Conference Dates: Apr 12, 2021 - Apr 14, 2021. However, Addra improves message latency in this architecture, which is a key performance metric for voice calls. Our evaluation shows that PET outperforms existing systems by up to 2.5, by unlocking previously missed opportunities from partially equivalent transformations. For example, optimistic concurrency control (OCC) is better than two-phase-locking (2PL) under low contention, while the converse is true under high contention. Submitted November 12, 2021 Accepted January 20, 2022. We present Storm, a web framework that allows developers to build MVC applications with compile-time enforcement of centrally specified data-dependent security policies. She is the author of the textbook Interconnections (about network layers 2 and 3) and coauthor of Network Security. . Yet, existing efforts randomly select FL participants, which leads to poor model and system efficiency. Four months after we reported the bugs to Geth developers, one of the bugs was triggered on the mainnet, and caused nodes using a stale version of Geth to hard fork the Ethereum blockchain. Research Impact Score 9.24. . ), Program Co-Chairs: Angela Demke Brown, University of Toronto, and Jay Lorch, Microsoft Research. We implement DeSearch for two existing decentralized services that handle over 80 million records and 240 GBs of data, and show that DeSearch can scale horizontally with the number of workers and can process 128 million search queries per day. Compared to a state-of-the-art fuzzer, Fluffy improves the fuzzing throughput by 510 and the code coverage by 2.7 with various optimizations: in-process fuzzing, fuzzing harnesses for Ethereum clients, and semantic-aware mutation that reduces erroneous test cases. Federated Learning (FL) is an emerging direction in distributed machine learning (ML) that enables in-situ model training and testing on edge data. We propose a new framework for computing the embeddings of large-scale graphs on a single machine. Devices employ adaptive interrupt coalescing heuristics that try to balance between these opposing goals. Second, it innovates on the underlying cryptographic machinery and constructs a new private information retrieval scheme, FastPIR, that reduces the time to process oblivious access requests for mailboxes. We present Nap, a black-box approach that converts concurrent persistent memory (PM) indexes into NUMA-aware counterparts. How can we design systems that will be reliable despite misbehaving participants? We also welcome work that explores the interface to related areas such as computer architecture, networking, programming languages, analytics, and databases. She also invented the spanning tree algorithm, which transformed Ethernet from a technology that supported a few hundred nodes, to something that can support large networks. SC is being increasingly adopted by industry for a variety of applications. For any further information, please contact the PC chairs: pc-chairs-2022@eurosys.org. Tao Luo, Mingen Pan, Pierre Tholoniat, Asaf Cidon, and Roxana Geambasu, Columbia University; Mathias Lcuyer, Microsoft Research. Starting with small invariant formulas and strongest possible invariants avoids large SMT queries, improving SMT solver performance. Writing a correct operating system kernel is notoriously hard. Attaching supplementary material is optional; if your paper says that you have source code or formal proofs, you need not attach them to convince the PC of their existence. This paper presents the design and implementation of CLP, a tool capable of losslessly compressing unstructured text logs while enabling fast searches directly on the compressed data. Jaehyun Hwang and Midhul Vuppalapati, Cornell University; Simon Peter, UT Austin; Rachit Agarwal, Cornell University. We observe that scalability challenges in training GNNs are fundamentally different from that in training classical deep neural networks and distributed graph processing; and that commonly used techniques, such as intelligent partitioning of the graph do not yield desired results. Our approach outperforms existing file systems on a block SSD by a wide margin 6.2 on average for metadata-intensive benchmarks. Using selective profiling, we build DMon, a system that can automatically locate data locality problems in production, identify access patterns that hurt locality, and repair such patterns using targeted optimizations. Calibrated interrupts increase throughput by up to 35%, reduce CPU consumption by as much as 30%, and achieve up to 37% lower latency when interrupts are coalesced. Pollux: Co-adaptive Cluster Scheduling for Goodput-Optimized Deep