Fine-grained Power Control Research Articles

Integrated Power Anomaly Defense: Towards Oversubscription-Safe Data Centers

Energy storage devices (e.g., batteries) are critical components for high-availability data center infrastructure today. Without resilient energy management of these devices, existing power-hungry data centers are largely unguarded targets for cyber criminals. Particularly for some of today's scale-out data centers, power infrastructure oversubscription unavoidably taxes the data center's backup energy resources (i.e., UPS), leaving very little room for dealing with power emergency. As a result, an attacker could manipulate the computing system to generate peak power demand and disrupt power-constrained server racks. This article aims at protecting data centers from malicious loads that seek to drain precious energy backup, overload server racks and compromise workload performance. We term such load as Elusive Power Peak (EPP) and demonstrate its basic three-phase attacking model. To defend against EPP, we propose IPAD, a remediation solution build on integrated software and hardware mechanisms. IPAD not only increases the attacking cost considerably by hiding vulnerable server racks from visible power peaks, but also strengthens the last line of defense against hidden power spikes with fine-grained power control strategy. We show that IPAD can effectively raise the bar of power-related attack, with reasonable design overhead.

Voltage Regulator Efficiency Aware Power Management

Conventional off-chip voltage regulators are typically bulky and slow, and are inefficient at exploiting system and workload variability using Dynamic Voltage and Frequency Scaling (DVFS). On-die integration of voltage regulators has the potential to increase the energy efficiency of computer systems by enabling power control at a fine granularity in both space and time. The energy conversion efficiency of on-chip regulators, however, is typically much lower than off-chip regulators, which results in significant energy losses. Fine-grained power control and high voltage regulator efficiency are difficult to achieve simultaneously, with either emerging on-chip or conventional off-chip regulators. A voltage conversion framework that relies on a hierarchy of off-chip switching regulators and on-chip linear regulators is proposed to enable fine-grained power control with a regulator efficiency greater than 90%. A DVFS control policy that is based on a reinforcement learning (RL) approach is developed to exploit the proposed framework. Per-core RL agents learn and improve their control policies independently, while retaining the ability to coordinate their actions to accomplish system level power management objectives. When evaluated on a mix of 14 parallel and 13 multiprogrammed workloads, the proposed voltage conversion framework achieves 18% greater energy efficiency than a conventional framework that uses on-chip switching regulators. Moreover, when the RL based DVFS control policy is used to control the proposed voltage conversion framework, the system achieves a 21% higher energy efficiency over a baseline oracle policy with coarse-grained power control capability.

Voltage Regulator Efficiency Aware Power Management

Conventional off-chip voltage regulators are typically bulky and slow, and are inefficient at exploiting system and workload variability using Dynamic Voltage and Frequency Scaling (DVFS). On-die integration of voltage regulators has the potential to increase the energy efficiency of computer systems by enabling power control at a fine granularity in both space and time. The energy conversion efficiency of on-chip regulators, however, is typically much lower than off-chip regulators, which results in significant energy losses. Fine-grained power control and high voltage regulator efficiency are difficult to achieve simultaneously, with either emerging on-chip or conventional off-chip regulators. A voltage conversion framework that relies on a hierarchy of off-chip switching regulators and on-chip linear regulators is proposed to enable fine-grained power control with a regulator efficiency greater than 90%. A DVFS control policy that is based on a reinforcement learning (RL) approach is developed to exploit the proposed framework. Per-core RL agents learn and improve their control policies independently, while retaining the ability to coordinate their actions to accomplish system level power management objectives. When evaluated on a mix of 14 parallel and 13 multiprogrammed workloads, the proposed voltage conversion framework achieves 18% greater energy efficiency than a conventional framework that uses on-chip switching regulators. Moreover, when the RL based DVFS control policy is used to control the proposed voltage conversion framework, the system achieves a 21% higher energy efficiency over a baseline oracle policy with coarse-grained power control capability.

Experimental and quantitative analysis of server power model for cloud data centers

Scientific computing applications like online social network analysis demand enormous computing capability from cloud service, but now the high energy consumption by cloud data centers has brought more concerns on power monitoring and management to cloud service providers (CSPs). Compared with hardware-based traditional techniques, server power monitoring based on power model is of higher scalability as well as lower deployment cost and thus, is more feasible for cloud data center power management. However, previous studies lack a systematic review and quantitative analysis on server power model. In this paper, we review and compare several popular power models of cloud server components including CPU, vCPU, memory and hard disk. We propose an I/O-mode aware disk power model based on our observation of disk power behavior. Experimentally, we first analyze the accuracy of different CPU power models by looking into a SPECpower_ssj2008 dataset. We also carried out experiments on a physical server to evaluate memory power models and disk power models. The experimental results indicate the advantage of polynomial CPU model, LLCM-based memory model and the proposed disk model. The ideology of component-level power modeling presented in this paper helps realize fine-grained power control. Moreover, the evaluation and comparison results provide CSPs with useful guidance on optimizing energy management of cloud data centers.

On the (in)feasibility of fine grained power control

Spectral efficiency has been a major focus of research in wireless networks. Recent theoretical work has shown that ideal medium access protocol using optimal power control can improve channel utilization by up to a factor of √ρ, where ρ is the density of nodes in the region [1]. Although power control mechanisms have been extensively studied in theory and simulations, their practical evaluation has not been done throughly. Most of the power control mechanisms proposed in literature [3, 4, 5] assume a very fine grained handle on transmit power of wireless nodes and exercise this control to provide sophisticated mechanisms of sharing wireless medium among competing flows. In this work, we investigate the (in)feasibility of fine-grained power control in wireless networks using real testbed experiments. We perform detailed experiments to highlight fundamental issues with power control mechanisms, that cannot be captured by using a network simulator. Our experiments indicate that fine grained power control may not be a viable solution for indoor wireless networks and most power control mechanisms may need to be adapted to be suitable for practical settings.