Youngseok Yang, Seoul National University; Taesoo Kim, Georgia Institute of Technology; Byung-Gon Chun, Seoul National University and FriendliAI. The full program will be available in May 2021. CLP's gains come from using a tuned, domain-specific compression and search algorithm that exploits the significant amount of repetition in text logs. See the USENIX Conference Submissions Policy for details. PC members are not required to read supplementary material when reviewing the paper, so each paper should stand alone without it. For general conference information, see https://www . Moreover, to handle dynamic workloads, Nap adopts a fast NAL switch mechanism. 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. MAGE outperforms the OS virtual memory system by up to an order of magnitude, and in many cases, runs SC computations that do not fit in memory at nearly the same speed as if the underlying machines had unbounded physical memory to fit the entire computation. This formulation of memory management, which we call memory programming, is a generalization of paging that allows MAGE to provide a highly efficient virtual memory abstraction for SC. Although the number of submissions is lower than the past, it's likely only due to the late announcement; being in my first OSDI PC, I think the quality of the submitted and accepted papers remains as high as ever. She also has made contributions in network security, including scalable data expiration, distributed algorithms despite malicious participants, and DDOS prevention techniques. We also verified a simple NFS server using GoJournals specs, which confirms that they are helpful for application verification: a significant part of the proof doesnt have to consider concurrency and crashes. Here, we focus on hugepage coverage. We also propose two file system techniques for ZNS+-aware LFS. 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. As a result, data characteristics and device capabilities vary widely across clients. DMon speeds up PostgreSQL, one of the most popular database systems, by 6.64% on average (up to 17.48%). OSDI - Guide Proceedings OSDI brings together professionals from academic and industrial backgrounds in a premier forum for discussing the design, implementation, and implications of systems software. He joined Intel Research at Berkeley in April 2002 as a principal architect of PlanetLab, an open, shared platform for developing and deploying planetary-scale services. 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. A significant obstacle to using SC for practical applications is the memory overhead of the underlying cryptography. The conference papers and full proceedings are available to registered attendees now and will be available to everyone beginning Wednesday, July 14, 2021. Sat, Aug 7, 2021 3 min read researches review. Compared to existing baselines, DPF allows training more models under the same global privacy guarantee. Submitted November 12, 2021 Accepted January 20, 2022. 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. Differential privacy (DP) enables model training with a guaranteed bound on this leakage. You must not improperly identify a PC member as a conflict if none of these three circumstances applies, even if for some other reason you want to avoid them reviewing your paper. Editor in charge: Daniel Petrolia . The chairs may reject abstracts or papers on the basis of egregious missing or extraneous conflicts. Secure hardware enclaves have been widely used for protecting security-critical applications in the cloud. Erhu Feng, Xu Lu, Dong Du, Bicheng Yang, and Xueqiang Jiang, Institute of Parallel and Distributed Systems, Shanghai Jiao Tong University; Engineering Research Center for Domain-specific Operating Systems, Ministry of Education, China; Yubin Xia, Binyu Zang, and Haibo Chen, Institute of Parallel and Distributed Systems, Shanghai Jiao Tong University; Shanghai AI Laboratory; Engineering Research Center for Domain-specific Operating Systems, Ministry of Education, China. All submissions will be treated as confidential prior to publication on the USENIX OSDI 21 website; rejected submissions will be permanently treated as confidential. With an aim to improve time-to-accuracy performance in model training, Oort prioritizes the use of those clients who have both data that offers the greatest utility in improving model accuracy and the capability to run training quickly. Instead of choosing among a small number of known algorithms, our approach searches in a "policy space" of fine-grained actions, resulting in novel algorithms that can outperform existing algorithms by specializing to a given workload. In this paper, we propose a software-hardware co-design to support dynamic, fine-grained, large-scale secure memory as well as fast-initialization. Timothy Roscoe is a Full Professor in the Systems Group of the Computer Science Department at ETH Zurich, where he works on operating systems, networks, and distributed systems, and is currently head of department. How can we design systems that will be reliable despite misbehaving participants? 