Multi-provider cloud computing network infrastructure optimization

被引:7
作者
Banditwattanawong, Thepparit [1 ,3 ]
Masdisornchote, Masawee [1 ,3 ]
Uthayopas, Putchong [2 ]
机构
[1] Sripatum Univ, Sch Informat Technol, Bangkok, Thailand
[2] Kasetsart Univ, Dept Comp Engn, Bangkok, Thailand
[3] Sripatum Univ, 2410-2 Phaholyothin Rd, Bangkok 10900, Thailand
来源
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE | 2016年 / 55卷
基金
美国国家科学基金会;
关键词
Cache replacement; Multi-provider cloud; Federated cloud; Hybrid cloud; Artificial neural network; Cost-saving ratio; PERFORMANCE EVALUATION; REPLACEMENT; IMPLEMENTATION; ALGORITHMS; ACCESS;
D O I
10.1016/j.future.2015.09.002
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Cloud-adopting enterprises have been increasingly employing multiple cloud providers concurrently, for example, to consume unique services and to mitigate data lock-in risk. As a consequence, the enterprises must be able to address contrasting quality-of-service degrees offered by the different providers. This paper presents an intelligent cloud cache eviction approach, namely i-Cloud, as the core component of a client-side cloud cache. i-Cloud is capable of reducing public cloud data-out expenses, improving cloud network scalability and lowering cloud service access latencies specifically in multi-provider cloud environments. Trace-driven simulations have shown that i-Cloud outperformed well-known approaches in all performance metrics. In addition, i-Cloud is not only able to achieve optimal performances in all metrics simultaneously but also delivered relatively stable performances across all performance metrics. The results have also indicated that taking the nonuniformity of data-out charge rates into cache eviction decisions improved caching performances in all metrics. (C) 2015 Elsevier B.V. All rights reserved.
引用
收藏
页码:116 / 128
页数:13
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