Power and energy consumption emerge as critical aspects in computing systems, spanning from mobile devices to data-center servers. Modern processors typically support idle states (i.e., C-states), which deactivate specific hardware components, in addition to offering multiple voltage and frequency states (i.e., P-states). While C-states can significantly reduce static power when processor cores are idle, a notable security vulnerability arises due to differences in wake-up latency among various C-states when the processor cores become active again. This paper proposes a security vulnerability arising from processor idle state management, called BrokenSleep, which exploits the aforementioned wake-up latency differences to create covert and side-channel between computing nodes connected via an external network. This study presents the first remote timing attack based on power management, overcoming the limitations of previous research that required the co-location of attacker and victim applications on the same local machine. This advancement significantly extends the range of existing remote timing attacks by integrating power-related factors. Regardless of the computing system types, our experiments demonstrate that an attacker can transfer data to remote machines without direct network access and deduce the keystroke timing. This vulnerability is not confined to a single processor architecture; it affects processors designed by both Intel and ARM, indicating a widespread potential risk across different hardware platforms.

Data centers face significant performance challenges with parallel processing for network I/O in virtualized environments, particularly for latency-critical (LC) workloads that must satisfy strict Service Level Objectives (SLOs). While previous studies have addressed performance challenges in network I/O virtualization, they overlook the impact of excessive parallelism on the performance of Virtual Machines (VMs). We observe that excessive parallelization for VMs and network I/O processing can lead to core oversubscription, resulting in significant resource contention, frequent preemptions, and task migrations. Based on these observations, we propose vSPACE, dynamic core management specifically designed to support parallel network I/O processing in virtualized environments efficiently. To reduce scheduling contention, vSPACE creates distinct core allocation groups for VM and network I/O and assigns dedicated cores to each. Then, it dynamically adjusts the number of allocated cores to enforce appropriate parallelism for VMs and network I/O processing based on varying demands. vSPACE employs continuous monitoring and a heuristic algorithm to periodically determine appropriate core allocation, addressing excessive contention and improving energy and resource efficiency. vSPACE operates in three modes: performance improvement, energy efficiency, and resource efficiency. Our evaluations demonstrate that vSPACE significantly enhances throughput by up to 4.2 × compared to existing core allocation approaches and improves energy and resource efficiency by up to 16.5% and 30.5%, respectively.

Processor power management exploiting Dynamic Voltage and Frequency Scaling (DVFS) plays a crucial role in improving the data-center’s energy efficiency. However, we observe that current power management policies in Linux (i.e., governors) often considerably increase tail response time (i.e., violate a given Service Level Objective (SLO)) and energy consumption of latency-critical applications. Furthermore, the previously proposed SLO-aware power management policies oversimplify network request processing and ignore the fact that network requests arrive at the application layer in bursts. Considering the complex interplay between the OS and network devices, we propose a power management framework exploiting network packet processing mode transitions in the OS to quickly react to the processing demands from the received network requests. Our proposed power management framework tracks the transitions between polling and interrupt in the network software stack to detect excessive packet processing on the cores and immediately react to the load changes by updating the voltage and frequency (V/F) states. Our experimental results show that our framework does not violate SLO and reduces energy consumption by up to 35.7% and 14.8% compared to Linux governors and state-of-the-art SLO-aware power management techniques, respectively.