IEEE INFOCOM 2023
TCP and Congestion Control
i-NVMe: Isolated NVMe over TCP for a Containerized Environment
Seongho Lee, Ikjun Yeom and Younghoon Kim (Sungkyunkwan University, Korea (South))
Speaker Lee Seongho (Sungkyunkwan University, South Korea)
He is currently working toward the Ph.D. degree in computer science at Sungkyunkwan University, South Korea. His research interests include optimizing containerized environments and the CPU scheduling.
Congestion Control Safety via Comparative Statics
Pratiksha Thaker (Carnegie Mellon University, USA); Tatsunori Hashimoto and Matei Zaharia (Stanford University, USA)
Speaker Pratiksha Thaker (Carnegie Mellon University)
Pratiksha Thaker is a postdoctoral researcher at Carnegie Mellon University. She is interested in applying tools from learning theory and game theory to practical systems problems.
Gemini: Divide-and-Conquer for Practical Learning-Based Internet Congestion Control
Wenzheng Yang and Yan Liu (Tencent, China); Chen Tian (Nanjing University, China); Junchen Jiang (University of Chicago, USA); Lingfeng Guo (The Chinese University of Hong Kong, Hong Kong)
Speaker Wenzheng Yang (Nanjing University and Tencent, China)
Marten: A Built-in Security DRL-Based Congestion Control Framework by Polishing the Expert
Zhiyuan Pan and Jianer Zhou (SUSTech, China); XinYi Qiu ( & Peng Cheng Laboratory, China); Weichao Li (Peng Cheng Laboratory, China); Heng Pan (Institute of Computing Technology, Chinese Academy of Sciences, China); Wei Zhang (The National Computer Network Emergency Response Technical Team Coordination Center of China, China)
Speaker Zhiyuan Pan (Sothern University of Science and Technology)
Zhiyuan Pan is studying for a master's degree at Southern University of Science and Technology. His main research topics are network congestion control algorithms and deep reinforcement learning algorithms.
Cloud/Edge Computing 2
TanGo: A Cost Optimization Framework for Tenant Task Placement in Geo-distributed Clouds
Luyao Luo, Gongming Zhao and Hongli Xu (University of Science and Technology of China, China); Zhuolong Yu (Johns Hopkins University, USA); Liguang Xie (Futurewei Technologies, USA)
To bridge the gap, we design a cost optimization framework for tenant task placement in geo-distributed clouds, called TanGo. However, it is non-trivial to achieve an optimization framework while meeting all the tenant requirements. To this end, we first formulate the electricity cost minimization for task placement problem as a constrained mixed-integer non-linear programming problem. We then propose a near-optimal algorithm with a tight approximation ratio (1-1/e) using an effective submodular-based method. Results of in-depth simulations based on real-world datasets show the effectiveness of our algorithm as well as the overall 10\%-30\% reduction in electricity expenses compared to commonly-adopted alternatives.
Speaker Zhenguo Ma (University of Science and Technology of China)
Zhenguo Ma received the B.S. degree in software engineering from the Shandong University, China, in 2018. He is currently pursuing his Ph.D. degree in the School of Computer Science and Technology, University of Science and Technology of China. His research interests include cloud computing, edge computing and federated learning.
An Approximation for Job Scheduling on Cloud with Synchronization and Slowdown Constraints
Dejun Kong and Zhongrui Zhang (Shanghai Jiao Tong University, China); Yangguang Shi (Shandong University, China); Xiaofeng Gao (Shanghai Jiao Tong University, China)
Speaker Dejun Kong (Shanghai Jiao Tong University)
Dejun Kong is a Ph. D. candidate of Shanghai Jiao Tong University. His research area includes scheduling algorithm, distributed computing and data analytics.
Time and Cost-Efficient Cloud Data Transmission based on Serverless Computing Compression
Rong Gu and Xiaofei Chen (Nanjing University, China); Haipeng Dai (Nanjing University & State Key Laboratory for Novel Software Technology, China); Shulin Wang (Nanjing University, China); Zhaokang Wang and Yaofeng Tu (Nanjing University of Aeronautics and Astronautics, China); Yihua Huang (Nanjing University, China); Guihai Chen (Shanghai Jiao Tong University, China)
Speaker Rong Gu (Nanjing University)
Rong Gu an assistant professor in the Department of Computer Science and Technology at Nanjing University. My research interests include Cloud and Big Data computing systems, efficient Cache/Index systems, Edge systems, etc. I have published over 40 papers in USENIX ATC, ICDE, WWW, INFOCOM, VLDBJ, IEEE TPDS, TNET, TMC, IPDPS, ICPP, IWQoS, DASFAA, and published a monograph. I received the IEEE TCSC Award for Excellence in Scalable Computing (Early Career), IEEE HPCC 2022 Best Paper Award (first author), the first prize of Jiangsu Science and Technology Prize in 2018, Tecent Cloud Valuable Professional (TVP) Award in 2021, the first place of the 30th SortBenchmark Competition CloudSort Track (Record Holder). My research results have been adopted by a number of well-known open source software such as Apache Spark, Alluxio, and leading IT/domain companies, including Alibaba, Baidu, Tencent, ByteDance, Huatai Securities, Intel, Sinopec, Weibo and so on. I am the community chair of the Fluid open source project (CNCF Sandbox project), a founding PMC member & maintainer of Alluxio (formly Tachyon) open source project. I am also the co-program chair of 15th IEEE iThings,the co-chair of 23rd ChinaSys, TPC member of SOSP’21/OSDI’22/USENIX ATC’22 Artifacts、AAAI’20、IEEE IPDPS’22.
