Received: 4 January 2019 Revised: 18 February 2019 Accepted: 7 March 2019 DOI: 10.1002/cpe.5262 SPECIAL ISSUE PAPER Rendering differential performance preference through intelligent network edge in cloud data centers Dian Shen 1 Pengcheng Zhou 2 Yidan Gao 3 Xiaolin Guo 1 Runqun Xiong 1 1 School of Computer Science and Engineering, Southeast University, Nanjing, China 2 Bytedance, Beijing, China 3 College of Letters and Science, University of California, Berkeley, California Correspondence Runqun Xiong, School of Computer Science and Engineering, Southeast University, Nanjing, China. Email: rxiong@seu.edu.cn Funding information National Key R.D Program of China, Grant/Award Number: 2017YFB1003000; National Natural Science Foundation of China, Grant/Award Number: 61872079, 61572129, 61602112, 61502097, 61702096, 61320106007, 61632008 and 61702097; International S.T Cooperation Program of China, Grant/Award Number: 2015DFA10490; Natural Science Foundation of Jiangsu Province, Grant/Award Number: BK20160695 and BK20170689; Fundamental Research Funds for the Central Universities, Jiangsu Provincial Key Laboratory of Network and Information Security, Grant/Award Number: BM2003201; Key Laboratory of Computer Network and Information Integration of Ministry of Education of China, Grant/Award Number: 93K-9; Collaborative Innovation Center of Novel Software Technology and Industrialization and Collaborative Innovation Center of Wireless Communications Technology Summary Sharing the network infrastructure, the performance of emerging distributed applications and services in data centers is directly impacted by the network. As these applications are becoming more and more demanding, it is challenging to satisfy their requirements of low latency, high throughput, and low packet loss rate simultaneously. Prior approaches typically resort to flow control or scheduling mechanisms, prioritizing flows according to their demands. However, none of the methods can solely satisfy the various demands of data center applications. Addressing this challenge, we propose tasch, a preference aware flow scheduling mechanism equipped in the software network edge (ie, end-host networking). This mechanism utilizes multiple separate queues for flows with different preferences, which guarantees low packet delay for latency-sensitive flows and provides bandwidth guarantees for throughput-sensitive flows. A coordinating algorithm is presented to share the network resource among multiple queues with pareto-optimality. tasch is implemented as a thin and plugable kernel module in Linux based hypervisors, which lies between the complicated physical network and tenants VMs. Subsequently, based on the flow traces of real-world applications, extensive experiments were conducted to verify the effectiveness of network management mechanism. KEYWORDS data center, flow scheduling, network edge, virtualization 1 INTRODUCTION Cloud data centers are becoming the hosting platform for a wide spectrum of applications. Ranging from latency-sensitive applications such as Web search to bandwidth-hungry ones such as data-parallel processing, these emerging applications are increasingly demanding in network resources. For instance, latency-sensitive applications like Web search require the latency as low as 5 ms, and 10% more latency can result in 20% revenue loss. 1 Data-parallel processing frameworks like Hadoop are throughput-intensive, with an extensive data transfer of more than 1 Gbps in the shuffle phase. However, in current data centers, applications can experience 5× or more variations, leading to unpredictable performance. Prior approaches typically resort to flow control or network scheduling mechanisms, prioritizing flows according to their preference, eg, latency-sensitive or throughput-sensitive. For instance, Fastpass 2 proposed a centralized ‘‘zero-queue’’ data center network architecture where each sender should ask a centralized arbiter for permission of transmission when sending packets. Thus, the queue build-up and related Concurrency Computat Pract Exper. 2019;e5262. wileyonlinelibrary.com/journal/cpe © 2019 John Wiley & Sons, Ltd. 1 of 14 https://doi.org/10.1002/cpe.5262