Automated Dynamic Resource Provisioning and Monitoring in Virtualized Large-Scale Datacenter

Infrastructure as a Service (IaaS) is a pay-as-you go based cloud provision model which on demand outsources the physical servers, guest virtual machine (VM) instances, storage resources, and networking connections. This article reports the design and development of our proposed innovative symbiotic simulation based system to support the automated management of IaaS-based distributed virtualized data enter. To make the ideas work in practice, we have implemented an Open Stack based open source cloud computing platform. A smart benchmarking application "Cloud Rapid Experimentation and Analysis Tool (aka CBTool)" is utilized to mark the resource allocation potential of our test cloud system. The real-time benchmarking metrics of cloud are fed to a distributed multi-agent based intelligence middleware layer. To optimally control the dynamic operation of prototype data enter, we predefine some custom policies for VM provisioning and application performance profiling within a versatile cloud modeling and simulation toolkit "CloudSim". Both tools for our prototypes' implementation can scale up to thousands of VMs, therefore, our devised mechanism is highly scalable and flexibly be interpolated at large-scale level. Autonomic characteristics of agents aid in streamlining symbiosis among the simulation system and IaaS cloud in a closed feedback control loop. The practical worth and applicability of the multiagent-based technology lies in the fact that this technique is inherently scalable hence can efficiently be implemented within the complex cloud computing environment. To demonstrate the efficacy of our approach, we have deployed an intelligible lightweight representative scenario in the context of monitoring and provisioning virtual machines within the test-bed. Experimental results indicate notable improvement in the resource provision profile of virtualized data enter on incorporating our proposed strategy

WS-PGRADE workflows for cloud-based distributed simulation

Modeling and Simulation (M&S) is used for systems analysis and decision making in existing or new systems. Modeling large and complex organizations produces large-scale simulations that are difficult or impossible to run on a single computer. Such experiment execution requires high computation. Distributed Simulation (DS) allows modeling of large systems as smaller submodels that execute on different nodes of a computer network and interoperate with each other in order to compose larger systems. Furthermore, cloud computing offers on-demand access to multiple compute resources. Consequently, being able to run DS on cloud resources allows for more experimentation with large-scale simulations in a cost-effective way. However, deploying DS and in fact Cloud-based DS presents significant technical challenges. This paper proposes a framework for deploying DS on the cloud in a transparent manner using the CloudSME Simulation Platform based on WS-PGRADE workflows. A healthcare case study is used to demonstrate our approach.Special thanks to Dr. Tamas Kiss and Mr Hannu Visti, Centre for Parallel Computing, University of Westminster for supporting in this study

Building an Expert System for Evaluation of Commercial Cloud Services

Commercial Cloud services have been increasingly supplied to customers in industry. To facilitate customers' decision makings like cost-benefit analysis or Cloud provider selection, evaluation of those Cloud services are becoming more and more crucial. However, compared with evaluation of traditional computing systems, more challenges will inevitably appear when evaluating rapidly-changing and user-uncontrollable commercial Cloud services. This paper proposes an expert system for Cloud evaluation that addresses emerging evaluation challenges in the context of Cloud Computing. Based on the knowledge and data accumulated by exploring the existing evaluation work, this expert system has been conceptually validated to be able to give suggestions and guidelines for implementing new evaluation experiments. As such, users can conveniently obtain evaluation experiences by using this expert system, which is essentially able to make existing efforts in Cloud services evaluation reusable and sustainable.Comment: 8 page, Proceedings of the 2012 International Conference on Cloud and Service Computing (CSC 2012), pp. 168-175, Shanghai, China, November 22-24, 201

Dust in the Photospheric Environment: Unified Cloudy Models of M, L, and T Dwarfs

We address the problem of how dust forms and how it could be sustained in the static photospheres of cool dwarfs for a long time. In the cool and dense gas, dust forms easily at the condensation temperature, T_cond, and the dust can be in detailed balance with the ambient gas so long as it remains smaller than the critical radius, r_cr. However, dust will grow larger and segregate from the gas when it will be larger than r_cr somewhere at the lower temperature, which we refer to as the critical temperature, T_cr. Then, the large dust grains will precipitate below the photosphere and only the small dust grains in the region of T_cr < T < T_cond can be sustained in the photosphere. Thus a dust cloud is formed. Incorporating the dust cloud, non-grey model photo- spheres in radiative-convective equilibrium are extended to T_eff as low as 800K. Observed colors and spectra of cool dwarfs can consistently be accounted for by a single grid of our cloudy models. This fact in turn can be regarded as supporting evidence for our basic assumption on the cloud formation.Comment: 50 pages with 14 postscript figures, to be published in Astrophys.

Atmospheres from very low-mass stars to extrasolar planets

Within the next few years, several instruments aiming at imaging extrasolar planets will see first light. In parallel, low mass planets are being searched around red dwarfs which offer more favorable conditions, both for radial velocity detection and transit studies, than solar-type stars. We review recent advancements in modeling the stellar to substellar transition. The revised solar oxygen abundances and cloud models allow to reproduce the photometric and spectroscopic properties of this transition to a degree never achieved before, but problems remain in the important M-L transition characteristic of the effective temperature range of characterizable exoplanets.Comment: submitted to Memorie della Societa Astronomica Italian

Maximum likelihood estimation of cloud height from multi-angle satellite imagery

We develop a new estimation technique for recovering depth-of-field from multiple stereo images. Depth-of-field is estimated by determining the shift in image location resulting from different camera viewpoints. When this shift is not divisible by pixel width, the multiple stereo images can be combined to form a super-resolution image. By modeling this super-resolution image as a realization of a random field, one can view the recovery of depth as a likelihood estimation problem. We apply these modeling techniques to the recovery of cloud height from multiple viewing angles provided by the MISR instrument on the Terra Satellite. Our efforts are focused on a two layer cloud ensemble where both layers are relatively planar, the bottom layer is optically thick and textured, and the top layer is optically thin. Our results demonstrate that with relative ease, we get comparable estimates to the M2 stereo matcher which is the same algorithm used in the current MISR standard product (details can be found in [IEEE Transactions on Geoscience and Remote Sensing 40 (2002) 1547--1559]). Moreover, our techniques provide the possibility of modeling all of the MISR data in a unified way for cloud height estimation. Research is underway to extend this framework for fast, quality global estimates of cloud height.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS243 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Cloud-Based Collaborative 3D Modeling to Train Engineers for the Industry 4.0

In the present study, Autodesk Fusion 360 software (which includes the A360 environment) is used to train engineering students for the demands of the industry 4.0. Fusion 360 is a tool that unifies product lifecycle management (PLM) applications and 3D-modeling software (PDLM—product design and life management). The main objective of the research is to deepen the students’ perception of the use of a PDLM application and its dependence on three categorical variables: PLM previous knowledge, individual practices and collaborative engineering perception. Therefore, a collaborative graphic simulation of an engineering project is proposed in the engineering graphics subject at the University of La Laguna with 65 engineering undergraduate students. A scale to measure the perception of the use of PDLM is designed, applied and validated. Subsequently, descriptive analyses, contingency graphical analyses and non-parametric analysis of variance are performed. The results indicate a high overall reception of this type of experience and that it helps them understand how professionals work in collaborative environments. It is concluded that it is possible to respond to the demand of the industry needs in future engineers through training programs of collaborative 3D modeling environments