Qos-aware fine-grained power management in networked computing systems

Power is a major design concern of today\u27s networked computing systems, from low-power battery-powered mobile and embedded systems to high-power enterprise servers. Embedded systems are required to be power efficiency because most embedded systems are powered by battery with limited capacity. Similar concern of power expenditure rises as well in enterprise server environments due to cooling requirement, power delivery limit, electricity costs as well as environment pollutions. The power consumption in networked computing systems includes that on circuit board and that for communication. In the context of networked real-time systems, the power dissipation on wireless communication is more significant than that on circuit board. We focus on packet scheduling for wireless real-time systems with renewable energy resources. In such a scenario, it is required to transmit data with higher level of importance periodically. We formulate this packet scheduling problem as an NP-hard reward maximization problem with time and energy constraints. An optimal solution with pseudo polynomial time complexity is presented. In addition, we propose a sub-optimal solution with polynomial time complexity. Circuit board, especially processor, power consumption is still the major source of system power consumption. We provide a general-purposed, practical and comprehensive power management middleware for networked computing systems to manage circuit board power consumption thus to affect system-level power consumption. It has the functionalities of power and performance monitoring, power management (PM) policy selection and PM control, as well as energy efficiency analysis. This middleware includes an extensible PM policy library. We implemented a prototype of this middleware on Base Band Units (BBUs) with three PM policies enclosed. These policies have been validated on different platforms, such as enterprise servers, virtual environments and BBUs. In enterprise environments, the power dissipation on circuit board dominates. Regulation on computing resources on board has a significant impact on power consumption. Dynamic Voltage and Frequency Scaling (DVFS) is an effective technique to conserve energy consumption. We investigate system-level power management in order to avoid system failures due to power capacity overload or overheating. This management needs to control the power consumption in an accurate and responsive manner, which cannot be achieve by the existing black-box feedback control. Thus we present a model-predictive feedback controller to regulate processor frequency so that power budget can be satisfied without significant loss on performance. In addition to providing power guarantee alone, performance with respect to service-level agreements (SLAs) is required to be guaranteed as well. The proliferation of virtualization technology imposes new challenges on power management due to resource sharing. It is hard to achieve optimization in both power and performance on shared infrastructures due to system dynamics. We propose vPnP, a feedback control based coordination approach providing guarantee on application-level performance and underlying physical host power consumption in virtualized environments. This system can adapt gracefully to workload change. The preliminary results show its flexibility to achieve different levels of tradeoffs between power and performance as well as its robustness over a variety of workloads. It is desirable for improve energy efficiency of systems, such as BBUs, hosting soft-real time applications. We proposed a power management strategy for controlling delay and minimizing power consumption using DVFS. We use the Robbins-Monro (RM) stochastic approximation method to estimate delay quantile. We couple a fuzzy controller with the RM algorithm to scale CPU frequency that will maintain performance within the specified QoS

FUS-NLS/Transportin 1 complex structure provides insights into the nuclear targeting mechanism of FUS and the implications in ALS

The C-terminal nuclear localization sequence of FUsed in Sarcoma (FUS-NLS) is critical for its nuclear import mediated by transportin (Trn1). Familial amyotrophic lateral sclerosis (ALS) related mutations are clustered in FUS-NLS. We report here the structural, biochemical and cell biological characterization of the FUS-NLS and its clinical implications. The crystal structure of the FUS-NLS/Trn1 complex shows extensive contacts between the two proteins and a unique α-helical structure in the FUS-NLS. The binding affinity between Trn1 and FUS-NLS (wide-type and 12 ALS-associated mutants) was determined. As compared to the wide-type FUS-NLS (K(D) = 1.7 nM), each ALS-associated mutation caused a decreased affinity and the range of this reduction varied widely from 1.4-fold over 700-fold. The affinity of the mutants correlated with the extent of impaired nuclear localization, and more importantly, with the duration of disease progression in ALS patients. This study provides a comprehensive understanding of the nuclear targeting mechanism of FUS and illustrates the significance of FUS-NLS in ALS

Research Advance on Prediction and Optimization for Fracture Propagation in Stimulated Unconventional Reservoirs

AbstractMultistage stimulation horizontal wells are prerequisite technologies for efficient development of unconventional reservoir. However, the induced fracture network morphology from hydraulic fracturing is very complex and affected by many factors, such as the in situ stress, rock mechanical properties, and natural fracture distribution. The large numbers of natural fractures and strong reservoir heterogeneity in unconventional reservoirs result in enhanced complexity of induced fractures from hydraulic fracturing. Accurate description of fracture network morphology and the flow capacity in different fractures form an important basis for production forecasting, evaluation (or optimization) of stimulation design, and development plan optimization. This paper focuses on hydraulic fracturing in unconventional reservoirs and discusses the current research advances from four aspects: (1) the prediction of induced fracture propagation, (2) the simulation of fluid flow in complex fracture networks, (3) the inversion of fracture parameter (fracture porosity, fracture permeability, etc.), and (4) the optimization of hydraulic fracturing in unconventional reservoirs. In addition, this paper provides comparative analysis of the characteristics and shortcomings of the current research by outlining the key technical problems in the study of flow characterization, parameter inversion, and optimization methods for stimulation in unconventional reservoirs. This work can provide a certain guiding role for further research