2019 - Present. Artifact Evaluation - Systems Research Artifacts Consensus bugs are bugs that make Ethereum clients transition to incorrect blockchain states and fail to reach consensus with other clients. Further, Vegito can recover from cascading machine failures by using the columnar backup in less than 60 ms. Distributed systems are notoriously hard to implement correctly due to non-determinism. By monitoring the status of each job during training, Pollux models how their goodput (a novel metric we introduce that combines system throughput with statistical efficiency) would change by adding or removing resources. While compiler-based techniques have been proposed to improve data locality, they depend on heuristics, which can sometimes hurt performance. Fan Lai, Xiangfeng Zhu, Harsha V. Madhyastha, and Mosharaf Chowdhury, University of Michigan. We also welcome work that explores the interface to related areas such as computer architecture, networking, programming languages, analytics, and databases. We introduce a hybrid cryptographic protocol for privacy-adhering transformations of encrypted data. 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. Kirk Rodrigues, Yu Luo, and Ding Yuan, University of Toronto and YScope Inc. Used Zotero to organize papers about the stress and diffusion between anode and electrolyte and made a summary . With the help of thousands of Lambda threads, Dorylus scales GNN training to billion-edge graphs. Poor data locality hurts an application's performance. Cores can safely and concurrently read from their local kernel replica, eliminating remote NUMA accesses. To help more profitably utilize sanitizers, we introduce SanRazor, a practical tool aiming to effectively detect and remove redundant sanitizer checks. We implement and evaluate a suite of applications, including MICA, Raft and Set Algebra for document retrieval; and we demonstrate that the nanoPU can be used as a high performance, programmable alternative for one-sided RDMA operations. This is unfortunate because good OS design has always been driven by the underlying hardware, and right now that hardware is almost unrecognizable from ten years ago, let alone from the 1960s when Unix was written. OSDI 2021 papers summary. As increasingly more sensitive data is being collected to gain valuable insights, the need to natively integrate privacy controls in data analytics frameworks is growing in importance. 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. . Existing algorithms are designed to work well for certain workloads. It then feeds those invariants and the desired safety properties to an SMT solver to check if the conjunction of the invariants and the safety properties is inductive. OSDI '22 - HotCRP.com For conference information, . Grand Rapids, Michigan, United States . 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. The abstractions we design for the privacy resource mirror those defined by Kubernetes for traditional resources, but there are also major differences. Swapnil Gandhi and Anand Padmanabha Iyer, Microsoft Research. 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. Secure Computation (SC) is a family of cryptographic primitives for computing on encrypted data in single-party and multi-party settings. Proceedings Cover | Pollux is implemented and publicly available as part of an open-source project at https://github.com/petuum/adaptdl. The 15th USENIX Symposium on Operating Systems Design and Implementation (OSDI '21) will take place as a virtual event on July 1416, 2021. Welcome to the SOSP 2021 Website. OSDI '21 Technical Sessions All the times listed below are in Pacific Daylight Time (PDT). Today, privacy controls are enforced by data curators with full access to data in the clear. NSDI 2021 - Research.com Third, GNNAdvisor capitalizes on the GPU memory hierarchy for acceleration by gracefully coordinating the execution of GNNs according to the characteristics of the GPU memory structure and GNN workloads. The novel aspect of the nanoPU is the design of a fast path between the network and applications---bypassing the cache and memory hierarchy, and placing arriving messages directly into the CPU register file. Dorylus is up to 3.8 faster and 10.7 cheaper compared to existing sampling-based systems. PLDI 2019 - PLDI Research Papers - PLDI 2019 - SIGPLAN We have implemented a prototype of our design based on Penglai, an open-sourced enclave system for RISC-V. We present DPF (Dominant Private Block Fairness) a variant of the popular Dominant Resource Fairness (DRF) algorithmthat is geared toward the non-replenishable privacy resource but enjoys similar theoretical properties as DRF. Authors must limit their responses to (a) correcting factual