Cloud data center has become one of the most important IT infrastructures. Building high-performance data centers at low cost requires collective effort of the entire global community. As an attempt to initiate a platform that brings together the most important and forward-looking work in the area for intriguing and productive discussions, the Sixth Workshop on Hot Topics on Data Centers (HotDC 2022) will be held in Beijing, China on October 14, 2022.
HotDC 2022 consists of by-invitation-only presentations from top academic and industrial groups around the world. The topics include a wide range of data-center related issues, including the state-of-the-art technologies for server architecture, storage system, data-center network, resource management, etc. Besides, HotDC 2022 includes a student poster session to present recent research works from the data-center teams in Institute of Computing Technology, Chinese Academy of Sciences. The HotDC workshop expects to provide a forum for the cutting edge in data-center research, where researchers/engineers can exchange ideas and engage in discussions with their colleagues around the world. Welcome to HotDC 2022!
Please join the Workshop with VooV Meeting
Time: GMT+8 9:00 ~ 17:30, Oct. 14, 2022 (Friday)
Meeting ID: 951 570 807
Online streamiing: https://live.bilibili.com/22391464
请使用 腾讯会议 加入会议
时间：2022年10月14日（周五）, 9:00 ~ 17:30
会议号：951 570 807
Date: Oct. 14, 2022 (Friday)
|09:00 - 09:10||Opening remark|
Yungang Bao, Institute of Computing Technology, Chinese Academy of
|Keynote Session I, Chair: Chenxi Wang|
|09:10 - 09:50||Resource Efficient Observability at Scale: Two Real-world Deployments in Data Center and Smartphones|
Ding Yuan, University of Toronto
|09:50 - 10:30||Software-Defined Cloud Systems|
Xin Jin, Peking University
|10:30 - 10:40||Break|
|Keynote Session II, Chair: Kan Shi|
|10:40 - 11:20||High-performance Memory Management Design for Modern Architectures|
Rachata Ausavarungnirun, King Mongkut’s University of Technology North Bangkok
|11:20 - 12:00||云计算数据中心当下问题与基础软件关键技术|
|12:00 - 14:00||Lunch|
|Keynote Session III, Chair: Mi Zhang|
|14:00 - 14:40||Invertible Sketches for Network Measurement at Scale. |
Patrick P. C. Lee, The Chinese University of Hong Kong
|14:40 - 15:20||Co-Designing Distributed Systems with Programmable Network Hardware |
Jialin Li, National University of Singapore
|15:20 - 15:30||Break|
|Keynote Session IV, Chair: Sa Wang|
|15:30 - 16:10||数据中心高可靠纠删码技术|
沈志荣 - 数据中心高可靠纠删码研究.pdf
|16:10 - 16:50||重新思考Web 场景下的事务抽象与SQL优化问题 |
|16:50 - 17:30||高性能高可靠键值存储系统|
|Student Poster Session, Chair: Dejun Jiang|
17:30 - 19:30, Lecture hall on the fourth floor, ICT
INTERNEURON: A Middleware with Multi-Network Communication Reliability for Infrastructure Vehicle Cooperative Autonomous Driving, Tianze Wu
Towards Developing High Performance RISC-V Processors Using Agile Methodology,
NapFS: A High-Performance NUMA-Aware PM File System, Wenqing Jia
FastStore: A High-Performance RDMA-enabled Distributed Key-Value Store with Persistent Memory, ZiWei Xiong
LightShaper：a high-precision and universal traffic shaping tool, Haokun Wang
MCCBench: A C10M Benchmark Oriented to Interactive Network Services, Hui Song
Exploiting Hybrid Index for RDMA-based Key-Value Stores, Shukai Han
GraFF: A Multi-FPGA System with Memory Semantic Fabric for Scalable Graph Processing, Xu Zhang
Topic: Resource Efficient Observability at Scale: Two Real-world Deployments in Data Center and Smartphones
Speaker: Ding Yuan, University of Toronto
Bio: Ding Yuan is an associate professor at the department of Electrical and Computer Engineering at University of Toronto. He is a Canada Research Chair in Systems Software and a recipient of the McCharles Early Research Distinction. His research has been licensed/used by many companies including Google, Uber, Netflix, NetApp, Huawei, Microsoft, and open-source projects including Linux, Hadoop, and HBase. He is the founder of YScope.
