

Section: Eai Endorsed Transactions Onmentioning

confidence: 99%

Service Co-evolution in the Internet of Things

Tran¹,

Baraki²,

Geihs³

2015

EAI Endorsed Transactions on Cloud Systems

View full text Add to dashboard Cite

The envisioned Internet of Things (IoT) foresees a future Internet incorporating smart physical objects that offer hosted functionality as IoT services. This service-based integration of IoT will be smarter, easier to communicate with and more valuable for enriching our environment. However, the interfaces and services can be modified due to updates and amendments. Such modifications require adaptations in all participating parties. Therefore, the aim of this research is to present a vision of service co-evolution in IoT. Moreover, we propose a novel agent architecture which supports the evolution by controlling service versions, updating local service instances and enabling the collaboration of agents. In this way, the service co-evolution can make systems more adaptive, efficient and reduce costs to manage maintenance.

show abstract

Context-Aware Prediction of QoS and QoE Properties for Web Services

Cited by 6 publications

References 12 publications

Scalable and Self-Adaptive Service Selection Method for the Internet of Things

Scalable and Self-Adaptive Service Selection Method for the Internet of Things

Bootstrapping quality of Web Services

Service Co-evolution in the Internet of Things

Contact Info

Product

Resources

About