Learning, Oort: Efficient Federated Learning via Guided Participant Selection, PET: Optimizing Tensor Programs with Partially Equivalent Transformations and Automated Corrections, Modernizing File System through In-Storage Indexing, Nap: A Black-Box Approach to NUMA-Aware Persistent Memory Indexes, Rearchitecting Linux Storage Stack for s Latency and High Throughput, Optimizing Storage Performance with Calibrated Interrupts, ZNS+: Advanced Zoned Namespace Interface for Supporting In-Storage Zone Compaction, DMon: Efficient Detection and Correction of Data Locality Problems Using Selective Profiling, CLP: Efficient and Scalable Search on Compressed Text Logs, Polyjuice: High-Performance Transactions via Learned Concurrency Control, Retrofitting High Availability Mechanism to Tame Hybrid Transaction/Analytical Processing, The nanoPU: A Nanosecond Network Stack for Datacenters, Beyond malloc efficiency to fleet efficiency: a hugepage-aware memory allocator, Scalable Memory Protection in the PENGLAI Enclave, NrOS: Effective Replication and Sharing in an Operating System, Addra: Metadata-private voice communication over fully untrusted infrastructure, Bringing Decentralized Search to Decentralized Services, Finding Consensus Bugs in Ethereum via Multi-transaction Differential Fuzzing, MAGE: Nearly Zero-Cost Virtual Memory for Secure Computation, Zeph: Cryptographic Enforcement of End-to-End Data Privacy, It's Time for Operating Systems to Rediscover Hardware, DistAI: Data-Driven Automated Invariant Learning for Distributed Protocols, GoJournal: a verified, concurrent, crash-safe journaling system, STORM: Refinement Types for Secure Web Applications, Horcrux: Automatic JavaScript Parallelism for Resource-Efficient Web Computation, SANRAZOR: Reducing Redundant Sanitizer Checks in C/C++ Programs, Dorylus: Affordable, Scalable, and Accurate GNN Training with Distributed CPU Servers and Serverless Threads, GNNAdvisor: An Adaptive and Efficient Runtime System for GNN Acceleration on GPUs, Marius: Learning Massive Graph Embeddings on a Single Machine, P3: Distributed Deep Graph Learning at Scale. To evaluate the security guarantees of Storm, we build a formally verified reference implementation using the Labeled IO (LIO) IFC framework. See the USENIX Conference Submissions Policy for details. We implemented the ZNS+ SSD at an SSD emulator and a real SSD. Petuum Awarded OSDI 2021 Best Paper for Goodput-Optimized Deep Learning Research Petuum CASL research and engineering team's Pollux technical paper on adaptive scheduling for optimized. Papers must be in PDF format and must be submitted via the submission form. Responses should be limited to clarifying the submitted work. Consensus bugs are bugs that make Ethereum clients transition to incorrect blockchain states and fail to reach consensus with other clients. Proceedings Cover | We propose a learning-based framework that instead explicitly optimizes concurrency control via offline training to maximize performance. Foreshadow was chosen as an IEEE Micro Top Pick. DeSearch uses trusted hardware to build a network of workers that execute a pipeline of small search engine tasks (crawl, index, aggregate, rank, query). Academic and industrial participants present research and experience papers that cover the full range of theory and practice of computer . If the conference registration fee will pose a hardship for the presenter of the accepted paper, please contact conference@usenix.org. CLP's gains come from using a tuned, domain-specific compression and search algorithm that exploits the significant amount of repetition in text logs. This post is for recording some notes from a few OSDI'21 papers that I got fun. Lifting predicates and crash framing make the specification easy to use for developers, and logically atomic crash specifications allow for modular reasoning in GoJournal, making the proof tractable despite complex concurrency and crash interleavings. Furthermore, to enable automatic runtime optimization, GNNAdvisor incorporates a lightweight analytical model for an effective design parameter search. With her students, she had led research in AI, with a focus on robotics and machine learning, having concretely researched and developed a variety of autonomous robots, including teams of soccer robots, and mobile service robots. Despite having the same end goals as traditional ML, FL executions differ significantly in scale, spanning thousands to millions of participating devices. Leveraging these information, Pollux dynamically (re-)assigns resources to improve cluster-wide goodput, while respecting fairness and continually optimizing each DL job to better