Abstract: Postmortem trouble-shooting and monitoring rely on collecting diagnostic information at runtime from production systems. However, doing so in a scalable and resource efficient manner is challenging. For examples, Uber’s growth in scale exceeded the capability of existing log management tools, forcing them to start dropping Petabytes of logs; Android smartphone environments present a stringent overhead requirements for any production tracing tools. In this talk, I will discuss two case studies. The first is a log compression and search tool called CLP. CLP has been deployed at Uber, enabling them to retain all of their logs and achieve 169x cost savings in log ingestion, retention, and analytics. The second is a system named Hubble, which provides continuous method tracing in production Android systems to aid debugging performance problems. Hubble is shipped on all supported and upcoming Android devices manufactured by Huawei.
Topic: Software-Defined Cloud Systems
Speaker: Xin Jin, Peking University
Bio: Xin Jin is an Associate Professor in School of Computer Science at Peking University. He received his BS in computer science and BA in economics from Peking University in 2011, and his MA and PhD in computer science from Princeton University in 2013 and 2016. His research is in computer networks and computer systems, with a focus on software-defined datacenters, programmable networks, and cloud computing. He received USENIX FAST Best Paper Award (2019), USENIX NSDI Best Paper Award (2018), Amazon AWS Machine Learning Research Award (2019), Google Faculty Research Award (2019), and Facebook Communications & Networking Research Award (2018).
Abstract: With the end of Moore's law and Dennard's scaling, it is challenging for cloud systems to keep up with the rapidly increasing user demands and meet the Service Level Objectives (SLOs). In this talk, I will present a new paradigm that exploits the capability of domain-specific hardware and co-design network, storage and compute to build next-generation cloud systems and meet these requirements. I will focus on two types of domain-specific hardware, which are programmable switching ASICs for IO-intensive workloads and GPUs for compute-intensive workloads. I will present several systems we have built recently with programmable switching ASICs and GPUs. These systems exemplify a new generation of cloud systems enabled by domain-specific hardware.
Topic: High-performance Memory Management Design for Modern Architectures
Speaker: Rachata Ausavarungnirun, King Mongkut’s University of Technology North Bangkok
Bio: Rachata Ausavarungnirun is an assistant professor at the Sirindhorn International Thai-German Graduate School of Engineering (TGGS) at King Mongkut’s University of Technology North Bangkok. His research spans multiple topics across computer architecture and system software with emphasis on GPU architecture, heterogeneous CPU-GPU architecture, virtual memory, containerization, management of GPUs in the cloud, memory subsystems, memory management, processing-in-memory, non-volatile memory, network-on-chip, and accelerator designs. Rachata received his Ph.D. in Electrical and Computer Engineering from Carnegie Mellon University in 2017.
Abstract: The growth in the new system and architecture designs have enabled significant performance improvement across various types of modern applications. However, these applications' increasing resource demand also creates new challenges as conventional methods to manage virtual memory fail to deliver good performance without non-trivial workarounds across diverse types of architectures. This talk identifies performance bottlenecks created by virtual memory and its metadata management system in modern architectures. Specifically, we provide an in-depth analysis of the bottlenecks and limitations of Linux's huge page on modern applications across multiple architectures. To minimize such performance bottlenecks, we introduce a combination of new techniques with modest hardware changes to manage virtual memory and its metadata. Our proposal allows the system to utilize different policies based on the applications' own demand to eliminate performance pathologies and improves system performance across various architectures.
Speaker: 刘峥, 阿里达摩院操作系统实验室
Topic: Invertible Sketches for Network Measurement at Scale.
