Experimental Analysis of Algorithms for Coflow Scheduling

Modern data centers face new scheduling challenges in optimizing job-level performance objectives, where a significant challenge is the scheduling of highly parallel data flows with a common performance goal (e.g., the shuffle operations in MapReduce applications). Chowdhury and Stoica introduced the coflow abstraction to capture these parallel communication patterns, and Chowdhury et al. proposed effective heuristics to schedule coflows efficiently. In our previous paper, we considered the strongly NP-hard problem of minimizing the total weighted completion time of coflows with release dates, and developed the first polynomial-time scheduling algorithms with O(1)-approximation ratios. In this paper, we carry out a comprehensive experimental analysis on a Facebook trace and extensive simulated instances to evaluate the practical performance of several algorithms for coflow scheduling, including the approximation algorithms developed in our previous paper. Our experiments suggest that simple algorithms provide effective approximations of the optimal, and that the performance of our approximation algorithms is relatively robust, near optimal, and always among the best compared with the other algorithms, in both the offline and online settings.Comment: 29 pages, 8 figures, 11 table

Estimating Blood Pressure from Photoplethysmogram Signal and Demographic Features using Machine Learning Techniques

Hypertension is a potentially unsafe health ailment, which can be indicated directly from the Blood pressure (BP). Hypertension always leads to other health complications. Continuous monitoring of BP is very important; however, cuff-based BP measurements are discrete and uncomfortable to the user. To address this need, a cuff-less, continuous and a non-invasive BP measurement system is proposed using Photoplethysmogram (PPG) signal and demographic features using machine learning (ML) algorithms. PPG signals were acquired from 219 subjects, which undergo pre-processing and feature extraction steps. Time, frequency and time-frequency domain features were extracted from the PPG and their derivative signals. Feature selection techniques were used to reduce the computational complexity and to decrease the chance of over-fitting the ML algorithms. The features were then used to train and evaluate ML algorithms. The best regression models were selected for Systolic BP (SBP) and Diastolic BP (DBP) estimation individually. Gaussian Process Regression (GPR) along with ReliefF feature selection algorithm outperforms other algorithms in estimating SBP and DBP with a root-mean-square error (RMSE) of 6.74 and 3.59 respectively. This ML model can be implemented in hardware systems to continuously monitor BP and avoid any critical health conditions due to sudden changes.Comment: Accepted for publication in Sensor, 14 Figures, 14 Table

Entanglement Entropy Near Kondo-Destruction Quantum Critical Points

We study the impurity entanglement entropy $S_e$ in quantum impurity models that feature a Kondo-destruction quantum critical point (QCP) arising from a pseudogap in the conduction-band density of states or from coupling to a bosonic bath. On the local-moment (Kondo-destroyed) side of the QCP, the entanglement entropy contains a critical component that can be related to the order parameter characterizing the quantum phase transition. In Kondo models describing a spin-\Simp, $S_e$ assumes its maximal value of \ln(2\Simp+1) at the QCP and throughout the Kondo phase, independent of features such as particle-hole symmetry and under- or over-screening. In Anderson models, $S_e$ is nonuniversal at the QCP, and at particle-hole symmetry, rises monotonically on passage from the local-moment phase to the Kondo phase; breaking this symmetry can lead to a cusp peak in $S_e$ due to a divergent charge susceptibility at the QCP. Implications of these results for quantum critical systems and quantum dots are discussed.Comment: 15 pages, 8 figures, replaced with published version, Editor's Suggestio

Fast Power Flow with Capability of Corrective Control Using a Neural Network

The authors present a number of different configurations of a neural network and identify a particular case which is most suitable for power flow analysis in real-time applications. The advantage of fast computation of the artificial neural network (ANN) is used for obtaining power flow solutions in real time. The inputs to the ANN are the real and reactive power generating and demand in the system, and the output data are the complex bus voltages. A few configurations of the neural network were experimented with, and the best results were achieved with a single-layer feedforward neural network with nonlinear feedback. By using the trained neural network, an approximate solution of power flow can be obtained almost immediately. One particular configuration of the ANN can be used for determining corrective strategies during abnormal conditions of the power syste

Security Assessment Using Neural Computing

The advantage of fast computation capability of an artificial neural network (ANN) is used to introduce an iterative scheme for security assessment of power systems. Two related approaches are shown which demonstratedly work satisfactorily. The idea of feedback in a single-layer feedforward neural network is experimented yielding higher accuracy. The ANN is trained by using a set of data obtained from off-line analysis of the power network. After training, an approximate solution for a given condition may be found almost immediately. The approximate solution obtained is judged adequate for assessing the security of the power system. A case study is also presented for demonstrating the applicability of the approach

Performance of Doubly Fed-Induction Machine Wind-Generators During Grid and Wind Disturbances

A doubly fed induction generator is under study for application with a wind turbine. The operation of the Doubly Fed Induction Generator (DFIG) under different disturbances namely, response to gradual increase or decrease in wind speeds, sudden change of wind speeds like wind gust and different fault scenarios near and farther to the wind turbine are demonstrated in this paper using the wind model developed by DIgSILENT® in their PowerFactory software

Implementation of a Converter in Sequence Domain to Counter Voltage Imbalances

This paper demonstrates the basic problem of the conventional d-axis and q-axis control applied to a voltage source converter (VSC) during a system imbalance. The specific imbalances studied are a balanced fault and an unbalanced fault at the terminals of the converter. The dc link voltage of the VSC is disturbed during the unbalanced fault due to negative sequence components in the system. The sequence characteristics of the system during an imbalance are further analyzed. A novel controller in the sequence domain is proposed. The positive sequence components of the control are implemented in the positive synchronous reference frame and the negative sequence components of the control are implemented in the negative synchronous reference frame. The negative sequence components are commanded to zero in the control. The approach demonstrates the stabilization of the voltage to a greater extent. The control also points out the need for a storage element when the VSC is to be used for application with that of a Doubly-Fed Induction Generators (DFIG) in wind turbines