Fast media caching for geo-distributed data centers

doi:10.1016/j.comcom.2018.02.005

CORC > 计算技术研究所 > 中国科学院计算技术研究所 > 中国科学院计算技术研究所期刊论文 > 英文

	Fast media caching for geo-distributed data centers
	Zhang, Wei 1; Wen, Yonggang 1; Liu, Fang 1; Chen, Yiqiang 3; Fan, Rui 2
刊名	COMPUTER COMMUNICATIONS
	2018-05-01
卷号	120 页码:46-57
关键词	CDN Caching algorithm Cloud computing Geo-distributed data centers
ISSN号	0140-3664
DOI	10.1016/j.comcom.2018.02.005
英文摘要	Recent years have witnessed a phenomenal increase in video traffic. Virtual content delivery networks (vCDNs) coordinate video content delivery through the use of computing and storage resources from the cloud and distributes content to edge nodes near consumers to reduce network traffic and improve service experience. An important objective of vCDNs is operation cost minimization. Since cloud data centers are geo-distributed, content transfer costs vary significantly with different data centers, i.e., the cost is high for retrieval from distant data centers and lower for nearby retrievals. Many popular caching algorithms in use today, such as LRU, do not consider cost when making caching decisions, and as a result, suffer from high data transfer costs and increased network congestion. On the other hand, cost-aware caching algorithms such as LANDLORD [1] are computationally inefficient, with time complexity scaling linearly to the amount of content in the vCDN. Such algorithms are unable to keep pace with the exponential growth in video content over time. In this paper, we propose FMC (fast media caching), a cost-aware and highly efficient caching algorithm for vCDN delivery over geo-distributed data centers. The load cost of each content item is determined by both the item's size and distance from the data center it is loaded from. We first prove that FMC is k/k-h+1 competitive under the resource augmentation paradigm, where FMC and the optimal offline adversary have k and h amount of cache, resp., and k >= h. Also, we show our algorithm is straightforward and efficient, requiring only O(log m) time per cache access, where m is the number of data centers and is a small constant in practice. We conduct experimental studies on MC using both synthetic and YouTube traces. Our results show that INC has on average 50% and up to 66.7% lower cost than tau. Besides, we show PMC is much faster than IANDLORD, and the speedup scales linearly with cache size.
资助项目	Science and Technology Planning Project of Guangdong Province, China[2015B010105001]
WOS研究方向	Computer Science ; Engineering ; Telecommunications
语种	英语
出版者	ELSEVIER SCIENCE BV
WOS记录号	WOS:000429513200005
内容类型	期刊论文
源URL	[http://119.78.100.204/handle/2XEOYT63/5717]
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Zhang, Wei
作者单位	1.Nanyang Technol Univ, Sch Comp Sci & Engn, 50 Nanyang Ave, Singapore 639798, Singapore 2.Shanghai Tech Univ, Sch Informat Sci & Technol, Shanghai 200031, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Wei,Wen, Yonggang,Liu, Fang,et al. Fast media caching for geo-distributed data centers[J]. COMPUTER COMMUNICATIONS,2018,120:46-57.
APA	Zhang, Wei,Wen, Yonggang,Liu, Fang,Chen, Yiqiang,&Fan, Rui.(2018).Fast media caching for geo-distributed data centers.COMPUTER COMMUNICATIONS,120,46-57.
MLA	Zhang, Wei,et al."Fast media caching for geo-distributed data centers".COMPUTER COMMUNICATIONS 120(2018):46-57.