Related papers: Tail Contagion: Sub-microsecond Time Protection in…

Network latencies have become increasingly important for the performance of web servers and cloud computing platforms. Identifying network-related tail latencies and reasoning about their potential causes is especially important to gauge…

This paper introduces the concept of size-aware sharding to improve tail latencies for in-memory key-value stores, and describes its implementation in the Minos key-value store. Tail latencies are crucial in distributed applications with…

In this paper we propose Virtuoso, a purely software-based multi-path RDMA solution for data center networks (DCNs) to effectively utilize the rich multi-path topology for load balancing and reliability. As a "middleware" library operating…

Excellent tail performance is crucial for modern machine learning tasks, such as algorithmic fairness, class imbalance, and risk-sensitive decision making, as it ensures the effective handling of challenging samples within a dataset. Tail…

The unprecedented growth in data demand from emerging applications has turned virtual memory (VM) into a major performance bottleneck. Researchers explore new hardware/OS co-designs to optimize VM across diverse applications and systems. To…

Microarchitectural timing channels enable unwanted information flow across security boundaries, violating fundamental security assumptions. They leverage timing variations of several state-holding microarchitectural components and have been…

Cache side-channel attacks lead to severe security threats to the settings that a CPU is shared across users, e.g., in the cloud. The existing attacks rely on sensing the micro-architectural state changes made by victims, and this…

Packet buffers in datacenter switches are shared across all the switch ports in order to improve the overall throughput. The trend of shrinking buffer sizes in datacenter switches makes buffer sharing extremely challenging and a critical…

3D occupancy prediction has attracted much attention in the field of autonomous driving due to its powerful geometric perception and object recognition capabilities. However, existing methods have not explored the most essential…

Achieving control stability is one of the key design challenges of scalable Wireless Networked Control Systems (WNCS) under limited communication and computing resources. This paper explores the use of an alternative control concept defined…

Cache coherence scalability is a big challenge in shared memory systems. Traditional protocols do not scale due to the storage and traffic overhead of cache invalidation. Tardis, a recently proposed coherence protocol, removes cache…

Efficient data access in High-Performance Computing (HPC) systems is essential to the performance of intensive computing tasks. Traditional optimizations of the I/O stack aim to improve peak performance but are often workload specific and…

Multi-tenant cloud computing provides great benefits in terms of resource sharing, elastic pricing, and scalability, however, it also changes the security landscape and introduces the need for strong isolation between the tenants, also…

The widespread diffusion of compute-intensive edge-AI workloads and the stringent demands of modern autonomous systems require advanced heterogeneous embedded architectures. Such architectures must support high-performance and reliable…

Distributed optimization has become the default training paradigm in modern machine learning due to the growing scale of models and datasets. To mitigate communication overhead, local updates are often applied before global aggregation,…

This paper introduces a new way to incorporate verifiable secret sharing (VSS) schemes into Byzantine Fault Tolerance (BFT) protocols. This technique extends the threshold guarantee of classical Byzantine Fault Tolerant algorithms to…

Different from the traditional software vulnerability, the microarchitecture side channel has three characteristics: extensive influence, potent threat, and tough defense. The main reason for the micro-architecture side channel is resource…

We present a differentially private mechanism to display statistics (e.g., the moving average) of a stream of real valued observations where the bound on each observation is either too conservative or unknown in advance. This is…

GPU underutilization is a significant concern in many production deep learning clusters, leading to prolonged job queues and increased operational expenses. A promising solution to this inefficiency is GPU sharing, which improves resource…

Privacy-preserving aggregation is a cornerstone for AI systems that learn from distributed data without exposing individual records, especially in federated learning and telemetry. Existing two-server protocols (e.g., Prio and successors)…