Search arXiv⌕ Search

arXiv · 0705.2786

Virtualization: A double-edged sword

Abstract

Virtualization became recently a hot topic once again, after being dormant for more than twenty years. In the meantime, it has been almost forgotten, that virtual machines are not so perfect isolating environments as it seems, when looking at the principles. These lessons were already learnt earlier when the first virtualized systems have been exposed to real life usage. Contemporary virtualization software enables instant creation and destruction of virtual machines on a host, live migration from one host to another, execution history manipulation, etc. These features are very useful in practice, but also causing headaches among security specialists, especially in current hostile network environments. In the present contribution we discuss the principles, potential benefits and risks of virtualization in a deja vu perspective, related to previous experiences with virtualization in the mainframe era.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Joachim J. Wlodarz. 2007-05-19. Virtualization: A double-edged sword. https://arxiv.org/abs/0705.2786

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Breaking the Coupling: Efficient Concurrency Fuzzing for Linux Kernel Data Race Detection

Detecting data races in the Linux kernel requires exploring both the input space and the thread-interleaving space. Existing kernel concurrency fuzzers couple these two searches by exploring interleavings during fuzzing, often spending substantial scheduling effort on inputs with little potential to trigger real races. In this paper, we present a new concurrency fuzzing paradigm that decouples concurrent-input generation from thread-interleaving exploration, enabling expensive scheduling to focus on promising inputs. To identify inputs that warrant targeted scheduling, we introduce May Race Pairs (MRPs), which represent conflicting memory-access pairs observed within a dynamically determined time threshold. Our framework first generates and executes concurrent syscall groups without controlled scheduling, collecting MRPs to guide further input mutation and identify schedule-worthy inputs. It then uses a separate scheduling instance to explore targeted interleavings and confirm real data races. To improve the effectiveness of both stages, we incorporate LLM-assisted semantic mutation for generating diverse syscall groups, backpressure-guided dynamic threshold control for balancing candidate production and scheduling consumption, and history-aware context recovery, context-aware interleaving scheduling, and candidate MRP prioritization for efficient race confirmation. We implement our framework for the Linux kernel and evaluate it on eight common kernel modules in Linux 6.17. It discovers 187 unique real-world data races, including 59 harmful races. Under the same testing budget, our approach detects substantially more data races than existing kernel concurrency fuzzers, including SegFuzz and CONZZER. These results demonstrate the effectiveness of decoupling concurrent-input generation from interleaving exploration and concentrating scheduling effort on promising candidates.

cs.OS↗

SARIA: Persistence-Aware KV Caching with Coordinated DRAM--SSD Reload for LLM Agents

Multi-turn LLM agents usually reuse long context prefixes, making key value (KV) caching crucial for reducing prefill latency. SSDs provide a low-cost extension to limited DRAM capacity but introduce two key challenges. Eagerly persisting all KV cache blocks incurs substantial write traffic and rapidly consumes finite SSD endurance. Meanwhile, the small number of SSDs provisioned in typical GPU servers limits aggregate reload bandwidth, making SSD-resident KV cache much slower to retrieve than that in DRAM and inflating prefill latency. We present \textsc{Saria} (Selective Admission and Reload for Inference Acceleration), a KV caching system that jointly manages DRAM and SSD by exploiting the session-level temporal locality of LLM agents to reduce SSD writes and accelerate cache reload. On the write path, \textsc{Saria} uses eviction and reuse history to selectively persist KV cache blocks that exhibit reuse opportunities beyond their DRAM residency, avoiding unnecessary writes for KV cache already hosted in DRAM. On the read path, \textsc{Saria} promotes selected SSD-hit KV cache blocks from GPU memory into DRAM after prefill, allowing subsequent reuse within the same session to reduce SSD read traffic while limiting DRAM contention. A transfer coordinator paces DRAM reads while SSD transfers are active, allowing both paths to make progress and efficiently share GPU ingress bandwidth. We implement \textsc{Saria} on LMCache MP and evaluate it using workloads from TraceLab and WEKA, covering both full-attention and hybrid-attention models. The results show that \textsc{Saria} reduces SSD writes by 57.1--58.9\% compared with popular full duplicate caching and the mean time to first token (TTFT) by 42.3--79.7\% over DRAM-only caching.

cs.OS↗

CXLResume: Retained-State Checkpoint and Restore for In-Memory Services

Checkpointing and restoring an in-memory service typically require writing its memory pages to checkpoint images and refilling them at the destination. When application state already resides in shared memory that remains accessible after failover, copying those pages into and out of checkpoint images is unnecessary. We present CXLResume, a prototype that retains the service's main heap objects in shared memory while checkpointing the remaining execution state and mapping metadata. Checkpointing preserves the shared heap at the same freeze point as the serialized process state. During recovery, CXLResume remaps the same shared memory at its original virtual addresses before restoring execution, preserving direct pointers within and across shared and private memory. We evaluate CXLResume with Redis and Memcached using QEMU CXL functional emulation. At 6 GiB, CXLResume reduces combined checkpoint and restore time from 15.015 s to 0.242 s for Redis and from 19.770 s to 0.276 s for Memcached in the evaluated deployments. These results show that retaining shared application state removes large application heaps from the checkpoint data path and substantially reduces checkpoint and restore latency.

cs.OS↗