1,450 research outputs found
On Optimal and Fair Service Allocation in Mobile Cloud Computing
This paper studies the optimal and fair service allocation for a variety of
mobile applications (single or group and collaborative mobile applications) in
mobile cloud computing. We exploit the observation that using tiered clouds,
i.e. clouds at multiple levels (local and public) can increase the performance
and scalability of mobile applications. We proposed a novel framework to model
mobile applications as a location-time workflows (LTW) of tasks; here users
mobility patterns are translated to mobile service usage patterns. We show that
an optimal mapping of LTWs to tiered cloud resources considering multiple QoS
goals such application delay, device power consumption and user cost/price is
an NP-hard problem for both single and group-based applications. We propose an
efficient heuristic algorithm called MuSIC that is able to perform well (73% of
optimal, 30% better than simple strategies), and scale well to a large number
of users while ensuring high mobile application QoS. We evaluate MuSIC and the
2-tier mobile cloud approach via implementation (on real world clouds) and
extensive simulations using rich mobile applications like intensive signal
processing, video streaming and multimedia file sharing applications. Our
experimental and simulation results indicate that MuSIC supports scalable
operation (100+ concurrent users executing complex workflows) while improving
QoS. We observe about 25% lower delays and power (under fixed price
constraints) and about 35% decrease in price (considering fixed delay) in
comparison to only using the public cloud. Our studies also show that MuSIC
performs quite well under different mobility patterns, e.g. random waypoint and
Manhattan models
A Location Based Value Prediction for Quality of Web Service
The number of web services with functionality increases, the service users usually depends on web recommendation systems. Now a days the service users pay more importance on non functional properties which are also known as Quality of Service (QoS) while finding and selecting appropriate web services. Collaborative filtering approach predicts the QoS values of the web services effectively. Existing recommendation systems rarely consider the personalized influence of the users and services in determining the similarity between users and services. The proposed system is a ranking oriented hybrid approach which integrates user-based and item-based QoS predictions. Many of the non-functional properties depends on the user and the service location. The system thus employs the location information of users and services in selecting similar neighbors for the target user and service and thereby making personalized service recommendation for service users
Fuzzy Reasoning Approach for Predicting Web Services QoS/QoE with ANFIS
Nowadays, the web service (WS) usage in information systems (IS) includes determining a feasible WS that fulfils a set of non-functional requirements of Quality of Services (QoS) and user’s needs of Quality of Experience (QoE). While most existing studies evaluate WS from one perspective, i.e., users, and are based on data-driven approach, which employs a numerical dataset to learn a reasoning model, they overlook that users express their needs in a non-numerical form. To address these issues, we propose a new fuzzy reasoning approach for predicting WS QoS/QoE with the adaptive neuro-fuzzy inference system (ANFIS) that encompasses multiple viewpoints and perspectives, and is also suitable for linguistic terms. To verify the efficiency, we implemented the proposed approach, conducted two experiments and compared them. The results show a good performance of the proposed approach for predicting WS QoS/QoE, and, consequently, it can be considered a suitable tool for predicting
Collaborative Based Filtering Approach for Web Service Recommendations using GEO-Locations
Service computing is one of Internet-based computing, whereas the shared configurable resources (e.g., infrastructure, platform, and software) are provided to computers and other devices are as services. Strongly promoted by the leading industrial companies like, Amazon, Google, Microsoft, IBM, etc, In recent years, service computing are quickly becoming popular. Applications are deployed in real time environment are typically large scale and complex. The rising popularity of service computing, it is how to build high-quality service applications it becomes an urgently required research problem. In Similar, the traditional component-based systems, cloud applications are typically involves multiple cloud components communicating with each other over application programming interfaces, through web services. On-functional performance of cloud services are usually described by the quality-of-service (QoS). QoS is an important research topic in cloud computing. When the creation optimal cloud service selection from a set of functionally corresponding services, QoS values are of cloud services provided the valuable information to assist decision making. The component-based systems, software components are invoked locally in tradition, while in cloud applications, the cloud services are invoked remotely by Internet connections. To evade the slow and expensive real-world service invocation QoS ranking prediction framework is used. This framework requires no extra invocations of cloud services when making QoS ranking prediction can implement novel collaborative filtering approach to recommend the web services with improved performance.
DOI: 10.17762/ijritcc2321-8169.15033
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
Fuzzy Clustering Based Highly Accurate Prediction Algorithm for Unknown Web Services
In today�s reality, the measure of web administrations is expansions on web, so that determination and suggestion of web administration are becoming more imperative. In the fields of E-commerce and other Web-based services recommendation systems are extremely significant. Recommendation system first of all searches the list of web services those having similar functionality, which is user wants. By using filtering, separated the required list and finally on the basis of past records of service provider select out the optimal web services and recommend to users. In this paper predicts that much not known Web services QoS values more precisely than other accessible approaches. Also, we proposed the QoS prediction by utilizing fuzzy clustering technique with ascertaining the clients similarity. Our methodology enhances the prediction accuracy, and this is confirmed by contrasting investigations with different techniques
Challenges to describe QoS requirements for web services quality prediction to support web services interoperability in electronic commerce
Quality of service (QoS) is significant and necessary for web service applications quality assurance. Furthermore, web services quality has contributed to the successful implementation of Electronic Commerce (EC) applications. However, QoS is still the big issue for web services research and remains one of the main research questions that need to be explored. We believe that QoS should not only be measured but should also be predicted during the development and implementation stages. However, there are challenges and constraints to determine and choose QoS requirements for high quality web services. Therefore, this paper highlights the challenges for the QoS requirements prediction as they are not easy to identify. Moreover, there are many different perspectives and purposes of web services, and various prediction techniques to describe QoS requirements. Additionally, the paper introduces a metamodel as a concept of what makes a good web service
Qos-Based Web Service Discovery And Selection Using Machine Learning
In service computing, the same target functions can be achieved by multiple
Web services from different providers. Due to the functional similarities, the
client needs to consider the non-functional criteria. However, Quality of
Service provided by the developer suffers from scarcity and lack of
reliability. In addition, the reputation of the service providers is an
important factor, especially those with little experience, to select a service.
Most of the previous studies were focused on the user's feedbacks for
justifying the selection. Unfortunately, not all the users provide the feedback
unless they had extremely good or bad experience with the service. In this
vision paper, we propose a novel architecture for the web service discovery and
selection. The core component is a machine learning based methodology to
predict the QoS properties using source code metrics. The credibility value and
previous usage count are used to determine the reputation of the service.Comment: 8 Pages, 3 Figure
- …