Enabling Age-Aware Big Data Analytics in Serverless Edge Clouds
Zichuan Xu, Yuexin Fu and Qiufen Xia (Dalian University of Technology, China); Hao Li (China Coal Research Institute, China)
Speaker Yuexin Fu
Yuexin Fu is a Master candidate at Dalian University of Technology. His research interests include edge computing and serverless computing.
CLP: A Community based Label Propagation Framework for Multiple Source Detection
Chong Zhang and Luoyi Fu (Shanghai Jiao Tong University, China); Fei Long (Chinaso, China); Xinbing Wang (Shanghai Jiaotong University, China); Chenghu Zhou (Shanghai Jiao Tong University, China)
Despite recent considerable effort, most of them are built on a preset propagation model, which limits their application range. Some attempts aim to break this limitation via a label propagation scheme where the nodes surrounded by large proportions of infected nodes are highlighted. Nonetheless, the detection accuracy may suffer since the node labels are simply integers with all infected or uninfected nodes sharing the same initialization setting respectively, which fall short of sufficiently distinguishing the structural properties of them. To this end, we propose a community based label propagation (CLP) framework that locates multiple sources through exploiting the community structures formed by infected subgraph of different sources. Besides, CLP tries to enhance the detection accuracy by incorporating node prominence and exoneration effects. As such, CLP is applicable in more propagation models. Experiments on both synthetic and real-world networks further validate the superiority of CLP to the state-of-the-art.
Speaker Chong Zhang
Chong Zhang received his B.E. degree in Telecommunications Engineering from Xidian University, China, in 2018. He is currently pursuing the Ph.D. degree in Department of Electronic Engineering in Shanghai Jiao Tong University, Shanghai, China. His research of interests are in the area of social networks and data mining.
GinApp: An Inductive Graph Learning based Framework for Mobile Application Usage Prediction
Zhihao Shen, Xi Zhao and Jianhua Zou (Xi'an Jiaotong University, China)
Speaker Zhihao Shen (Xi'an Jiaotong University)
Zhihao Shen received his B.E. degree in automation engineering from School of Electronic and Information, Xi'an Jiaotong University, Xi'an, China, in 2016, where he is currently pursuing the Ph.D. degree with the Systems Engineering Institute. His research interests include mobile computing, big data analytics, and deep learning.
Cost-Effective Live Expansion of Three-Stage Switching Networks without Blocking or Connection Rearrangement
Takeru Inoue and Toru Mano (NTT Network Innovation Labs., Japan); Takeaki Uno (National Institute of Informatics, Japan)
Speaker Takeru Inoue (NTT Labs, Japan)
Takeru Inoue is a Distinguished Researcher at Nippon Telegraph and Telephone Corporation (NTT) Laboratories, Japan. He received the B.E. and M.E. degrees in engineering science and the Ph.D. degree in information science from Kyoto University, Japan, in 1998, 2000, and 2006, respectively. In 2000, he joined NTT Laboratories. From 2011 to 2013, he was an ERATO Researcher with the Japan Science and Technology Agency, where his research focused on algorithms and data structures. Currently, his research interests widely cover the reliable design of communication networks. Inoue was the recipient of several prestigious awards, including the Best Paper Award of the Asia-Pacific Conference on Communications in 2005, the Best Paper Award of the IEEE International Conference on Communications in 2016, the Best Paper Award of IEEE Global Communications Conference in 2017, the Best Paper Award of IEEE Reliability Society Japan Joint Chapter in 2020, the IEEE Asia/Pacific Board Outstanding Paper Award in 2020, and the IEICE Paper of the Year in 2021. He serves as an Associate Editor of the IEEE Transactions on Network and Service Management.
ASR: Efficient and Adaptive Stochastic Resonance for Weak Signal Detection
Xingyu Chen, Jia Liu, Xu Zhang and Lijun Chen (Nanjing University, China)
Speaker Xingyu Chen (Nanjing University)
Xingyu Chen is currently a Ph.D. student with the Department of Computer Science and Technology at Nanjing University of China. His research interests focus on indoor localization and RFID system. He is a student member of the IEEE.