Accepted papers
We have 40 papers accepted tentatively for SoCC 2023. Paper title, content, and even the acceptance decision may change during the shepherding process. The detailed program is coming up soon.
Research
Plexus: Optimizing Join Approximation for Geo-Distributed Data Analytics
- Joel Wolfrath, Abhishek Chandra (University of Minnesota)
Carbon Containers: A System-level Facility for Managing Application-level Carbon Emissions
- John Thiede (University of Massachusetts - Amherst); Noman Bashir (University of Massachusetts Amherst); David Irwin (University of Massachusetts, Amherst), Prashant Shenoy
Golgi: Performance-Aware, Resource-Efficient Function Scheduling for Serverless Computing
- Suyi Li, Wei Wang (Hong Kong University of Science and Technology); Jun Yang, Guangzhen Chen, Daohe Lu (WeBank)
Lifting the Fog of Uncertainties: Dynamic Resource Orchestration for the Containerized Cloud
- Yuqiu Zhang, Tongkun Zhang, Gengrui Zhang, Hans-Arno Jacobsen (University of Toronto)
Oblivious Paxos: Privacy-Preserving Consensus Over Secret-Shares
- Fadhil I. Kurnia, Arun Venkataramani (University of Massachusetts Amherst)
Maximizing VMs' IO Performance on Overcommitted CPUs with Fairness
- Tong Xing, Cong Xiong (The University of Edinburgh); Chuan Ye, Qi Wei, Javier Picorel (Huawei); Antonio Barbalace (The University of Edinburgh)
Not All Resources are Visible: Exploiting Fragmented Shadow Resources in Shared-State Scheduler Architecture
- Xinkai Wang, Hao He, Yuancheng Li, Chao Li, Xiaofeng Hou, Jing Wang, Quan Chen, Jingwen Leng, Minyi Guo (Shanghai Jiao Tong University); Leibo Wang (Huawei Technologies Co., Ltd.)
Polis: Efficient Federated Learning via Scalable Client Clustering
- Jiachen Liu, Fan Lai (University of Michigan); Yinwei Dai (Princeton University); Aditya Akella (UT Austin and Google); Harsha V. Madhyastha (University of Southern California); Mosharaf Chowdhury (University of Michigan)
Dissecting Overheads of Service Mesh Sidecars
- Xiangfeng Zhu (University of Washington); Guozhen She (Duke university); Bowen Xue (University of Washington); Yu Zhang, Yongsu Zhang, Xuan Kelvin Zou, XiongChun Duan, Peng He (ByteDance Inc.); Arvind Krishnamurthy (University of Washington); Matthew Lentz (Duke University and VMware Research); Danyang Zhuo (Duke University); Ratul Mahajan (University of Washington, Amazon)
OneAdapt: Fast Adaptation for Deep Learning Applications via Backpropagation
- Kuntai Du, Yuhan Liu, Yitian Hao (University of Chicago); Qizheng Zhang (Stanford University); Haodong Wang (The University of Chicago); Yuyang Huang (University of Chicago); Ganesh Ananthanarayanan (Microsoft); Junchen Jiang (University of Chicago)
Parrotfish: Parametric Regression for Optimizing Serverless Functions
- Arshia Moghimi (University of British Columbia); Joe Hattori (The University of Tokyo); Alexander Li (University of British Columbia); Mehdi BEN CHIKHA (National Institute of Applied Sciences and Technology (INSAT)); Mohammad Shahrad (University of British Columbia)
A Comparison of End-to-End Decision Forest Inference Pipelines
- Hong Guan, Saif Masood, Mahidhar Reddy Dwarampudi, Venkatesh Gunda (Arizona State University); Hong Min (IBM T. J. Watson Research Center); Lei Yu (Rensselaer Polytechnic Institut); Soham Nag, Jia Zou (Arizona State University)
Enabling Multi-tenancy on SSDs with Accurate IO Interference Modeling
