Research On Monitoring And Managing Quality Of Experience(Qoe)in Cloud Computing

Cloud computing provide services to users on pay per use and service level agreement(SLA)is signed document between the service providers and end users.Violation of SLA is due to peak internal cloud traffic,virtual machine(VM)migration,external network or client device issues,this may cause unsatisfactory situation for users,so they migrate to other service provider for better qualit y of service(QoS).Cloud organization use qualit y of experience(QoE)to get feedback from users about QoS and needs but sometime greedy nature of users provides wrong feedback to get more level of service mentioned in SLA.Monitoring and managing qualit y of experience and provide services according to QoE is cumbersome for cloud organization.Recently,QoE in cloud computing considered in research interest and lot of work has been conducted on the QoE in cloud computing.Several QoE frameworks were designed and developed to provide QoS to end users.Different parameters were used to assess the subjective QoE of end users in cloud computing services and but there is no solution was proposed by any researcher to distinguish the positive and negative QoE.This research investigates limitation of the previous proposed QoE frameworks for cloud and presents QoC(Qualit y of experience framework for Cloud computing)agent based framework for monitoring the qualit y of experience(QoE)of the end user using video streaming services in cloud computing.The design and development of QoC framework include web interface for end users to manage their account,video data and submit the feedback about the services.Previous cloud frameworks used fix policies which did not have the functionalit y to upgrade services when the user did not receive services according to service level agreement(S LA)and also there was lack of functionality of monitoring of external network and client device resources.Limitations of previous cloud frameworks mean that the analysis of user submitted QoE,whether it is positive or negative and cloud services cannot follow according to user preferences.The management platform is used for administration purpose in QoC framework,which provides facility to easily manage the cloud environment and provide services according to SLA via runtime policy change.The objective QoE/QoS section will automatically monitor the quality of service(QoS)data from internal cloud environment to end user 's device for scanning issues occurred in providing QoS to end user.The objective QoE/QoS will compare and analyze the subjective QoE submitted by the user and objective QoS data which is collected by agent based framework for accurate QoE predication and proper management.The proposed QoC framework sends alert to cloud management if the problem is found in cloud environment and it also sends an alert message to the end user if problem arises at the user side.It further has self-management functionality to handle all operations without the intervention of cloud administration.The proposed QoC framework has new features of real time network monitoring,client device monitoring and change policy in runtime environment which to our knowledge is currently not provided by existing cloud QoE frameworks.Secondly cache performance test of the QoC: quality of experience framework for cloud computing was analyzed on the server.QoC framework is based on the server-side design and implementation of the use of hierarchical architecture.Reverse proxy technology is used to build a server cluster,which is composed of front-end access layer to achieve the server for load balancing,improve the performance of the system and the use of built-in distributed cache server.The cluster consists of the cache acceleration layer,which reduces the load of the backend database.The second database server cluster,which is constructed by the database master and slave synchronization technology,forms the data storage layer,which realizes the database read and writes separation and data redundancy.The server-side hierarchical architecture improves the performance and stabilit y of the entire system,and has a high degree of scalabilit y,laying a solid foundation for future expansion of system business logic and increases user volume.The cache replacement algorithm for inconsistent for video file size and then analyzes the specific needs for the multi-terminal t ype of QoC framework,and gives the client and server-side outline design;it describes the implementation details of the client and the server-side and finall y the whole system of detailed functional and performance testing.Thirdl y,we assess in the thesis,QoE of long distance and short distance between the end users and cloud organizations.The impact on the end user's Qualit y of Experience(QoE)was anal yzed,when videos from nearby location and long distance Clouds were accessed.In the first stage of experiment ping response of each Cloud from multiple locations were collected using Broadband and 3G/4G networks.The second stage of experiment based on the perceiving videos from different Clouds and videos having different codec,bitrate and resolution.A questionnaire was provided to users to give their experience about services for each cloud.The results show that QoE level is high when perceiving videos from local Clouds as compared to long distance clouds.End users are accessing cloud services via mobile apps,but enormous amount of temporary/cache data is generated by apps,so internal storage of mobile devices are filled quickly.Mobile device without any space in internal storage has huge impact on the performance when accessing the cloud services,which degrade the QoE of end users for particular cloud app.The thesis presents the results of experiments conducted using two mobile devices HTC and Samsung to anal yze the impact on end user 's QoE during accessing cloud,when internal storage of HTC mobile device is filled and Samsung having 10 GB free space.Finall y,on the basis of experimental results future changes in cloud apps are suggested for service provider to improve end user 's QoE.Finally,we estimate the subjective QoE of image/video services of social cloud computing,which help to analyze the positive and negative feedback of users.Social clouds compress the videos to save storage and stream over slow networks to provide quality of service(QoS).Compression of video decreases the quality compared to original video and parameters are changed during the online play as well as after download.Degradation of video quality due to compression decreases the quality of experience(QoE)level of end users.To assess the QoE of video compression,we conducted subjective(QoE)experiments by uploading,sharing,and playing videos from social clouds.Image posting and sharing on the social media clouds is a common activity of end users.During the uploading of an image,social media cloud automatically compresses the original image to reduce resolution and file size to save storage and provides service to speed up content loading in the web page.Image quality degradation on social media clouds decreases the user satisfaction level.Quality of Experience(QoE)experiment was conducted for assessing the end user's satisfaction for image compression.The results show that Facebook and Twitter compress image less as compared toWeChat and Tumblr and user QoE ratings show that Facebook and Twitter compression level for the image is acceptable.Further,we found that decrease of luminance and chrominance has less impact on image quality compared to resolution scaling which has higher impact on quality.