The Research Of Personalized Cloudmedia Service Selection Based On QoS-aware

With the development and application of cloud computing, cloudmedia as a special kind of cloud computing mode has been one of the most popular research topic in the media service field. Cloudmedia can effectively deal with multimedia services and provide a large number of media applications, due to its flexibility and agility. Compared with the traditional media services, cloudmedia service has many advantages in business, it has promoted many media services organization to develop new services and fusion their existing media applications in the cloudmedia environment. In recent years, cloudmedia has a broad application prospect especially in the office learning, radio and television communications, medical and health care, movie and music recommendation, electronic tourism and other fields. In order to maximize the potential of cloudmedia, one of the key problem is to ensure that users’ specific needs and quality of service (QoS) can be meet by service providers.With the rapid development of cloudmedia services, a large number of service providers, they can provide similar services in different level of price and performance. In order to select suitable service for user, the user’s demand and level of QoS is the focus needs to consider. Get QoS performance and according to every user’s preference to choose the appropriate service, improve service quality and customer satisfaction is an important task of the present study.Based on the above analysis, this paper improves service quality mainly from the aspects of service selection. In the cloudmedia environment, this paper analyzes the relevant technology of the service selection, take advantage of QoS prediction technology and TOPSIS technology, combined with the user preference to select service, proposes algorithm PCSSQ (Personalized Cloudmedia Service Selection based on QoS-aware). The algorithm firstly gets user QoS preferences according to user’s cloud media service requests, uses the QoS historical information of available media services and multiple linear regression technique to calculate each service QoS prediction model, and according to the QoS index to calculate the predicted value of the each service QoS and filter services. Secondly, using TOPSIS method, combined with the user preference of each index. Finally get the service with the biggest similarity as the ideal service and provide it to the user.Innovation points of this paper is using multiple linear regression technology to predict QoS, and on the basis of introducing the concept of similarity, combined with the user preferences, using TOPSIS technology, to select the service with the biggest similarity to the ideal service, improve the quality of cloudmedia services and user satisfaction.Cloudmedia services selection algorithm has become a hot problem in the study on service selection technology. In the cloudmedia environment, an effective service selection algorithm can consider service quality and user preferences to ensure user to select the satisfactory service.Ensure the quality of service for the user, at the same time, meet their individual needs, has a certain theoretical significance and practical value.