Personalized decision making for QOS-based web service selection

In order to choose from a list of functionally similar services, users often need to make their decisions based on multiple QoS criteria they require on the target service. In this process, different users may follow different decision making strategies, some are compensatory and some are non-compensatory. Most of the current QoS-based service selection systems do not consider these decision strategies in the ranking process, which we believe are crucial for generating accurate ranking results for individual users. In this thesis, we propose a decision strategy based service ranking model. Furthermore, considering that different users follow different strategies in different contexts at different times, we apply a learning to rank algorithm to learn a personalized ranking model for individual users based on how they select services in the past. Our experiment result shows the effectiveness of the proposed approach.

Personalized decision making for QOS-based web service selection

In order to choose from a list of functionally similar services, users often need to make their decisions based on multiple QoS criteria they require on the target service. In this process, different users may follow different decision making strategies, some are compensatory and some are non-compensatory. Most of the current QoS-based service selection systems do not consider these decision strategies in the ranking process, which we believe are crucial for generating accurate ranking results for individual users. In this thesis, we propose a decision strategy based service ranking model. Furthermore, considering that different users follow different strategies in different contexts at different times, we apply a learning to rank algorithm to learn a personalized ranking model for individual users based on how they select services in the past. Our experiment result shows the effectiveness of the proposed approach.

Enhanced Algorithm to Optimize QoS and Security Parameters in Ad hoc Networks

In ad hoc networks, it is essential and desirable that running applications offer appropriate QoS. However, it becomes a challenge, especially in wireless sensor networks. This chapter concentrates on integrating the security aspect with those of disconnection because of the excessive utilization of a particular node. It is seen that in ad-hoc networks that it is possible to enhance the network lifetime significantly by incorporating the battery properties in the routing protocol. So that will increase the network performance while routing protocol integrates the security with battery to support the simulation results. These results show that adaptive DSR protocol maintains useful network lifetime in addition to improved performance. The chapter attempts to improve the overall QoS of the MANET. It is well known that QoS provisioning is an intricate and challenging issue in mobile ad hoc networks, especially when there are multiple QoS constraints.

A Novel Hybrid Optimization-Based Approach for Efficient Development of Business-Applications in Cloud

The requests of the companies for the development and deployment of their business-applications in Cloud become more complex so that, sometimes, a one single service cannot carry out the target task on its own. Hence, a user-request is provided as a composite service. On another note, the number of available services is significantly increasing. Therefore, the authors would need to find the optimal cloud service-compositions that satisfy the quality of service values as well as user requirements. The methods proposed in literature for composing cloud services do not consider the composition and deployment constraints of candidate cloud services. This paper presents a novel optimization-based approach for building business-application in Cloud. The proposed approach combines the particle swarm optimization algorithm with some principles of ant colony optimization algorithm to deal with multiple QoS parameters, but also to satisfy the composition and deployment constraints of cloud services. The experimental results show the efficiency of the method for all tests instances.

Mathematical model of improved reverse charging of wireless internet pricing scheme in servicing multiple QoS

This paper seeks to utilize the improved model of reverse charging scheme. Reverse charging basically is defined as a capability of stored network that replaces the network used when the network is suddenly shut down. In this paper, charging back on 3G and 4G network that is user automated platform, will change the access of 4G to 3G and on the contrary when platform conduct the hosting. This research was solved as a problem Mixed Integer Nonlinear Programming (MINLP) by LINGO 13.0. An optimal pricing scheme is applied to a local data server, including digilib traffic and mail traffic. The improved model of Reverse Charging is modified into 4 cases and formed by setting the base price (α) and service level (β). Based on the analysis that has been done, the results of this study indicate that the reverse charging model can be utilized Internet Service Provider (ISP) to maximize profits and provide quality services for the user if compared to previous model without reverse charging scheme. Keywords: improved model of revere charging scheme, MINLP, ISP, QoS, pricing scheme