QaMeC: A QoS-driven IoVs application optimizing deployment scheme in multimedia edge clouds

被引:36
|
作者
Wu, Ziyan [1 ]
Lu, Zhihui [1 ]
Hung, Patrick C. K. [2 ]
Huang, Shih-Chia [3 ]
Tong, Yu [4 ]
Wang, Zhenfang [4 ]
机构
[1] Fudan Univ, Sch Comp Sci, Shanghai, Peoples R China
[2] Univ Ontario Inst Technol, Fac Business & IT, Oshawa, ON, Canada
[3] Natl Taipei Univ Technol, Dept Elect Engn, Taipei, Taiwan
[4] Minist Educ, Engn Res Ctr Cyber Secur Auditing & Monitoring, Shanghai, Peoples R China
基金
中国国家自然科学基金;
关键词
IoVs (Internet of Vehicles); IoT(Internet of Things); Optimizing deployment; Cloud computing; Edge computing; Multi-clouds; QoS; CDN (Content Delivery Network; Content Distribution Network);
D O I
10.1016/j.future.2018.09.032
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Deploying applications to a centralized cloud for service delivery is infeasible because of the excessive latency and bandwidth limitation of the Internet, such as transporting all IoVs data to big data processing service in a centralized cloud. Therefore, multi-clouds, especially multiple edge clouds is a rising trend for cloud service provision. However, heterogeneity of the cloud service, complex deployment requirements, and large problem space of multi-clouds deployment make how to deploy applications in the multi-clouds environment be a difficult and error-prone decision-making process. Due to these difficulties, current SIA-based solution lacks a unified model to represent functional and non-functional requirements of users. In this background, we propose a QoS-driven IoVs application optimizing deployment scheme in multimedia edge clouds (QaMeC). Our scheme builds a unified QoS model to shield off the inconsistency of QoS calculation. Moreover, we use NSGA-II algorithm to solve the multi-clouds application deployment problem. The implementation and experiments show that our QaMeC scheme can provide optimal and efficient service deployment solutions for a variety of applications with different QoS requirements in CDN multimedia edge clouds environment. (C) 2018 Elsevier B.V. All rights reserved.
引用
收藏
页码:17 / 28
页数:12
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