Speaker: Patrick P. C. Lee, The Chinese University of Hong Kong
Bio: Patrick P. C. Lee is now a Professor of the Department of Computer Science and Engineering at the Chinese University of Hong Kong. His research interests are in various applied/systems topics on improving the dependability of large-scale storage systems, distributed systems, and networks. He heads the Applied Distributed Systems Lab and is working very closely with a group of graduate students on different projects in networks and systems. He is now serving on the editorial boards of IEEE/ACM Transactions on Networking and ACM Transactions on Storage. For details, please refer to his personal homepage: http://www.cse.cuhk.edu.hk/~pclee.
Abstract: Network measurement in massive network traffic is challenging due to the stringent requirements of fast packet processing and limited resource availability. Given the fast-speed and ever-increasing network traffic, particularly in modern large-scale data center networks, maintaining per-flow state in network measurement tasks inevitably has tremendous resource demands. In this talk, I will introduce invertible sketches for various network measurement tasks that can be carried out in a real-time and scalable manner. Invertible sketches are summary data structures that significantly mitigate memory footprints with bounded errors; by "invertible", we mean that the measurement results can be readily recovered from only the sketch data structure itself. We show how to design invertible sketches for heavy flow detection and superspreader detection.
Topic: Co-Designing Distributed Systems with Programmable Network Hardware
Speaker: Jialin Li, National University of Singapore
Bio: Jialin Li is an Assistant Professor in the School of Computing at the National University of Singapore. He finished his PhD from the University of Washington in 2019, advised by Dan R. K. Ports. As part of his dissertation work, he pioneered a new approach to building high performance distributed systems, by co-designing with data center networks. He is the recipient of best paper awards at OSDI and NSDI. His current research interests include distributed systems and programmable hardware co-design, data plane operating systems, and decentralized system infrastructure.
Abstract:With the end of Dennard scaling and Moore's Law, performance improvement of general purpose processors has been stagnant for the past decade. This is in contrast to the continuous growth in network speed in data centers and telecommunication networks, and the increasing demand of modern applications. Not surprisingly, software processing on CPUs has become the performance bottleneck of many large scale distributed systems deployed in data centers. In this talk, I will introduce a new approach to designing distributed systems in data centers that tackle the aforementioned challenge -- by co-designing distributed systems with the data center network. Specifically, my work has taken advantage of new-generation programmable switches in data centers to build novel network-level primitives with near-zero processing overhead. We then leverage these primitives to enable more efficient protocol and system designs. I will describe several systems we built that demonstrate the benefit of this approach. The first two, Network-Ordered Paxos and Eris, virtually eliminate the coordination overhead in state machine replication and fault-tolerant distributed transactions, by relying on network sequencing primitives to consistently order user requests. The third system, Hydra, overcomes the limitations of a centralized network sequencer in previous solutions, by deploying a distributed set of sequencers without sacrificing the ordering guarantees. The last system, Pegasus, substantially improves the load balancing of a distributed storage system -- up to a 9x throughput improvement over existing solutions -- by implementing an in-network coherence directory in the switching ASICs.
