Privacy Budget Scheduling
Explores privacy budget scheduling in machine learning, introducing PrivateKube for managing differential privacy as a resource. Presents DPF algorithm for fair allocation of non-replenishable privacy budgets in ML workloads.
Pollux - Co-adaptive Cluster Scheduling for Goodput-Optimized Deep Learning
Innovative deep learning cluster scheduler optimizing resource allocation and job performance through adaptive co-optimization, resulting in reduced completion times and improved fairness among competing jobs.
Finding Consensus Bugs in Ethereum via Multi-transaction Differential Fuzzing
Explore multi-transaction differential fuzzing for detecting Ethereum consensus bugs, improving security and reliability in blockchain networks.
Distributed Deep Graph Learning at Scale
Explores a novel system for scaling Graph Neural Network training to large real-world graphs, introducing pipelined push-pull parallelism for faster and more efficient distributed processing.
Dorylus - Affordable, Scalable, and Accurate GNN Training with Distributed CPU Servers and Serverless Threads
Distributed system for training Graph Neural Networks using serverless computing, offering affordable scalability for billion-edge graphs with improved performance-per-dollar compared to GPU servers.
GoJournal - A Verified, Concurrent, Crash-Safe Journaling System
Explore GoJournal, a verified concurrent journaling system for storage applications. Learn about its implementation, performance, and the challenges overcome in its development and verification process.
SANRAZOR - Reducing Redundant Sanitizer Checks in C-C++ Programs
Optimizing sanitizer performance in C/C++ programs by identifying and removing redundant checks, reducing runtime overhead while maintaining bug detection capabilities.
Zeph - Cryptographic Enforcement of End-to-End Data Privacy
Cryptographic system for enforcing user-defined privacy policies in real-time data analytics, ensuring compliance and protection against breaches while maintaining scalability and low latency.
Cobra - Making Transactional Key-Value Stores Verifiably Serializable
Explore techniques for verifying serializability in transactional key-value stores, focusing on Cobra's innovative approach to handling real-world workloads and improving verification efficiency.
Fast RDMA-based Ordered Key-Value Store Using Remote Learned Cache
Explores XSTORE, an RDMA-based key-value store using machine learning models as cache for tree-based indexes, improving performance and scalability while efficiently handling dynamic workloads and reducing client-side memory usage.
Semeru - A Memory-Disaggregated Managed Runtime
Innovative distributed JVM for memory-disaggregated environments, featuring a universal Java heap, distributed garbage collection, and efficient swap system to enhance managed cloud application performance.
Thunderbolt - Throughput-Optimized, Quality-of-Service-Aware Power Capping at Scale
Explore power capping in data centers with Thunderbolt, a system optimizing throughput and quality of service. Learn about hardware-agnostic solutions for safe power oversubscription and task-level QoS differentiation.
Twine - A Unified Cluster Management System for Shared Infrastructure
Explore Facebook's Twine, a unified cluster management system for shared infrastructure, scaling to manage one million machines across data centers with innovative approaches to workload customization and resource allocation.
AntMan - Dynamic Scaling on GPU Cluster for Deep Learning
Dynamic scaling system for efficient GPU utilization in deep learning, improving memory and computation usage without compromising fairness or performance in large-scale clusters.
Blockene - A High-throughput Blockchain Over Mobile Devices
Innovative blockchain system enabling smartphone participation in validation and consensus, offering high throughput and scalability while minimizing resource usage through a novel split-trust design.
GraphX - Graph Processing in a Distributed Dataflow Framework
Explore GraphX, a graph processing framework built on Apache Spark, offering performance comparable to specialized systems while enabling broader computational capabilities and fault tolerance.
Theseus - An Experiment in Operating System Structure and State Management
Redesigning OS modularity using Rust, Theseus reduces inter-component state dependencies, enabling live evolution and fault recovery for core components beyond existing systems.
RedLeaf - Isolation and Communication in a Safe Operating System
Explore RedLeaf, a Rust-based OS leveraging language safety for fine-grained isolation. Learn about innovative approaches to fault isolation, driver recovery, and performance in operating system design.
Microsecond Consensus for Microsecond Applications
Innovative consensus protocol leveraging RDMA for microsecond-scale replication and rapid fail-over in latency-sensitive applications, significantly outperforming existing systems.
KungFu - Making Training in Distributed Machine Learning Adaptive
KungFu: adaptive distributed machine learning library for TensorFlow. Enables real-time parameter tuning using high-level policies, efficient monitoring, and asynchronous communication for improved training performance.
Caladan - Mitigating Interference at Microsecond Timescales
Explore a new CPU scheduler that achieves better quality of service through fast core allocation, outperforming resource partitioning systems in managing microsecond-scale interference between tasks.