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With the increasing use and adoption of cloud and cloud-native computing, the underlying technologies (i.e., containerization and virtualization) have become foundational. However, strict isolation and maintaining runtime security in these…
Code often suffers from performance bugs. These bugs necessitate the research and practice of code optimization. Traditional rule-based methods rely on manually designing and maintaining rules for specific performance bugs (e.g., redundant…
Subspace optimization methods have the attractive property of reducing large-scale optimization problems to a sequence of low-dimensional subspace optimization problems. However, existing subspace optimization frameworks adopt a fixed…
System-level resource monitoring with both precision and efficiency is a continuous challenge. We introduce eHashPipe, a lightweight, real-time resource observability system utilizing eBPF and the HashPipe sketching algorithm. eHashPipe…
Program optimization is the process of modifying software to execute more efficiently. Superoptimizers attempt to find the optimal program by employing significantly more expensive search and constraint solving techniques. Generally, these…
We propose repair pipelining, a technique that speeds up the repair performance in general erasure-coded storage. By carefully scheduling the repair of failed data in small-size units across storage nodes in a pipelined manner, repair…
eBPF is a new technology which allows dynamically loading pieces of code into the Linux kernel. It can greatly speed up networking since it enables the kernel to process certain packets without the involvement of a userspace program. So far…
In this paper, we propose a novel quantum compiler optimization, named relaxed peephole optimization (RPO) for quantum computers. RPO leverages the single-qubit state information that can be determined statically by the compiler. We define…
Linux-based cloud environments have become lucrative targets for ransomware attacks, employing various encryption schemes at unprecedented speeds. Addressing the urgency for real-time ransomware protection, we propose leveraging the…
Searchable Symmetric Encryption (SSE) allows users to search over encrypted data stored on untrusted servers, like cloud providers. While SSE hides the content of queries and documents, it still leaks patterns, such as how often a query is…
Safe reinforcement learning (safe RL) aims to respect safety requirements while optimizing long-term performance. In many practical applications, however, the problem involves an infinite number of constraints, known as semi-infinite safe…
As distributed optimization scales to meet the demands of Large Language Model (LLM) training, hardware failures become increasingly non-negligible. Existing fault-tolerant training methods often introduce significant computational or…
As the volume of data that needs to be processed continues to increase, we also see renewed interests in near-data processing in the form of computational storage, with eBPF (extended Berkeley Packet Filter) being proposed as a vehicle for…
Resource-constrained embedded systems demand secure yet lightweight data protection, particularly when data formats must be preserved. This paper introduces eFPE (Enhanced Format-Preserving Encryption), an 8-round Feistel cipher featuring a…
Despite their success, kernel methods suffer from a massive computational cost in practice. In this paper, in lieu of commonly used kernel expansion with respect to $N$ inputs, we develop a novel optimal design maximizing the entropy among…
Dynamic analysis based on the full-system emulator QEMU is widely used for various purposes. However, it is challenging to run firmware images of embedded devices in QEMU, especially the process to boot the Linux kernel (we call this…
Quantum error-correcting codes protect fragile quantum information by encoding it redundantly, but identifying codes that perform well in practice with minimal overhead remains difficult due to the combinatorial search space and the high…
Offline reinforcement learning (RL) aims to learn decision policies from a fixed batch of logged transitions, without additional environment interaction. Despite remarkable empirical progress, offline RL remains fragile under distribution…
Inaccuracies in conventional dependency-tracking methods frequently undermine the security and integrity of modern software supply chains. This paper introduces a kernel-level framework leveraging extended Berkeley Packet Filter (eBPF) to…
Repeated sampling with a verifier is the standard way to allocate test-time compute for code generation, with pass@$K$ as the canonical metric. Yet the standard policy class draws $K$ independent samples from a single answer distribution,…