Fuzzy Modeling and Control Based Virtual Machine Resource Management

Virtual machines (VMs) are powerful platforms for building agile datacenters and emerging cloud systems. However, resource management for a VM-based system is still a challenging task. First, the complexity of application workloads as well as the interference among competing workloads makes it difficult to understand their VMs’ resource demands for meeting their Quality of Service (QoS) targets; Second, the dynamics in the applications and system makes it also difficult to maintain the desired QoS target while the environment changes; Third, the transparency of virtualization presents a hurdle for guest-layer application and host-layer VM scheduler to cooperate and improve application QoS and system efficiency. This dissertation proposes to address the above challenges through fuzzy modeling and control theory based VM resource management. First, a fuzzy-logic-based nonlinear modeling approach is proposed to accurately capture a VM’s complex demands of multiple types of resources automatically online based on the observed workload and resource usages. Second, to enable fast adaption for resource management, the fuzzy modeling approach is integrated with a predictive-control-based controller to form a new Fuzzy Modeling Predictive Control (FMPC) approach which can quickly track the applications’ QoS targets and optimize the resource allocations under dynamic changes in the system. Finally, to address the limitations of black-box-based resource management solutions, a cross-layer optimization approach is proposed to enable cooperation between a VM’s host and guest layers and further improve the application QoS and resource usage efficiency. The above proposed approaches are prototyped and evaluated on a Xen-based virtualized system and evaluated with representative benchmarks including TPC-H, RUBiS, and TerraFly. The results demonstrate that the fuzzy-modeling-based approach improves the accuracy in resource prediction by up to 31.4% compared to conventional regression approaches. The FMPC approach substantially outperforms the traditional linear-model-based predictive control approach in meeting application QoS targets for an oversubscribed system. It is able to manage dynamic VM resource allocations and migrations for over 100 concurrent VMs across multiple hosts with good efficiency. Finally, the cross-layer optimization approach further improves the performance of a virtualized application by up to 40% when the resources are contended by dynamic workloads.

Gamification e Machine Learning per migliorare l’esperienza di guida e diminuire i consumi in un veicolo elettrico

Questo progetto di tesi è parte di un programma più ampio chiamato TIME (Tecnologia Integrata per Mobilità Elettrica) sviluppato tra diversi gruppi di ricerca afferenti al settore meccanico, termofluidodinamico e informatico. TIME si pone l'obiettivo di migliorare la qualità dei componenti di un sistema powertrain presenti oggi sul mercato progettando un sistema general purpose adatto ad essere installato su veicoli di prima fornitura ma soprattutto su retrofit, quindi permettendo il ricondizionamento di veicoli con motore a combustione esistenti ma troppo datati. Lo studio svolto si pone l'obiettivo di identificare tutti gli aspetti di innovazione tecnologica che possono essere installati all'interno del sistema di interazione uomo-macchina. All'interno di questo progetto sarà effettuata una pianificazione di tutto il lavoro del gruppo di ricerca CIRI-ICT, partendo dallo studio normativo ed ergonomico delle interfacce dei veicoli analizzando tutti gli elementi di innovazione che potranno far parte del sistema TIME e quindi programmare tutte le attività previste al fine di raggiungere gli obiettivi prefissati, documentando opportunamente tutto il processo. Nello specifico saranno analizzate e definite le tecniche da utilizzare per poi procedere alla progettazione e implementazione di un primo sistema sperimentale di Machine Learning e Gamification con lo scopo di predire lo stato della batteria in base allo stile di guida dell'utente e incentivare quest'ultimo tramite sistemi di Gamification installati sul cruscotto ad una guida più consapevole dei consumi. Questo sistema sarà testato su dati simulati con l'obiettivo di avere un prodotto configurabile da installare sul veicolo.

