TY - GEN
T1 - Impact of different cloud deployments on real-time video applications for mobile video cloud users
AU - Khan, Kashif A.
AU - Wang, Qi
AU - Luo, Chunbo
AU - Wang, Xinheng
AU - Grecos, Christos
N1 - Publisher Copyright:
© 2015 SPIE-IS&T.
PY - 2015
Y1 - 2015
N2 - The latest trend to access mobile cloud services through wireless network connectivity has amplified globally among both entrepreneurs and home end users. Although existing public cloud service vendors such as Google, Microsoft Azure etc. are providing on-demand cloud services with affordable cost for mobile users, there are still a number of challenges to achieve high-quality mobile cloud based video applications, especially due to the bandwidth-constrained and errorprone mobile network connectivity, which is the communication bottleneck for end-to-end video delivery. In addition, existing accessible clouds networking architectures are different in term of their implementation, services, resources, storage, pricing, support and so on, and these differences have varied impact on the performance of cloud-based real-time video applications. Nevertheless, these challenges and impacts have not been thoroughly investigated in the literature. In our previous work, we have implemented a mobile cloud network model that integrates localized and decentralized cloudlets (mini-clouds) and wireless mesh networks. In this paper, we deploy a real-time framework consisting of various existing Internet cloud networking architectures (Google Cloud, Microsoft Azure and Eucalyptus Cloud) and a cloudlet based on Ubuntu Enterprise Cloud over wireless mesh networking technology for mobile cloud end users. It is noted that the increasing trend to access real-time video streaming over HTTP/HTTPS is gaining popularity among both research and industrial communities to leverage the existing web services and HTTP infrastructure in the Internet. To study the performance under different deployments using different public and private cloud service providers, we employ real-time video streaming over the HTTP/HTTPS standard, and conduct experimental evaluation and in-depth comparative analysis of the impact of different deployments on the quality of service for mobile video cloud users. Empirical results are presented and discussed to quantify and explain the different impacts resulted from various cloud deployments, video application and wireless/mobile network setting, and user mobility. Additionally, this paper analyses the advantages, disadvantages, limitations and optimization techniques in various cloud networking deployments, in particular the cloudlet approach compared with the Internet cloud approach, with recommendations of optimized deployments highlighted. Finally, federated clouds and inter-cloud collaboration challenges and opportunities are discussed in the context of supporting real-time video applications for mobile users.
AB - The latest trend to access mobile cloud services through wireless network connectivity has amplified globally among both entrepreneurs and home end users. Although existing public cloud service vendors such as Google, Microsoft Azure etc. are providing on-demand cloud services with affordable cost for mobile users, there are still a number of challenges to achieve high-quality mobile cloud based video applications, especially due to the bandwidth-constrained and errorprone mobile network connectivity, which is the communication bottleneck for end-to-end video delivery. In addition, existing accessible clouds networking architectures are different in term of their implementation, services, resources, storage, pricing, support and so on, and these differences have varied impact on the performance of cloud-based real-time video applications. Nevertheless, these challenges and impacts have not been thoroughly investigated in the literature. In our previous work, we have implemented a mobile cloud network model that integrates localized and decentralized cloudlets (mini-clouds) and wireless mesh networks. In this paper, we deploy a real-time framework consisting of various existing Internet cloud networking architectures (Google Cloud, Microsoft Azure and Eucalyptus Cloud) and a cloudlet based on Ubuntu Enterprise Cloud over wireless mesh networking technology for mobile cloud end users. It is noted that the increasing trend to access real-time video streaming over HTTP/HTTPS is gaining popularity among both research and industrial communities to leverage the existing web services and HTTP infrastructure in the Internet. To study the performance under different deployments using different public and private cloud service providers, we employ real-time video streaming over the HTTP/HTTPS standard, and conduct experimental evaluation and in-depth comparative analysis of the impact of different deployments on the quality of service for mobile video cloud users. Empirical results are presented and discussed to quantify and explain the different impacts resulted from various cloud deployments, video application and wireless/mobile network setting, and user mobility. Additionally, this paper analyses the advantages, disadvantages, limitations and optimization techniques in various cloud networking deployments, in particular the cloudlet approach compared with the Internet cloud approach, with recommendations of optimized deployments highlighted. Finally, federated clouds and inter-cloud collaboration challenges and opportunities are discussed in the context of supporting real-time video applications for mobile users.
KW - Cloudlet
KW - Eucalyptus
KW - Google cloud
KW - Microsoft Azure
KW - real-time cloud application
KW - wireless mesh network
UR - http://www.scopus.com/inward/record.url?scp=84925639521&partnerID=8YFLogxK
U2 - 10.1117/12.2080436
DO - 10.1117/12.2080436
M3 - Conference Proceeding
AN - SCOPUS:84925639521
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Proceedings of SPIE-IS and T Electronic Imaging - Real-Time Image and Video Processing 2015
A2 - Kehtarnavaz, Nasser
A2 - Carlsohn, Matthias F.
PB - SPIE
T2 - Real-Time Image and Video Processing 2015
Y2 - 10 February 2015
ER -