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, real-world
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 is an important research topic in cloud
computing. When making optimal cloud service selection from a set of
functionally equivalent services, QoS values of cloud services provide valuable
information to assist decision making. In traditional component-based systems,
software components are invoked locally, while in cloud applications, cloud
services are invoked remotely by Internet connections. Client-side performance
of cloud services is thus greatly influenced by the unpredictable Internet
connections. Therefore, different cloud applications may receive different
levels of quality for the same cloud service. In other words, the QoS ranking
of cloud services for a user (e.g.,cloud application1) cannot be transferred
directly to another user (e.g.,cloud application2), since the locations of the
cloud applications are quite different. Personalized cloud service QoS ranking
is thus required for different cloud applications.
DISADVANTAGES
OF EXISTING SYSTEM:
This approach is impractical in reality, since
invocations of cloud services may be charged. Even if the invocations are free,
executing a large number of service invocations is time consuming and resource
consuming, and some service invocations may produce irreversible effects in
the
real world.
When the number of candidate services is large, it
is difficult for the cloud application designers to evaluate all the cloud
services efficiently.
PROPOSED SYSTEM:
In this paper, we propose a personalized ranking
prediction framework, named Cloud Rank, to predict the QoS ranking of a set of
cloud services without requiring additional real-world service invocations from
the intended users. Our approach takes advantage of the past usage experiences
of other users for making personalized ranking prediction for the current user.
ADVANTAGES
OF PROPOSED SYSTEM:
This paper identifies the critical problem of
personalized QoS ranking for cloud services and proposes a QoS ranking
prediction framework to address the problem.
Extensive real-world experiments are conducted to study
the ranking prediction accuracy of our ranking prediction algorithms compared
with other competing ranking algorithms
SYSTEM ARCHITECTURE:
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
·
System : Pentium IV 2.4 GHz.
·
Hard Disk : 40 GB.
·
Monitor :
15 inch VGA Colour.
·
Mouse :
Logitech Mouse.
·
Ram : 512 MB
·
Keyboard :
Standard Keyboard
SOFTWARE REQUIREMENTS:
·
Operating System : Windows XP.
·
Coding Language : ASP.NET, C#.Net.
·
Database :
SQL Server 2005
REFERENCE:
Zibin Zheng,Member, IEEE, Xinmiao Wu, Yilei Zhang,
Student Member, IEEE, Michael R. Lyu,Fellow, IEEE, and Jianmin Wang “QoS
Ranking Prediction for Cloud Services”- IEEE
TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, VOL. 24, NO. 6, JUNE 2013.