utilize those resources. Furthermore, by combining SanRazor with an existing sanitizer reduction tool ASAP, we show synergistic effect by reducing the runtime cost to only 7.0% with a reasonable tradeoff of security. If you submit a paper to either of those venues, you may not also submit it to OSDI 21. In this paper, we present Vegito, a distributed in-memory HTAP system that embraces freshness and performance with the following three techniques: (1) a lightweight gossip-style scheme to apply logs on backups consistently; (2) a block-based design for multi-version columnar backups; (3) a two-phase concurrent updating mechanism for the tree-based index of backups. Manuela M. Veloso is the Head of J.P. Morgan AI Research, which pursues fundamental research in areas of core relevance to financial services, including data mining and cryptography, machine learning, explainability, and human-AI interaction. The symposium emphasizes innovative research as well as quantified or insightful experiences in systems design and implementation. First, GNNAdvisor explores and identifies several performance-relevant features from both the GNN model and the input graph, and use them as a new driving force for GNN acceleration. Hence, kernel developers are constantly refining synchronization within OS kernels to improve scalability at the risk of introducing subtle bugs. We build Polyjuice based on our learning framework and evaluate it against several existing algorithms. Consensus bugs are extremely rare but can be exploited for network split and theft, which cause reliability and security-critical issues in the Ethereum ecosystem. Even the little publishable OS work that is not based on Linux still assumes the same simplistic hardware model (essentially a multiprocessor VAX) that bears little resemblance to modern reality. This paper describes the design, implementation, and evaluation of Addra, the first system for voice communication that hides metadata over fully untrusted infrastructure and scales to tens of thousands of users. Pollux promotes fairness among DL jobs competing for resources based on a more meaningful measure of useful job progress, and reveals a new opportunity for reducing DL cost in cloud environments. Ankit Bhardwaj and Chinmay Kulkarni, University of Utah; Reto Achermann, University of British Columbia; Irina Calciu, VMware Research; Sanidhya Kashyap, EPFL; Ryan Stutsman, University of Utah; Amy Tai and Gerd Zellweger, VMware Research. Academic and industrial participants present research and experience papers that cover the full range of theory . Professor Veloso is the Past President of AAAI (the Association for the Advancement of Artificial Intelligence), and the co-founder, Trustee, and Past President of RoboCup. We describe PrivateKube, an extension to the popular Kubernetes datacenter orchestrator that adds privacy as a new type of resource to be managed alongside other traditional compute resources, such as CPU, GPU, and memory. Hence, CLP enables efficient search and analytics on archived logs, something that was impossible without it. The blockchain community considers this hard fork the greatest challenge since the infamous 2016 DAO hack. Kirk Rodrigues, Yu Luo, and Ding Yuan, University of Toronto and YScope Inc. She also has made contributions in network security, including scalable data expiration, distributed algorithms despite malicious participants, and DDOS prevention techniques. To resolve the problem, we propose a new LFS-aware ZNS interface, called ZNS+, and its implementation, where the host can offload data copy operations to the SSD to accelerate segment compaction. We have implemented a prototype of our design based on Penglai, an open-sourced enclave system for RISC-V. Currently, for large graphs, CPU servers offer the best performance-per-dollar over GPU servers. Evaluation on a four-node machine with Optane DC Persistent Memory shows that Nap can improve the throughput by up to 2.3 and 1.56 under write-intensive and read-intensive workloads, respectively. To adapt to different workloads, prior works mix or switch between a few known algorithms using manual insights or simple heuristics. Mothy's current research centers on Enzian, a powerful hybrid CPU/FPGA machine designed for research into systems software. will work with the steering committee to ensure that the symposium program will accommodate presentations for all accepted papers. We first introduce two new hardware primitives: 1) Guarded Page Table (GPT), which protects page table pages to support page-level secure memory isolation; 2) Mountable Merkle Tree (MMT), which supports scalable integrity