Riconoscimento real-time di gesture tramite tecniche di machine learning

Il riconoscimento delle gesture è un tema di ricerca che sta acquisendo sempre più popolarità, specialmente negli ultimi anni, grazie ai progressi tecnologici dei dispositivi embedded e dei sensori. Lo scopo di questa tesi è quello di utilizzare alcune tecniche di machine learning per realizzare un sistema in grado di riconoscere e classificare in tempo reale i gesti delle mani, a partire dai segnali mioelettrici (EMG) prodotti dai muscoli. Inoltre, per consentire il riconoscimento di movimenti spaziali complessi, verranno elaborati anche segnali di tipo inerziale, provenienti da una Inertial Measurement Unit (IMU) provvista di accelerometro, giroscopio e magnetometro. La prima parte della tesi, oltre ad offrire una panoramica sui dispositivi wearable e sui sensori, si occuperà di analizzare alcune tecniche per la classificazione di sequenze temporali, evidenziandone vantaggi e svantaggi. In particolare, verranno considerati approcci basati su Dynamic Time Warping (DTW), Hidden Markov Models (HMM), e reti neurali ricorrenti (RNN) di tipo Long Short-Term Memory (LSTM), che rappresentano una delle ultime evoluzioni nel campo del deep learning. La seconda parte, invece, riguarderà il progetto vero e proprio. Verrà impiegato il dispositivo wearable Myo di Thalmic Labs come caso di studio, e saranno applicate nel dettaglio le tecniche basate su DTW e HMM per progettare e realizzare un framework in grado di eseguire il riconoscimento real-time di gesture. Il capitolo finale mostrerà i risultati ottenuti (fornendo anche un confronto tra le tecniche analizzate), sia per la classificazione di gesture isolate che per il riconoscimento in tempo reale.

Using Machine Learning to Enhance Software Tools for Internet Information Management

Postprint

A bidirectional relationship between executive function and health behavior : evidence, implications, and future directions

This work was supported by a grant from the UK Economic and Social Research Council (ES/L010437/1).

Setting type on a linotype machine

Inscriptions: Verso: [stamped] Photograph by Freda Leinwand. [463 West Street, Studio 229G, New York, NY 10014].

Women at work: typsetter at the linotype machine

Inscription: Verso: Women at work: miscellaneous occupations.

High school classes: boy using sewing machine

Inscription: Verso: boy using sewing machine, home economics class, Thompson Jr. High School, Syosset, New York.

Outer rotor mechanical and dynamic performance analysis for high-speed machine

Due to high-speed rotation, the problems about rotor mechanics and dynamics for outer rotor high-speed machine are more serious than conventional ones, in view of above problems the mechanical and dynamics analysis for an outer rotor high-speed permanent magnet claw pole motor are carried out. The rotor stress analytical calculation model was derived, then the stress distribution is calculated by finite element method also, which is coincided with that calculated by analytical model. In addition, the stress distribution of outer rotor yoke and PMs considering centrifugal force and temperature effect has been calculated, some influence factors on rotor stress distribution have been analyzed such as pole-arc coefficient and speed. The rotor natural frequency and critical speed were calculated by vibration mode analysis, and its dynamics characteristics influenced by gyroscope effect were analyzed based on Campbell diagram. Based on the analysis results above an outer rotor permanent magnet high-speed claw pole motor is design and verified.

Performance improvement of broadband distributed Raman amplifier using bidirectional pumping with first and dual order forward pumps

In this paper, a new bidirectional pumping scheme with dual order forward pumps is proposed. Performance is compared numerically with conventional bidirectional and backward only pumping schemes for a 70 nm bandwidth, 61.5 km distributed Raman amplifier. We demonstrate that it is possible to design a flat gain spectrum with improved noise figure and OSNR, as well as a low gain ripple (<1 dB).

Numerical modeling of gyroscopic effect in bidirectional ultrafast Erbium-doped fibre laser

We demonstrate the numerical model which allows investigation of gyroscopic effect in hybrid mode-locked bidirectional Erbium-doped fibre ring laser. The model is based on transport theory with accounting of dispersion, gain in EDFA and saturable absorption. The predictions of gyroscopic effect are also presented for the particular laser cavity.

