相关论文: Noisy Neighbor: Exploiting RDMA for Resource Exhau…

Coordinating concurrent access to a shared resource using mutual exclusion is a fundamental problem in computation. In this paper, we present a novel approach to mutual exclusion designed specifically for distributed systems leveraging a…

Remote Direct Memory Access (RDMA) has been haunted by the need of pinning down memory regions. Pinning limits the memory utilization because it impedes on-demand paging and swapping. It also increases the initialization latency of large…

Modern systems mitigate Rowhammer using victim refresh, which refreshes the two neighbours of an aggressor row when it encounters a specified number of activations. Unfortunately, complex attack patterns like Half-Double break…

In cloud computing, network Denial of Service (DoS) attacks are well studied and defenses have been implemented, but severe DoS attacks on a victim's working memory by a single hostile VM are not well understood. Memory DoS attacks are…

3D-stacked High Bandwidth Memory (HBM) architectures provide high-performance memory interactions to address the well-known performance challenge, namely the memory wall. However, these architectures are susceptible to thermal…

Memory spatial errors, i.e., buffer overflow vulnerabilities, have been a well-known issue in computer security for a long time and remain one of the root causes of exploitable vulnerabilities. Most of the existing mitigation tools adopt a…

Memory resources in data centers generally suffer from low utilization and lack of dynamics. Memory disaggregation solves these problems by decoupling CPU and memory, which currently includes approaches based on RDMA or interconnection…

We identify and study a new security loophole in continuous-variable quantum key distribution (CV-QKD) implementations, related to the imperfect linearity of the homodyne detector. By exploiting this loophole, we propose an active…

To address the issue of powerful row hammer (RH) attacks, our study involved an extensive analysis of the prevalent attack patterns in the field. We discovered a strong correlation between the timing and density of the active-to-active…

Despite its increasing popularity, most of RDMA's benefits such as ultra-low latency can be achieved only when running an application in isolation. Using microbenchmarks and real open-source RDMA applications, we identify a series of…

Disaggregated systems have a novel architecture motivated by the requirements of resource intensive applications such as social networking, search, and in-memory databases. The total amount of resources such as memory and CPU cores is very…

Many modern wireless networks integrate carrier sense mul-tiple access/collision avoidance (CSMA/CA) with exponential backoff as medium access control (MAC) technique. In order to decrease the MAC overhead and the collision probability, we…

Recent advances in 3D fabrication have allowed handling the memory bottlenecks for modern data-intensive applications by bringing the computation closer to the memory, enabling Near Memory Processing (NMP). Memory Centric Networks (MCN) are…

Energy-efficient computation is an inevitable trend for mobile edge computing (MEC) networks. Resource allocation strategies for maximizing the computation efficiency are critically important. In this paper, computation efficiency…

Deep neural networks (DNNs) have been widely used in many fields such as images processing, speech recognition; however, they are vulnerable to adversarial examples, and this is a security issue worthy of attention. Because the training…

While the robustness of rate-splitting multiple access (RSMA) to imperfect channel state information (CSI) is well-documented, its susceptibility to attacks launched with malicious reconfigurable intelligent surfaces (RISs) remains…

In cloud computing environments, multiple tenants are often co-located on the same multi-processor system. Thus, preventing information leakage between tenants is crucial. While the hypervisor enforces software isolation, shared hardware,…

The extra-large multiple-input multiple-output (XL-MIMO) architecture has been recognized as a technology for supporting the massive MTC (mMTC), providing very high-data rates in high-user density scenarios. However, the large dimension of…

Data-intensive applications in data centers, especially machine learning (ML), have made the network a bottleneck, which in turn has motivated the development of more efficient network protocols and infrastructure. For instance, remote…

Popular deep learning frameworks require users to fine-tune their memory usage so that the training data of a deep neural network (DNN) fits within the GPU physical memory. Prior work tries to address this restriction by virtualizing the…