errors in the reviews or (b) directly addressing questions posed by reviewers. This is especially true for DPF over Rnyi DP, a highly composable form of DP. Of the 26 submitted artifacts: 26 artifacts received the Artifacts Available badge (100%). Widely used log-search tools like Elasticsearch and Splunk Enterprise index the logs to provide fast search performance, yet the size of the index is within the same order of magnitude as the raw log size. Computation separation makes it possible to construct a deep, bounded-asynchronous pipeline where graph and tensor parallel tasks can fully overlap, effectively hiding the network latency incurred by Lambdas. Notification of conditional accept/reject for revisions: 3 March 2022. We discuss the design and implementation of TEMERAIRE including strategies for hugepage-aware memory layouts to maximize hugepage coverage and to minimize fragmentation overheads. In some cases, the quality of these artifacts is as important as that of the document itself. For instance, FAST 21 and NSDI 21 have author-notification dates after the OSDI 21 abstract-registration deadline. 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. Papers not meeting these criteria will be rejected without review, and no deadline extensions will be granted for reformatting. We develop a prototype of Zeph on Apache Kafka to demonstrate that Zeph can perform large-scale privacy transformations with low overhead. Prepublication versions of the accepted papers from the summer submission deadline are available below. These limitations require state-of-the-art systems to distribute training across multiple machines. This paper addresses a key missing piece in the current ecosystem of decentralized services and blockchain apps: the lack of decentralized, verifiable, and private search. To this end, we propose GNNAdvisor, an adaptive and efficient runtime system to accelerate various GNN workloads on GPU platforms. Instead, we propose addressing the root cause of the heuristics problem by allowing software to explicitly specify to the device if submitted requests are latency-sensitive. We present DistAI, a data-driven automated system for learning inductive invariants for distributed protocols. We propose PET, the first DNN framework that optimizes tensor programs with partially equivalent transformations and automated corrections. Submissions may include as many additional pages as needed for references but not for appendices. While verifying GoJournal, we found one serious concurrency bug, even though GoJournal has many unit tests. Currently, for large graphs, CPU servers offer the best performance-per-dollar over GPU servers. In addition, CLP outperforms Elasticsearch and Splunk Enterprise's log ingestion performance by over 13x, and we show CLP scales to petabytes of logs. Acm Ccs 2022 - Sigsac People often assume that blockchain has Byzantine robustness, so adding it to any system will make that system super robust against any calamity. Han Meng - Research Assistant - Michigan State University | LinkedIn OSDI takes a broad view of the systems area and solicits contributions from many fields of systems practice, including, but not limited to, operating systems, file and storage systems, distributed systems, cloud computing, mobile systems, secure and reliable systems, systems aspects of big data, embedded systems, virtualization, networking as it relates to operating systems, and management and troubleshooting of complex systems. If in doubt about whether your submission to OSDI 2021 and your upcoming submission to SOSP are the same paper or not, please contact the PC chairs by email. (Visa applications can take at least 30 working days to process.) Kyuhwa Han, Sungkyunkwan University and Samsung Electronics; Hyunho Gwak and Dongkun Shin, Sungkyunkwan University; Jooyoung Hwang, Samsung Electronics. Graph Neural Networks (GNNs) have gained significant attention in the recent past, and become one of the fastest growing subareas in deep learning. We present the nanoPU, a new NIC-CPU co-design to accelerate an increasingly pervasive class of datacenter applications: those that utilize many small Remote Procedure Calls (RPCs) with very short (s-scale) processing times. NrOS is primarily constructed as a simple, sequential kernel with no concurrency, making it easier to develop and reason about its correctness. Metadata from voice calls, such as the knowledge of who is communicating with whom, contains rich information about peoples lives. Concretely, Dorylus is 1.22 faster and 4.83 cheaper than GPU servers for massive sparse graphs. 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. If you submit a paper to either of those venues, you may not also submit it to OSDI 21. Finding the inductive invariant of the distributed protocol is a critical step in verifying the correctness of distributed systems, but takes a long time to do even for simple protocols.