Performance models of storage contention in cloud environments

We propose simple models to predict the performance degradation of disk requests due to storage device contention in consolidated virtualized environments. Model parameters can be deduced from measurements obtained inside Virtual Machines (VMs) from a system where a single VM accesses a remote storage server. The parameterized model can then be used to predict the effect of storage contention when multiple VMs are consolidated on the same server. We first propose a trace-driven approach that evaluates a queueing network with fair share scheduling using simulation. The model parameters consider Virtual Machine Monitor level disk access optimizations and rely on a calibration technique. We further present a measurement-based approach that allows a distinct characterization of read/write performance attributes. In particular, we define simple linear prediction models for I/O request mean response times, throughputs and read/write mixes, as well as a simulation model for predicting response time distributions. We found our models to be effective in predicting such quantities across a range of synthetic and emulated application workloads. 

A Theoretical and Experimental Study of Resonance in a High Performance Engine Intake System: Part 1

The unsteady gas dynamic phenomena in engine intake systems of the type found in racecars have been examined. In particular, the resonant tuning effects, including cylinder-to-cylinder power variations, which can occur as a result of the interaction between an engine and its airbox have been considered. Frequency analysis of the output from a Virtual 4-Stroke 1D engine simulation was used to characterise the forcing function applied by an engine to an airbox. A separate computational frequency sweeping technique, which employed the CFD package FLUENT, was used to determine the natural frequencies of virtual airboxes in isolation from an engine. Using this technique, an airbox with a natural frequency at 75 Hz was designed for a Yamaha R6 4-cylinder motorcycle engine. The existence of an airbox natural frequency at 75 Hz was subsequently confirmed by an experimental frequency sweeping technique carried out on the engine test bed. A coupled 1D/3D analysis which employed the engine simulation package Virtual 4-Stroke and the CFD package FLUENT, was used to model the combined engine and airbox system. The coupled 1D/3D analysis predicted a 75 Hz resonance of the airbox at an engine speed of 9000 rpm. This frequency was the induction frequency for a single cylinder. An airbox was fabricated and tested on the engine. Static pressure was recorded at a grid of points in the airbox as the engine was swept through a speed range of 3000 to 10000 rpm. The measured engine speed corresponding to resonance in the airbox agreed well with the predicted values. There was also good correlation between the amplitude and phase of the pressure traces recorded within the airbox and the 1D/3D predictions.

Prospective, Randomized Assessment of Transfer of Training (ToT) and Transfer Effectiveness Ratio (TER) of Virtual Reality Simulation Training for Laparoscopic Skill Acquisition

OBJECTIVES:: We assessed the effectiveness of ToT from VR laparoscopic simulation training in 2 studies. In a second study, we also assessed the TER. ToT is a detectable performance improvement between equivalent groups, and TER is the observed percentage performance differences between 2 matched groups carrying out the same task but with 1 group pretrained on VR simulation. Concordance between simulated and in-vivo procedure performance was also assessed. DESIGN:: Prospective, randomized, and blinded. PARTICIPANTS:: In Study 1, experienced laparoscopic surgeons (n = 195) and in Study 2 laparoscopic novices (n = 30) were randomized to either train on VR simulation before completing an equivalent real-world task or complete the real-world task only. RESULTS:: Experienced laparoscopic surgeons and novices who trained on the simulator performed significantly better than their controls, thus demonstrating ToT. Their performance showed a TER between 7% and 42% from the virtual to the real tasks. Simulation training impacted most on procedural error reduction in both studies (32- 42%). The correlation observed between the VR and real-world task performance was r > 0·96 (Study 2). CONCLUSIONS:: VR simulation training offers a powerful and effective platform for training safer skills.

Exploring Virtual-Channel architecture in FPGA based Networks-on-Chip

A novel Networks-on-Chip (NoC) router architecture specified for FPGA based implementation with configurable Virtual-Channel (VC) is presented. Each pipeline stage of the proposed architecture has been optimized so that low packet propagation latency and reduced hardware overhead can be achieved. The proposed architecture enables high performance and cost effective VC NoC based on-chip system interconnects to be deployed on FPGA.

IO Performance Prediction in Consolidated Virtualized Environments

We propose a trace-driven approach to predict the performance degradation of disk request response times due to storage device contention in consolidated virtualized environments. Our performance model evaluates a queueing network with fair share scheduling using trace-driven simulation. The model parameters can be deduced from measurements obtained inside Virtual Machines (VMs) from a system where a single VM accesses a remote storage server. The parameterized model can then be used to predict the effect of storage contention when multiple VMs are consolidated on the same virtualized server. The model parameter estimation relies on a search technique that tries to estimate the splitting and merging of blocks at the the Virtual Machine Monitor (VMM) level in the case of multiple competing VMs. Simulation experiments based on traces of the Postmark and FFSB disk benchmarks show that our model is able to accurately predict the impact of workload consolidation on VM disk IO response times.