protection for secure memory. Distributed Trust: Is Blockchain the answer? Additionally, there is no assurance that data processing and handling comply with the claimed privacy policies. This approach misses possible optimization opportunities as transformations that only preserve equivalence on subsets of the output tensors are excluded. Simultaneous submission of the same work to multiple venues, submission of previously published work, or plagiarism constitutes dishonesty or fraud. Uniquely, Dorylus can take advantage of serverless computing to increase scalability at a low cost. However, existing enclave designs fail to meet the requirements of scalability demanded by new scenarios like serverless computing, mainly due to the limitations in their secure memory protection mechanisms, including static allocation, restricted capacity and high-cost initialization. For conference information, see: . NrOS replicates kernel state on each NUMA node and uses operation logs to maintain strong consistency between replicas. Second, GNNAdvisor implements a novel and highly-efficient 2D workload management tailored for GNN computation to improve GPU utilization and performance under different application settings. Despite their extensive use for debugging and vulnerability discovery, sanitizer checks often induce a high runtime cost. Authors are required to register abstracts by 3:00 p.m. PST on December 3, 2020, and to submit full papers by 3:00 p.m. PST on December 10, 2020. The paper review process is double-blind. When registering your abstract, you must provide information about conflicts with PC members. We compare Marius against two state-of-the-art industrial systems on a diverse array of benchmarks. The chairs will review paper conflicts to ensure the integrity of the reviewing process, adding or removing conflicts if necessary. Typically, monolithic kernels share state across cores and rely on one-off synchronization patterns that are specialized for each kernel structure or subsystem. The device then "calibrates" its interrupts to completions of latency-sensitive requests. Authors must make a good faith effort to anonymize their submissions, and they should not identify themselves or their institutions either explicitly or by implication (e.g., through the references or acknowledgments). A graph neural network (GNN) enables deep learning on structured graph data. A scientific paper consists of a constellation of artifacts that extend beyond the document itself: software, hardware, evaluation data and documentation, raw survey results, mechanized proofs, models, test suites, benchmarks, and so on. And yet, they continue to rely on centralized search engines and indexers to help users access the content they seek and navigate the apps. Sponsored by USENIX in cooperation with ACM SIGOPS. The copyback-aware block allocation considers different copy costs at different copy paths within the SSD. This paper demonstrates that it is possible to achieve s-scale latency using Linux kernel storage stack, even when tens of latency-sensitive applications compete for host resources with throughput-bound applications that perform read/write operations at throughput close to hardware capacity. Indeed, it is a prime target for powerful adversaries such as nation states. In particular, responses must not include new experiments or data, describe additional work completed since submission, or promise additional work to follow. This change is receiving considerable attention in the architecture and security communities, for example, but in contrast, so-called OS researchers are mostly in denial. Camera-ready submission (all accepted papers): 2 April 2021; Main conference program: 27-28 April 2021; All deadline times are . We demonstrate the above using design, implementation and evaluation of blk-switch, a new Linux kernel storage stack architecture. All deadline times are 23:59 hrs UTC. Kyuhwa Han, Sungkyunkwan University and Samsung Electronics; Hyunho Gwak and Dongkun Shin, Sungkyunkwan University; Jooyoung Hwang, Samsung Electronics. blk-switch uses this insight to adapt techniques from the computer networking literature (e.g., multiple egress queues, prioritized processing of individual requests, load balancing, and switch scheduling) to the Linux kernel storage stack. We develop a prototype of Zeph on Apache Kafka to demonstrate that Zeph can perform large-scale privacy transformations with low overhead. To help more profitably utilize sanitizers, we introduce SanRazor, a practical tool aiming to effectively detect and remove redundant sanitizer checks.

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