摘要
Current applications,consisting of multiple replicas,are packaged into lightweight containers with their execution dependencies.Considering the dominant impact of distribution efficiency of gigantic images on container startup(e.g.,distributed deep learning application),the image“warm-up”technique which prefetches images of these replicas to destination nodes in the cluster is proposed.However,the current image“warm-up”technique solely focuses on identical image distribution,which fails to take effect when distributing different images to destination nodes.To address this problem,this paper proposes Hound,a simple but efficient cluster image distribution system based on Docker.To support diverse image distribution requests of cluster nodes,Hound additionally adopts node-level parallelism(i.e.,downloading images to destination nodes in parallel)to further improve the efficiency of image distribution.The experimental results demonstrate Hound outperforms Docker,kubernetes container runtime interface(CRI-O),and Docker-compose in terms of image distribution performance when cluster nodes request different images.Moreover,the high scalability of Hound is evaluated in the scenario of ten nodes.
基金
supported by the National Natural Science Foundation of China(61872423)
Industry Prospective Primary Research&Development Plan of Jiangsu Province(BE2017111)
the Scientific Research Foundation of the Higher Education Institutions of Jiangsu Province(19KJA180006)
the Postgraduate Research&Practice Innovation Program of Jiangsu Province(KYCX20_0764)。
作者简介
LIU Zijie was born in 1996.He received his B.S.degree from Nanjing Institute of Technology,Nanjing,China,in 2014.He is currently pursuing his Ph.D.degree in information network with Nanjing University of Posts and Telecommunications,Nanjing,China.His current research interests include cloud computing and distributed systems.E-mail:2019070274@njupt.edu.cn;LI Junjiang was born in 1996.He received his B.S.degree from Tongda College of Nanjing University of Posts and Telecommunications,Yangzhou,China,in 2014.He is currently pursuing his M.S.degree in logistics engineering with Nanjing University of Posts and Telecommunications,Nanjing,China.His current research interests include cloud computing,container orchestration systems,and distributed systems.E-mail:admin@run-linux.com;CHEN Can was born in 1993.He received his B.S.degree from Nanjing University of Posts and Telecommunications,Nanjing,China,in 2015.He is currently pursuing his Ph.D.degree in signal and information processing in the College of Telecommunications and Information Engineering,Nanjing University of Posts and Telecommunications,Nanjing,China.His research interests include image and video coding,image and video processing,machine learning,and compressive sensing.E-mail:chencan@njupt.edu.cn;Corresponding author:ZHANG Dengyin was born in 1964.He received his B.S.,M.S.and Ph.D.degrees from Nanjing University of Posts and Telecommunications,Nanjing,China,in 1986,1989 and 2004 respectively.He was in Digital Media Lab at Umea University in Sweden as a visiting scholar from 2007 to 2008.He is currently a professor with the School of Internet of Things,Nanjing University of Posts and Telecommunications,Nanjing,China.His research interests include signal and information processing,networking technique,and information security.E-mail:zhangdy@njupt.edu.cn。