Speaker: 沈志荣, 厦门大学
Bio: 沈志荣，厦门大学信息学院副教授，入选厦门大学“南强青年拔尖人才计划”，主要研究方向为大数据/云存储系统、数据中心和新型非易失存储介质的数据存储可靠性，在IEEE TC、TPDS、TDSC和USENIX ATC、INFOCOM、ICDCS、IPDPS、DSN、SRDS等CCF A/B类期刊会议发表论文40多篇，获得SRDS'20最佳论文奖（通讯作者）和SRDS'15最佳论文提名奖（第一作者），主持包括国家重点研发计划课题、国家自然科学基金面上项目和青年基金、CCF-腾讯犀牛鸟基金和CCF-华为胡杨林基金等多项课题。
Abstract: 数据中心软硬件故障成为常态，数据存储的可靠性成为数据中心的一个重要问题。纠删码是一种具有低存储开销的容错编码，如今被广泛应用于Hadoop、Windows Azure Storage、Ceph等开源或商用存储系统之中。然而纠删码具有修复代价高和更新开销大等问题，从而极易激发大量的网络和存储I/O。在该问题上，我们展开了一些初步探索，分别基于数据中心网络架构和纠删码理论，设计了一系列降低数据中心纠删码修复和校验更新开销等方法，从而能够显著加速数据中心的数据修复和校验更新操作。
Topic: 重新思考Web 场景下的事务抽象与SQL优化问题
Speaker: 王肇国, 上海交通大学
Bio: 上海交通大学长聘教轨副教授，博士生导师。于2008年从南京大学本科毕业，11年和14年分别从复旦大学获得硕士和博士学位。2014至2018年在纽约大学从事博士后研究工作，后担任研究助理教授。主要从事并行与分布式数据库系统方面的研究，主攻系统一致性、存储结构和SQL优化方面问题。相关成果发表在SIGMOD，OSDI， PPoPP，EuroSys, Usenix ATC, NSDI，PODC等国际重要会议上。曾获SIGMOD 2022最佳论文优胜奖（Honorable Mention），APSys 2017最佳论文奖，ACM ChinaSys新星奖，华为奥林帕斯先锋奖，以及两次华为火花奖。他是CCF数据库专委执行委员、ACM ChinaSys青执委委员、开源组委委员、FCS预备青年编委、ACM SIGOPS指定编辑、ChinaSys学术开源创新平台的主要发起人，曾受邀担任 ACM ChinaSys 2021大会联合主席、ACM ChinaSys 2022程序委员会联合主席，ACM APSys 2018宣传主席， IEEE ICDCS 2020、IEEE Cluster 2021、TPDS 专刊 2020/2021等国际会议和专刊的程序委员会成员。
Abstract:如何让数据库系统更好的服务当代Web应用？这一直是学术界与工业界共同关心的问题。针对该问题，我们从事务抽象和SQL优化两方面切入，对Web场景下传统数据库技术的合理性进行了大胆探究。在事务抽象方面，我们分析了GitHub上多个大型Web应用，系统性的揭示了Web应用中的即席事务（Ad Hoc Transaction）现象以及其对应用和系统的影响；在SQL优化方面，我们借鉴超优化思想，根据Web场景需求，自动生成SQL重写规则（Rewrite Rule），有效提高了对Web应用中SQL优化效率。
Speaker: 李永坤, 中国科学技术大学
Bio: 李永坤，博士，中国科学技术大学副教授，中科院青促会会员，仲英青年学者。主要研究方向是存储系统，包括键值存储、内存系统、虚拟化与图计算等。目前共发表论文60余篇。主持国家自然科学基金青年与面上各一项、科技部重点研发计划子课题一项，以及PingCAP、华为等多项企业合作项目。部分成果在PingCAP落地应用，获评PingCAP优秀合作奖。担任ASPLOS2022(ERC)、APSys2020等会议程序委员会委员，以及Journal of Computer Science and Technology青年编委。
Yungang Bao, Institute of Computing Technology, Chinese Academy of Sciences
Sa Wang, Institute of Computing Technology, Chinese Academy of Sciences
Chenxi Wang, Institute of Computing Technology, Chinese Academy of Sciences
Kan Shi, Institute of Computing Technology, Chinese Academy of Sciences
Mi Zhang, Institute of Computing Technology, Chinese Academy of Sciences
Dejun Jiang, Institute of Computing Technology, Chinese Academy of Sciences
Wenya Hu, Institute of Computing Technology, Chinese Academy of Sciences
Zhiwei Lai, Institute of Computing Technology, Chinese Academy of Sciences
Ziwei Huang, Institute of Computing Technology, Chinese Academy of Sciences
Ziwei Huang (firstname.lastname@example.org)