High performance integrated photonic circuit based on inverse design method

The basic indexes of all-optical integrated photonic circuits include high-density integration, ultrafast response and ultra-low energy consumption. Traditional methods mainly adopt conventional micro/nano-structures. The overall size of the circuit is large, usually reaches hundreds of microns. Besides, it is difficult to balance the ultrafast response and ultra-low energy consumption problem, and the crosstalk between two traditional devices is difficult to overcome. Here, we propose and experimentally demonstrate an approach based on inverse design method to realize a high-density, ultrafast and ultra-low energy consumption integrated photonic circuit with two all-optical switches controlling the input states of an all-optical XOR logic gate. The feature size of the whole circuit is only 2.5 μm × 7 μm, and that of a single device is 2 μm × 2 μm. The distance between two adjacent devices is as small as 1.5 μm, within wavelength magnitude scale. Theoretical response time of the circuit is 150 fs, and the threshold energy is within 10 fJ/bit. We have also considered the crosstalk problem. The circuit also realizes a function of identifying two-digit logic signal results. Our work provides a new idea for the design of ultrafast, ultra-low energy consumption all-optical devices and the implementation of high-density photonic integrated circuits

SPatiotemporal-ENcoded acoustic radiation force imaging of focused ultrasound

Neuromodulation technology has provided novel therapeutic approaches for diseases caused by neural circuit dysfunction. Transcranial focused ultrasound (FU) is an emerging neuromodulation approach that combines noninvasiveness with relatively sharp focus, even in deep brain regions. It has numerous advantages such as high precision and good safety in neuromodulation, allowing for modulation of both peripheral and central nervous systems. To ensure accurate treatment targeting in FU neuromodulation, a magnetic resonance acoustic radiation force imaging (MR-ARFI) sequence is crucial for the visualization of the focal point. Currently, the commonly used 2D Spin Echo ARFI (2D SE-ARFI) sequence suffers from the long acquisition time, while the echo planar imaging ARFI (EPI-ARFI) sequence with a shorter acquisition time is vulnerable to the magnetic field inhomogeneities. To address these problems, we proposed a spatiotemporal-encoded acoustic radiation force imaging sequence (i.e., SE-SPEN-ARFI, shortened to SPEN-ARFI) in this study. The displacement at the focal spot obtained was highly consistent with that of the SE-ARFI sequence. Our research shows that SPEN-ARFI allows for rapid image acquisition and has less image distortions even under great field inhomogeneities. Therefore, a SPEN-ARFI sequence is a practical alternative for the treatment planning in ultrasound neuromodulation

CloudBrain-MRS: An Intelligent Cloud Computing Platform for in vivo Magnetic Resonance Spectroscopy Preprocessing, Quantification, and Analysis

Magnetic resonance spectroscopy (MRS) is an important clinical imaging method for diagnosis of diseases. MRS spectrum is used to observe the signal intensity of metabolites or further infer their concentrations. Although the magnetic resonance vendors commonly provide basic functions of spectra plots and metabolite quantification, the widespread clinical research of MRS is still limited due to the lack of easy-to-use processing software or platform. To address this issue, we have developed CloudBrain-MRS, a cloud-based online platform that provides powerful hardware and advanced algorithms. The platform can be accessed simply through a web browser, without the need of any program installation on the user side. CloudBrain-MRS also integrates the classic LCModel and advanced artificial intelligence algorithms and supports batch preprocessing, quantification, and analysis of MRS data from different vendors. Additionally, the platform offers useful functions: 1) Automatically statistical analysis to find biomarkers for diseases; 2) Consistency verification between the classic and artificial intelligence quantification algorithms; 3) Colorful three-dimensional visualization for easy observation of individual metabolite spectrum. Last, both healthy and mild cognitive impairment patient data are used to demonstrate the functions of the platform. To the best of our knowledge, this is the first cloud computing platform for in vivo MRS with artificial intelligence processing. We have shared our cloud platform at MRSHub, providing free access and service for two years. Please visit https://mrshub.org/software_all/#CloudBrain-MRS or https://csrc.xmu.edu.cn/CloudBrain.html.Comment: 11 pages, 12 figure