Kumo：一个以安全为导向的无服务器云模拟器
Kumo: A Security-Focused Serverless Cloud Simulator
AI总结：
Kumo通过模拟调度和资源共享决策，提供可控的可重复分析安全风险，揭示调度选择对共置攻击的影响及DoS行为的系统因素。
摘要：
无服务器计算抽象了基础设施管理，但也掩盖了可能引入安全风险的系统级行为。先前研究表明，无服务器平台易受共享执行环境攻击的影响，如攻击者-受害者共置和通过资源竞争的拒绝服务攻击，但分析这些风险在生产平台上困难，由于可观测性有限、成本高和缺乏实验控制。现有模拟器主要关注性能和成本而非安全。我们提出了Kumo，一个专注于无服务器平台安全的模拟器，能够对调度和资源共享决策带来的安全风险进行受控、可重复的分析。Kumo在离散事件框架中建模调用到达、调度器放置、容器重用、资源竞争和排队，明确将攻击者和受害者作为第一类实体，并提供共置概率、首次共置时间、调用丢弃率和尾部延迟等指标。通过两个案例研究，我们显示调度选择是共置攻击的首要因素，在相同负载下可产生数量级差异，而拒绝服务行为在竞争主导时主要由系统级因素如服务时间、排队策略和集群容量决定。这些结果突显了区分调度驱动的隔离风险与更广泛的资源耗尽漏洞的必要性，并将Kumo定位为系统性、安全意识探索无服务器平台的灵活基础。
英文摘要：
Serverless computing abstracts infrastructure management but also obscures system-level behaviors that can introduce security risks. Prior work has shown that serverless platforms are vulnerable to attacks exploiting shared execution environments, including attacker--victim co-location and denial-of-service through resource contention, yet analyzing these risks on production platforms is difficult due to limited observability, high cost, and lack of experimental control, while existing simulators primarily focus on performance and cost rather than security. We present Kumo, a security-focused simulator for serverless platforms that enables controlled, reproducible analysis of security risks arising from scheduling and resource sharing decisions. Kumo models invocation arrivals, scheduler placement, container reuse, resource contention, and queuing within a discrete-event framework, explicitly representing attackers and victims as first-class entities and providing metrics such as co-location probability, time to first co-location, invocation drop rate, and tail latency. Through two case studies, we show that scheduler choice is a first-order factor for co-location attacks, inducing orders-of-magnitude differences under identical workloads, while Denial-of-Service behavior is largely governed by system-level factors such as service time, queuing policy, and cluster capacity once contention dominates. These results highlight the need to distinguish scheduler-driven isolation risks from broader resource exhaustion vulnerabilities and position Kumo as a flexible foundation for systematic, security-aware exploration of serverless platforms.
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