Improving energy efficiency to reduce costs in server environments has attracted considerable attention. Considering that processors account for a significant portion of energy consumption in servers, Dynamic Voltage and Frequency Scaling (DVFS) enhances their energy efficiency by adjusting the operational speed and power consumption of processors. Additionally, modern high-end processors extend DVFS functionality not only to core components but also to uncore parts. This is because the increasing complexity and integration of System on Chips (SoCs) have highlighted the substantial energy consumption. However, existing uncore voltage/frequency scaling fails to effectively consider Latency-Critical (LC) applications, leading to sub-optimal energy efficiency or degraded performance. In this paper, we introduce Co-UP, power management that simultaneously scales core and uncore frequencies for latency-critical applications, designed to improve energy efficiency without violating Service Level Objectives (SLOs). To this end, Co-UP incorporates a prediction model that estimates outcomes of energy consumption and performance as uncore and core frequency changes. Based on the estimated gains, Co-UP adjusts to uncore and/or core frequencies to further enhance energy efficiency or performance. This predictive model can rapidly adapt to new and unlearned loads, enabling Co-UP to operate online without any prior profiling. Our experiments show that Co-UP can reduce energy consumption by up to 28.2% compared to existing Intel's policy and up to 17.6% compared to state-of-the-art power management studies, without SLO violations.

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.

Distributed Deep Learning (DDL), as a paradigm, dictates the use of GPU-based clusters as the optimal infrastructure for training large-scale Deep Neural Networks (DNNs). However, the high cost of such resources makes them inaccessible to many users. Public cloud services, particularly Spot Virtual Machines (VMs), offer a cost-effective alternative, but their unpredictable availability poses a significant challenge to the crucial checkpointing process in DDL. To address this, we introduce DeepVM, a novel solution that recommends cost-effective cluster configurations by intelligently balancing the use of Spot and On-Demand VMs. DeepVM leverages a four-stage process that analyzes instance performance using the FLOPP (FLoating-point Operations Per Price) metric, performs architecture-level analysis with linear programming, and identifies the optimal configuration for the user-specific needs. Extensive simulations and real-world deployments in the AWS environment demonstrate that DeepVM consistently outperforms other policies, reducing training costs and overall makespan. By enabling cost-effective checkpointing with Spot VMs, DeepVM opens up DDL to a wider range of users and facilitates a more efficient training of complex DNNs.

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.

Power and energy consumed by DRAM comprising main memory of data-center servers have increased substantially as the capacity and bandwidth of memory increase. Especially, the fraction of DRAM background power in DRAM total power is already high, and it will continue to increase with the decelerating DRAM technology scaling as we will have to plug more DRAM modules in servers or stack more DRAM dies in a DRAM package to provide necessary DRAM capacity in the future. To reduce the background power, we may exploit low average utilization of the DRAM capacity in data-center servers (i.e., 40–60%) for DRAM power management. Nonetheless, the current DRAM power management supports low-power states only at the rank granularity, which becomes ineffective with memory interleaving techniques devised to disperse memory requests across ranks. That is, ranks need to be frequently woken up from low-power states with aggressive power management, which can significantly degrade system performance, or they do not get a chance to enter low-power states with conservative power management.

In this paper, we first analyze the effects of interrupt management on response latency of the latency-critical application and the efficiency of current dynamic power management governor which determines Voltage and Frequency States (V/F states) based on CPU utilization. We also demonstrate that interrupt management provides a governor an opportunity to decrease the V/F state without performance degradation. Next, we propose I-state that adjusts the interrupt rate based on the V/F state determined by the governor. When a core is highly utilized with a high V/F state, I-state improves response latency by decreasing interrupt rate, moderating the load on the CPU. I-state also improves energy-efficiency by making the governor decrease the V/F state more often. When a core is not highly utilized while operating at low V/F states, I-state improves response latency by increasing interrupt rate, which can notify the processor of packet arrivals faster so that the CPU processes the packets quickly. Our experimental results show that I-state improves 95thpercentile latency by up to 15.9x while reducing energy consumption by up to 17.6%.