- Lokesh N. Jaliminche (University of California, Santa Cruz); Chandranil (Nil) Chakraborttii (Trinity College, Hartford, USA); Changho Choi (Samsung Semiconductor, Inc, USA); Heiner Litz (University of California, Santa Cruz)
KVSEV: A Secure In-Memory Key-Value Store with Secure Encrypted Virtualization
- Junseung You, Kyeongryong Lee (Seoul National University); Hyungon Moon (UNIST (Ulsan National Institute of Science and Technology)); Yeongpil Cho (Hanyang University); Yunheung Paek (Seoul National University)
Building GPU TEEs using CPU Secure Enclaves with GEVisor
- Xiaolong Wu, Dave (Jing) Tian (Purdue University); Chung Hwan Kim (University of Texas at Dallas)
Maximizing the Utilization of GPUs Used by Cloud Gaming through Adaptive Co-location with Combo
- Binghao Chen, Han Zhao, Weihao Cui, Yifu He, Shulai Zhang, Quan Chen, Zijun Li, Minyi Guo (Shanghai Jiao Tong University)
AsyFunc: A High-Performance and Resource-Efficient Serverless Inference System via Asymmetric Functions
- Qiangyu Pei, Yongjie Yuan, Haichuan Hu (School of Computer Science and Technology, Huazhong University of Science and Technology); Qiong Chen (Huawei); Fangming Liu (Peng Cheng Laboratory & Huazhong University of Science and Technology)
Fledge: Simplifying Topology Extension in Federated Learning
- Harshit Daga (Georgia Institute of Technology); Jaemin Shin (KAIST); Dhruv Garg (Georgia Institute of Technology); Ada Gavrilovska (Georgia Tech); Myungjin Lee, Ramana Rao Kompella (Cisco Systems)
TMC: Near-Optimal Resource Allocation for Tiered-Memory Systems
- Yuanjiang Ni (Alibaba Group); Pankaj Mehra (Elephance Memory, Inc.); Ethan Miller (Pure Storage / University of California, Santa Cruz); Heiner Litz (University of California, Santa Cruz)
User Disengagement-Oriented Target Enforcement for Multi-Tenant Database Systems
- Ning Li (Dept. of Computer Science and Engineering, University of Texas at Arlington, USA); Hong Jiang (UT Arlington); Hao Che (Department of Computer Science and Engineering, The University of Texas at Arlington); Zhijun Wang (University of Texas at Arlington); Minh Q. Nguyen (Faculty of IT, Ho Chi Minh City University of Transport, Vietnam); Stoddard Rosenkrantz (The University of Texas at Arlington)
Anticipatory Resource Allocation for ML Training Clusters
- Tapan Chugh (University of Washington); Srikanth Kandula (Microsoft); Arvind Krishnamurthy (University of Washington); Ratul Mahajan (University of Washington, Amazon); Ishai Menache (Microsoft)
𝜇ConAdapter: Reinforcement Learning-based Fast Concurrency Adaptation for Microservices in the Cloud
- Jianshu Liu (Louisiana State University); Shungeng Zhang (Augusta University); Qingyang Wang (Louisiana State University)
FileScale: Fast and Elastic Metadata Management for Distributed File Systems
- Gang Liao (UNIVERSITY OF MARYLAND); Daniel Abadi (University of Maryland, College Park)
HELIOS: Hardware-assisted High-performance Security Extension for Cloud Networking
- Myoungsung You (KAIST); Jaehyun Nam (DANKOOK University); Minjae Seo, Seungwon Shin (KAIST)
LatenSeer: Causal Modeling of End-to-End Latency Distribution by Harnessing Distributed Tracing
- Yazhuo Zhang (Emory University); Rebecca Isaacs (Amazon Web Services); Yao Yue (Pelican Foundation); Juncheng Yang (Carnegie Mellon University); Lei Zhang (Princeton University); Ymir Vigfusson (Emory University)
Cryonics: Trustworthy Function-as-a-Service using Snapshot-based Enclaves