A Theoretical and Experimental Study of Resonance in a High Performance Engine Intake System: Part 2

The unsteady gas dynamic phenomena in a racecar airbox have been examined, and resonant tuning effects have been considered. A coupled 1D/3D analysis, using the engine simulation package Virtual 4-Stroke and the CFD package FLUENT, was used to model the engine and airbox. The models were experimentally validated. An airbox was designed with a natural frequency in the region of 75 Hz. A coupled 1D/3D analysis of the airbox and a Yamaha R6 4 cylinder engine predicted resonance at the single-cylinder induction frequency; 75 Hz at an engine speed of 9000 rpm.

MSC-clustering and forward stepwise regression for virtual metrology in highly correlated input spaces

Increasingly semiconductor manufacturers are exploring opportunities for virtual metrology (VM) enabled process monitoring and control as a means of reducing non-value added metrology and achieving ever more demanding wafer fabrication tolerances. However, developing robust, reliable and interpretable VM models can be very challenging due to the highly correlated input space often associated with the underpinning data sets. A particularly pertinent example is etch rate prediction of plasma etch processes from multichannel optical emission spectroscopy data. This paper proposes a novel input-clustering based forward stepwise regression methodology for VM model building in such highly correlated input spaces. Max Separation Clustering (MSC) is employed as a pre-processing step to identify a reduced srt of well-conditioned, representative variables that can then be used as inputs to state-of-the-art model building techniques such as Forward Selection Regression (FSR), Ridge regression, LASSO and Forward Selection Ridge Regression (FCRR). The methodology is validated on a benchmark semiconductor plasma etch dataset and the results obtained are compared with those achieved when the state-of-art approaches are applied directly to the data without the MSC pre-processing step. Significant performance improvements are observed when MSC is combined with FSR (13%) and FSRR (8.5%), but not with Ridge Regression (-1%) or LASSO (-32%). The optimal VM results are obtained using the MSC-FSR and MSC-FSRR generated models. © 2012 IEEE.

On Regression Methods for Virtual Metrology in Semiconductor Manufacturing

Virtual metrology (VM) aims to predict metrology values using sensor data from production equipment and physical metrology values of preceding samples. VM is a promising technology for the semiconductor manufacturing industry as it can reduce the frequency of in-line metrology operations and provide supportive information for other operations such as fault detection, predictive maintenance and run-to-run control. The prediction models for VM can be from a large variety of linear and nonlinear regression methods and the selection of a proper regression method for a specific VM problem is not straightforward, especially when the candidate predictor set is of high dimension, correlated and noisy. Using process data from a benchmark semiconductor manufacturing process, this paper evaluates the performance of four typical regression methods for VM: multiple linear regression (MLR), least absolute shrinkage and selection operator (LASSO), neural networks (NN) and Gaussian process regression (GPR). It is observed that GPR performs the best among the four methods and that, remarkably, the performance of linear regression approaches that of GPR as the subset of selected input variables is increased. The observed competitiveness of high-dimensional linear regression models, which does not hold true in general, is explained in the context of extreme learning machines and functional link neural networks.

Building Information Modelling Energy Performance Assessment on Domestic Dwellings: A Comparative Study

Building Information Modelling (BIM) is growing in pace, not only in design and construction stages, but also in the analysis of facilities throughout their life cycle. With this continued growth and utilisation of BIM processes, comes the possibility to adopt such procedures, to accurately measure the energy efficiency of buildings, to accurately estimate their energy usage. To this end, the aim of this research is to investigate if the introduction of BIM Energy Performance Assessment in the form of software analysis, provides accurate results, when compared with actual energy consumption recorded. Through selective sampling, three domestic case studies are scrutinised, with baseline figures taken from existing energy providers, the results scrutinised and compared with calculations provided from two separate BIM energy analysis software packages. Of the numerous software packages available, criterion sampling is used to select two of the most prominent platforms available on the market today. The two packages selected for scrutiny are Integrated Environmental Solutions - Virtual Environment (IES-VE) and Green Building Studio (GBS). The results indicate that IES-VE estimated the energy use in region of ±8% in two out of three case studies while GBS estimated usage approximately ±5%. The findings indicate that the introduction of BIM energy performance assessment, using proprietary software analysis, is a viable alternative to manual calculations of building energy use, mainly due to the accuracy and speed of assessing, even the most complex models. Given the surge in accurate and detailed BIM models and the importance placed on the continued monitoring and control of buildings energy use within today’s environmentally conscious society, this provides an alternative means by which to accurately assess a buildings energy usage, in a quick and cost effective manner.