Uni-and bidirectional hybrid mode-locked erbium-doped isolator-free fibre laser

We have investigated a hybrid mode-locked Erbium-doped fibre ring laser without optical isolator. Creating different losses in the cavity for counter-propagating pulses via net birefringence adjusting, the laser can operate in both unidirectional regimes with extinction over 22 dB, as well as can establish stable bidirectional generation.

Kerr-induced nonlinearity reduction in coherent optical OFDM by low complexity support vector machine regression-based equalization

We experimentally demonstrate 7-dB reduction of nonlinearity penalty in 40-Gb/s CO-OFDM at 2000-km using support vector machine regression-based equalization. Simulation in WDM-CO-OFDM shows up to 12-dB enhancement in Q-factor compared to linear equalization.

Machine Learning-based Techniques to Address Spectral Distortions in Photon Counting X-ray Computed Tomography

Spectral CT using a photon counting x-ray detector (PCXD) shows great potential for measuring material composition based on energy dependent x-ray attenuation. Spectral CT is especially suited for imaging with K-edge contrast agents to address the otherwise limited contrast in soft tissues. We have developed a micro-CT system based on a PCXD. This system enables full spectrum CT in which the energy thresholds of the PCXD are swept to sample the full energy spectrum for each detector element and projection angle. Measurements provided by the PCXD, however, are distorted due to undesirable physical eects in the detector and are very noisy due to photon starvation. In this work, we proposed two methods based on machine learning to address the spectral distortion issue and to improve the material decomposition. This rst approach is to model distortions using an articial neural network (ANN) and compensate for the distortion in a statistical reconstruction. The second approach is to directly correct for the distortion in the projections. Both technique can be done as a calibration process where the neural network can be trained using 3D printed phantoms data to learn the distortion model or the correction model of the spectral distortion. This replaces the need for synchrotron measurements required in conventional technique to derive the distortion model parametrically which could be costly and time consuming. The results demonstrate experimental feasibility and potential advantages of ANN-based distortion modeling and correction for more accurate K-edge imaging with a PCXD. Given the computational eciency with which the ANN can be applied to projection data, the proposed scheme can be readily integrated into existing CT reconstruction pipelines.

BIDIRECTIONAL AC-DC CONVERTERS USING ONE CYCLE CONTROL (OCC) FOR ELECTRIC VEHICLES

The electric vehicle (EV) market has seen a rapid growth in the recent past. With an increase in the number of electric vehicles on road, there is an increase in the number of high capacity battery banks interfacing the grid. The battery bank of an EV, besides being the fuel tank, is also a huge energy storage unit. Presently, it is used only when the vehicle is being driven and remains idle for rest of the time, rendering it underutilized. Whereas on the other hand, there is a need of large energy storage units in the grid to filter out the fluctuations of supply and demand during a day. EVs can help bridge this gap. The EV battery bank can be used to store the excess energy from the grid to vehicle (G2V) or supply stored energy from the vehicle to grid (V2G ), when required. To let power flow happen, in both directions, a bidirectional AC-DC converter is required. This thesis concentrates on the bidirectional AC-DC converters which have a control on power flow in all four quadrants for the application of EV battery interfacing with the grid. This thesis presents a bidirectional interleaved full bridge converter topology. This helps in increasing the power processing and current handling capability of the converter which makes it suitable for the purpose of EVs. Further, the benefit of using the interleaved topology is that it increases the power density of the converter. This ensures optimization of space usage with the same power handling capacity. The proposed interleaved converter consists of two full bridges. The corresponding gate pulses of each switch, in one cell, are phase shifted by 180 degrees from those of the other cell. The proposed converter control is based on the one-cycle controller. To meet the challenge of new requirements of reactive power handling capabilities for grid connected converters, posed by the utilities, the controller is modified to make it suitable to process the reactive power. A fictitious current derived from the grid voltage is introduced in the controller, which controls the converter performance. The current references are generated using the second order generalized integrators (SOGI) and phase locked loop (PLL). A digital implementation of the proposed control ii scheme is developed and implemented using DSP hardware. The simulated and experimental results, based on the converter topology and control technique discussed here, are presented to show the performance of the proposed theory.