- Seong-Joong Kim (The Affiliated Institute of ETRI, KAIST); Myoungsung You (KAIST); Byung Joon Kim (The Affiliated Institute of ETRI); Seungwon Shin (KAIST)
CAMEO: A Causal Transfer Learning Approach for Performance Optimization of Configurable Computer Systems
- Md Shahriar Iqbal (University of South Carolina); Ziyuan Zhong (Columbia University); Iftakhar Ahmad (University of South Carolina); Baishakhi Ray (Columbia University); Pooyan Jamshidi (University of South Carolina)
Yama: Providing Performance Isolation for Black-Box Offloads
- Tao Ji, Divyanshu Saxena (The University of Texas at Austin); Brent Stephens (University of Utah); Aditya Akella (UT Austin and Google)
Chitu: Accelerating Serverless Workflows with Asynchronous State Replication Pipeline
- Zhengyu Lei (Institute of Computing Technology, Chinese Academy of Sciences; University of Chinese Academy of Sciences); Xiao Shi (Institute of Computing Technology, Chinese Academy of Sciences; Nanjing Institute of InforSuperBahn); Cunchi Lv, Xiaobing Yu, Xiaofang Zhao (Institute of Computing Technology, Chinese Academy of Sciences; University of Chinese Academy of Sciences)
Industry
Towards GPU Memory Efficiency for Distributed Training at Scale
- Runxiang Cheng (University of Illinois Urbana-Champaign); Chris Cai, Selman Yilmaz, Rahul Mitra, Malay Bag, Mrinmoy Ghosh (Meta Platforms, Inc.); Tianyin Xu (University of Illinois Urbana-Champaign)
Gödel: Unified Large-Scale Resource Management and Scheduling at ByteDance
- Wu Xiang (Bytedance); Yakun Li (ByteDance); Yuquan Ren (Bytedance); Fan Jiang, Chaohui Xin, Varun Gupta, Chao Xiang (ByteDance); Xinyi Song (Bytedance); Meng Liu, Bing Li, Kaiyang Shao, Chen Xu, Wei Shao (ByteDance); Yuqi Fu (George Mason University); Wilson Wang, Cong Xu (Bytedance); Wei Xu (ByteDance); Caixue Lin, Rui Shi (Bytedance); Yuming Liang (ByteDance)
Disaggregating ML Input Data Processing at Scale
- Andrew Audibert, Yang Chen (Google); Dan-Ovidiu Graur, Ana Klimovic (ETH Zurich); Jiri Simsa, Chandramohan A. Thekkath (Google)
How Does It Function? Characterizing Long-term Trends in Production Serverless Workloads
- Artjom Joosen, Ahmed Hassan, Luke Darlow, Martin Asenov, Wang Jianfeng, Rajkarn Singh (Huawei); Adam Barker (Huawei / University of St Andrews)
Vision
Function as a Function
- Tom Kuchler, Michael Giardino, Timothy Roscoe, Ana Klimovic (ETH Zurich)
Metaverse as a Service: Megascale Social 3D on the Cloud
- Andreas Haeberlen, Linh Thi Xuan Phan (Roblox / University of Pennsylvania); Morgan McGuire (Roblox)
The Gap Between Serverless Research and Real-world Systems
- Qingyuan Liu, Dong Du, Yubin Xia (Shanghai Jiao Tong University); Ping Zhang (Huawei Cloud); Haibo Chen (Shanghai Jiao Tong University)
Is Machine Learning Necessary for Cloud Resource Usage Forecasting?
- Georgia Christofidi, Konstantinos Papaioannou, Thaleia Dimitra Doudali (IMDEA Software Institute)
Sustainable supercomputing for AI: Experiences from GPU Power Capping at HPC Scale
- Dan Zhao (MIT); Siddharth Samsi, Joseph McDonald (MIT Lincoln Laboratory); Baolin Li (Northeastern University); Michael Jones, David Bestor (MIT Lincoln Laboratory); Devesh Tiwari (Northeastern University); Vijay Gadepally (MIT Lincoln Laboratory)
Work-in Progress
Multivariate Anomaly Detection with Domain Clustering
- Frederic Boesel, Livio Schläpfer (IBM Research Zurich Research Lab); H. Pozidis (IBM Research GmbH, Switzerland); Mitch Gusat (IBM Research Zurich Research Lab)