Movement and Perceptual Strategies to Intercept Virtual Sound Sources

To intercept a moving object, one needs to be in the right place at the right time. In order to do this, it is necessary to pick up and use perceptual information that specifies the time to arrival of an object at an interception point. In the present study, we examined the ability to intercept a laterally moving virtual sound object by controlling the displacement of a sliding handle and tested whether and how the interaural time difference (ITD) could be the main source of perceptual information for successfully intercepting the virtual object. The results revealed that in order to accomplish the task, one might need to vary the duration of the movement, control the hand velocity and time to reach the peak velocity (speed coupling), while the adjustment of movement initiation did not facilitate performance. Furthermore, the overall performance was more successful when subjects employed a time-to-contact (tau) coupling strategy. This result shows that prospective information is available in sound for guiding goal-directed actions.

Extreme learning machines for virtual metrology and etch rate prediction

Virtual metrology (VM) aims to predict metrology values using sensor data from production equipment and physical metrology values of preceding samples. VM is a promising technology for the semiconductor manufacturing industry as it can reduce the frequency of in-line metrology operations and provide supportive information for other operations such as fault detection, predictive maintenance and run-to-run control. Methods with minimal user intervention are required to perform VM in a real-time industrial process. In this paper we propose extreme learning machines (ELM) as a competitive alternative to popular methods like lasso and ridge regression for developing VM models. In addition, we propose a new way to choose the hidden layer weights of ELMs that leads to an improvement in its prediction performance.

Understanding perception and action in sport:How can virtual reality technology help?

Although technology can facilitate improvements in performance by allowing us to understand, monitor and evaluate performance, improvements must ultimately come from within the athlete. The first part of this article will focus on understanding how perception and action relate to performance from two different theoretical viewpoints. The first will be predominantly a cognitive or indirect approach that suggests that expertise and decision-making processes are mediated by athletes accruing large knowledge bases that are built up through practice and experience. The second, and alternative approach, will advocate a more 'direct' solution, where the athlete learns to 'tune' into the relevant information that is embedded in their relationship with the surrounding environment and unfolding action. The second part of the article will attempt to show how emerging virtual reality technology is revealing new evidence that helps us understand elite performance. Possibilities of how new types of training could be developed from this technology will also be discussed. © 2014 Crown Copyright.

Improvisation, Indeterminacy and Game-Mediated Participation in Network Music Performance

This chapter focuses on the relationship between improvisation and indeterminacy. We discuss the two practices by referring to play theory and game studies and situate it in recent network music performance. We will develop a parallel with game theory in which indeterminacy is seen as a way of articulating situations where structural decisions are left to the discernment of the performers and discuss improvisation as a method of play. The improvisation-indeterminacy relationship is discussed in the context of network music performance, which employs digital networks in the exchange of data between performers and hence relies on topological structures with varying degrees of openness and flexibility. Artists such as Max Neuhaus and The League of Automatic Music Composers initiated the development of a multitude of practices and technologies exploring the network as an environment for music making. Even though the technologies behind “the network” have shifted dramatically since Neuhaus’ use of radio in the 1960’s, a preoccupation with distribution and sharing of artistic agency has remained at the centre of networked practices. Gollo Föllmer, after undertaking an extensive review of network music initiatives, produced a typology that comprises categories as diverse as remix lists, sound toys, real/virtual space installations and network performances. For Föllmer, “the term ‘Net music’ comprises all formal and stylistic kinds of music upon which the specifics of electronic networks leave considerable traces, whereby the electronic networks strongly influence the process of musical production, the musical aesthetic, or the way music is received” (2005: 185).

Cloud benchmarking for performance

How can applications be deployed on the cloud to achieve maximum performance? This question has become significant and challenging with the availability of a wide variety of Virtual Machines (VMs) with different performance capabilities in the cloud. The above question is addressed by proposing a six step benchmarking methodology in which a user provides a set of four weights that indicate how important each of the following groups: memory, processor, computation and storage are to the application that needs to be executed on the cloud. The weights along with cloud benchmarking data are used to generate a ranking of VMs that can maximise performance of the application. The rankings are validated through an empirical analysis using two case study applications, the first is a financial risk application and the second is a molecular dynamics simulation, which are both representative of workloads that can benefit from execution on the cloud. Both case studies validate the feasibility of the methodology and highlight that maximum performance can be achieved on the cloud by selecting the top ranked VMs produced by the methodology.