a context aware service evaluation approach over big data for cloud applications

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IEEE PROJECTS DEVELOPMENTS WE OFFER IEEE PROJECTS MCA FINAL YEAR STUDENT PROJECTS, ENGINEERING PROJECTS AND TRAINING, PHP PROJECTS, JAVA AND J2EE PROJECTS, ASP.NET PROJECTS, NS2 PROJECTS, MATLAB PROJECTS AND IPT TRAINING . CELL: +91 8870791415 Mail to: [email protected] A Context-aware Service Evaluation Approach over Big Data for Cloud Applications Abstract Cloud computing has promoted the success of big data applications such as medical data analyses. With the abundant resources provisioned by cloud platforms, the QoS (quality of service) of services that process big data could be boosted significantly. However, due to unstable network or fake advertisement, the QoS published by service providers is not always trusted. Therefore, it becomes a necessity to evaluate the service quality in a trustable way, based on the services’ historical QoS records. However, the evaluation efficiency would be low and cannot meet users’ quick response requirement, if all the records of a service are recruited for quality evaluation. Moreover, it may lead to ‘Lagging Effect’ or low evaluation accuracy, if all the records are treated equally, as the invocation contexts of different records are not exactly the same. In view of these challenges, a novel approach named Partial-HR (Partial Historical Records-based service evaluation

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Page 1: A context aware service evaluation approach over big data for cloud applications

IEEE PROJECTS DEVELOPMENTSWE OFFER IEEE PROJECTS MCA FINAL YEAR STUDENT PROJECTS, ENGINEERING PROJECTS

AND TRAINING, PHP PROJECTS, JAVA AND J2EE PROJECTS, ASP.NET PROJECTS, NS2 PROJECTS, MATLAB PROJECTS AND IPT TRAINING .

CELL: +91 8870791415                                                  Mail to: [email protected]   

A Context-aware Service Evaluation Approach over Big Data for Cloud Applications

Abstract

Cloud computing has promoted the success of big data applications such as medical data

analyses. With the abundant resources provisioned by cloud platforms, the QoS (quality of

service) of services that process big data could be boosted significantly. However, due to

unstable network or fake advertisement, the QoS published by service providers is not always

trusted. Therefore, it becomes a necessity to evaluate the service quality in a trustable way, based

on the services’ historical QoS records. However, the evaluation efficiency would be low and

cannot meet users’ quick response requirement, if all the records of a service are recruited for

quality evaluation. Moreover, it may lead to ‘Lagging Effect’ or low evaluation accuracy, if all

the records are treated equally, as the invocation contexts of different records are not exactly the

same. In view of these challenges, a novel approach named Partial-HR (PartialHistorical

Records-based service evaluation approach) is put forward in this paper. In Partial-HR, each

historical QoS record is weighted based on its service invocation context. Afterwards, only

partial important records are employed for quality evaluation. Finally, a group of experiments are

deployed to validate the feasibility of our proposal, in terms of evaluation accuracy and

efficiency.

Page 2: A context aware service evaluation approach over big data for cloud applications

IEEE PROJECTS DEVELOPMENTSWE OFFER IEEE PROJECTS MCA FINAL YEAR STUDENT PROJECTS, ENGINEERING PROJECTS

AND TRAINING, PHP PROJECTS, JAVA AND J2EE PROJECTS, ASP.NET PROJECTS, NS2 PROJECTS, MATLAB PROJECTS AND IPT TRAINING .

CELL: +91 8870791415                                                  Mail to: [email protected]   

System Specification

System Requirements:

Hardware Requirements:

• System : Pentium IV 2.4 GHz.

• Hard Disk : 40 GB.

• Floppy Drive : 1.44 Mb.

• Monitor : 15 VGA Colour.

• Mouse : Logitech.

• Ram : 512 Mb.

Software Requirements:

• Operating system : - Windows 7. 32 bit

Page 3: A context aware service evaluation approach over big data for cloud applications

IEEE PROJECTS DEVELOPMENTSWE OFFER IEEE PROJECTS MCA FINAL YEAR STUDENT PROJECTS, ENGINEERING PROJECTS

AND TRAINING, PHP PROJECTS, JAVA AND J2EE PROJECTS, ASP.NET PROJECTS, NS2 PROJECTS, MATLAB PROJECTS AND IPT TRAINING .

CELL: +91 8870791415                                                  Mail to: [email protected]   

• Coding Language : C#.net 4.0

• Data Base : SQL Server 2008