This paper proposes a QoS ranking prediction framework for cloud services that does not require additional real-world service invocations. It takes advantage of past service usage experiences from other consumers to predict QoS rankings for cloud services. Two personalized approaches are developed to directly predict QoS rankings. Experiments using real-world data from 300 distributed users and 500 cloud services worldwide show the approaches outperform other methods.

1. QoS Ranking Prediction for Cloud Services
Abstract:
Cloud computing is becoming popular. Building high quality cloud applications is a critical
research problem. QoS rankings provide valuable information for making optimal cloud service
selection from a set of functionally equivalent service candidates. To obtain QoS values, realworld invocations on the service candidates are usually required. To avoid the time-consuming
and expensive real-world service invocations, this paper proposes a QoS ranking prediction
framework for cloud services by taking advantage of the past service usage experiences of other
consumers. Our proposed framework requires no additional invocations of cloud services when
making QoS ranking prediction. Two personalized QoS ranking prediction approaches are
proposed to predict the QoS rankings directly. Comprehensive experiments are conducted
employing real-world QoS data, including 300 distributed users and 500 real-world Web services
all over the world. The experimental results show that our approaches outperform other
competing approaches.
Existing System:
QoS rankings provide valuable information for making optimal cloud service selection from a set
of functionally equivalent service candidates. To obtain QoS values, real-world invocations on
the service candidates are usually required. It is Time consuming and expensive.
Proposed System:
To avoid the time-consuming and expensive real-world service invocations, this paper proposes
a QoS ranking prediction framework for cloud services by taking advantage of the past service
usage experiences of other consumers. Our proposed framework requires no additional
invocations of cloud services when making QoS ranking prediction. Two personalized QoS
ranking prediction approaches are proposed to predict the QoS rankings directly. Comprehensive
experiments are conducted employing real-world QoS data, including 300 distributed users and
500 real-world Web services all over the world.

2. Modules:
1. Adminstrator:
The administrator will first login on to the site and he will add the details about Hotels, Cabs
and Airline tickets. Based on the requirement the users will utilize the services and they will
book the services. After that based on the utilization the quality of the service is improves. A
graph is generated for each service. The graph is visible to administrator only. After viewing the
ratings the admin will update the details.
2. Users:
The user will login on to the system and he will view the services provided by admin. Based
on his/her requirement the user will reserve the service. While reserving the service the user will
enter his/her details. After reservation the quality of the service is increased. This is know to the
administrator.
Hard Ware Requirements:
Processor:: Pentium-III (or) Higher
Ram:: 64MB (or) Higher
Cache:: 512MB
Hard disk:: 10GB
Soft Ware Requirements:
Operating System
: Windows 2000 server Family.
Techniques
: JDK 1.5

3. Data Bases
: My SQL
User Interface(GUI)
: HTML,CSS,JAVASCRIPT
Server Programing
:Servlet,JSP