A power management policy aims to improve energy efficiency by choosing an appropriate performance (voltage/frequency) state for a given core. In current virtualized environments, multiple virtual machines (VMs) running on the same core must follow a single power management policy governed by the hypervisor. However, we observe that such a per-core power management policy has two limitations. First, it cannot offer the flexibility of choosing a desirable power management policy for each VM (or client). Second, it often hurts the power efficiency of some or even all VMs especially when the VMs desire conflicting power management policies. To tackle these limitations, we propose a per-VM power management mechanism, VIP supporting Virtual Performance-state for each VM. Specifically, for VMs sharing a core, VIP allows each VM's guest OS to deploy its own desired power management policy while preventing such VMs from interfering/influencing each other's power management policy. That is, VIP can also facilitate a pricing model based on the choice of a power management policy. Second, identifying some inefficiency in strictly enforcing per-VM power management policies, we propose hypervisor-assisted techniques to further improve power and energy efficiency without compromising the key benefits of per-VM power management. To demonstrate the efficacy of VIP, we take a case that some VMs run CPU-intensive applications and other VMs run latency-sensitive applications sharing the same cores. Our evaluation shows that VIP reduces the overall energy consumption and improves the execution time of CPU-intensive applications compared with the default ondemand governor of Xen hypervisor up to 27% and 32%, respectively, without violating service level agreement (SLA) of latency-sensitive applications.

This paper focuses on analyzing the performance of sensor fusion models for a brake pedal in electromechanical braking (EMB) systems. To properly control a brake, a signal obtained from a pedal is important and must be stable. Sensor fusion models can be used to make the pedal signal more stable and resilient against the abnormalities. In this work, a pedal sensor and a pedal effort sensor are exploited for the sensor fusion to control the brake. Then, we analyze the performance of each fusion model (e.g., average method, moving average method, median filter, iterative filter, and Marzullo's algorithm) in various fault scenarios. We propose the hybrid method using multiple models to have the better resiliency and to detect the faulty sensor as well. For the practical experiments, we employ the real measurement data set obtained from a vehicle at different speeds. Then, its fused result is validated using EMB system test bench to confirm how the fused result influences on the motor of the brake.

While vehicle to everything (V2X) communication enables safety-critical automotive control systems to better support various connected services to improve safety and convenience of drivers, they also allow automotive attack surfaces to increase dynamically in modern vehicles. Many researchers as well as hackers have already demonstrated that they can take remote control of the targeted car by exploiting the vulnerabilities of in-vehicle networks such as Controller Area Networks (CANs). For assuring CAN security, we focus on how to authenticate electronic control units (ECUs) in real-time by addressing the security challenges of in-vehicle networks. In this paper, we propose a novel and lightweight authentication protocol with an attack-resilient tree algorithm, which is based on one-way hash chain. The protocol can be easily deployed in CAN by performing a firmware update of ECU. We have shown analytically that the protocol achieves a high level of security. In addition, the performance of the proposed protocol is validated on CANoe simulator for virtual ECUs and Freescale S12XF used in real vehicles. The results show that our protocol is more efficient than other authentication protocol in terms of authentication time, response time, and service delay.

Smarthome has been developed for enhancing convenience and safety, and robots are one of the major parts of the smarthome. The robots assist the elderly and disabled people by helping specific tasks such as drug delivery or following human commands to control smarthome devices. They perform daily activities instead of humans for improving quality of life in the smarthome. Even though throwing away trash is one of daily activities and a simple task, it is sometimes difficult and inconvenient especially when a trash bin is far away from people with movement disabilities. To provide convenience in the task, we propose a smart-bin robot platform. Main function of the smartbin is to deliver the trash bin close to a user. We used Ultrawideband (UWB) sensors since they provide centimeter level accuracy and can be used as ground truth data for the future indoor localization researches. In this paper, we propose a basic robot platform delivering a trash bin in smarthome. The robot platform has been installed in our smathome testbed and to demonstrate how the smart-bin can be helpful to people with movement disabilities.

In the age of smart and connected vehicles, there are significant issues in providing security for in-vehicle networking. Many security efforts for in-vehicle networks are still insufficient to build a lightweight security mechanism. Typically, it comes from the limitations of Controller Area Network (CAN) protocol common to in-vehicle network. We propose a lightweight authentication method based on one-way hash chain in CAN. In addition, we identify three technical challenges to be addressed for the proposed method and present our key idea to address them.