45,842 research outputs found
Grid Infrastructure for Domain Decomposition Methods in Computational ElectroMagnetics
The accurate and efficient solution of Maxwell's equation is the problem addressed by the scientific discipline called Computational ElectroMagnetics (CEM). Many macroscopic phenomena in a great number of fields are governed by this set of differential equations: electronic, geophysics, medical and biomedical technologies, virtual EM prototyping, besides the traditional antenna and propagation applications. Therefore, many efforts are focussed on the development of new and more efficient approach to solve Maxwell's equation. The interest in CEM applications is growing on. Several problems, hard to figure out few years ago, can now be easily addressed thanks to the reliability and flexibility of new technologies, together with the increased computational power. This technology evolution opens the possibility to address large and complex tasks. Many of these applications aim to simulate the electromagnetic behavior, for example in terms of input impedance and radiation pattern in antenna problems, or Radar Cross Section for scattering applications. Instead, problems, which solution requires high accuracy, need to implement full wave analysis techniques, e.g., virtual prototyping context, where the objective is to obtain reliable simulations in order to minimize measurement number, and as consequence their cost. Besides, other tasks require the analysis of complete structures (that include an high number of details) by directly simulating a CAD Model. This approach allows to relieve researcher of the burden of removing useless details, while maintaining the original complexity and taking into account all details. Unfortunately, this reduction implies: (a) high computational effort, due to the increased number of degrees of freedom, and (b) worsening of spectral properties of the linear system during complex analysis. The above considerations underline the needs to identify appropriate information technologies that ease solution achievement and fasten required elaborations. The authors analysis and expertise infer that Grid Computing techniques can be very useful to these purposes. Grids appear mainly in high performance computing environments. In this context, hundreds of off-the-shelf nodes are linked together and work in parallel to solve problems, that, previously, could be addressed sequentially or by using supercomputers. Grid Computing is a technique developed to elaborate enormous amounts of data and enables large-scale resource sharing to solve problem by exploiting distributed scenarios. The main advantage of Grid is due to parallel computing, indeed if a problem can be split in smaller tasks, that can be executed independently, its solution calculation fasten up considerably. To exploit this advantage, it is necessary to identify a technique able to split original electromagnetic task into a set of smaller subproblems. The Domain Decomposition (DD) technique, based on the block generation algorithm introduced in Matekovits et al. (2007) and Francavilla et al. (2011), perfectly addresses our requirements (see Section 3.4 for details). In this chapter, a Grid Computing infrastructure is presented. This architecture allows parallel block execution by distributing tasks to nodes that belong to the Grid. The set of nodes is composed by physical machines and virtualized ones. This feature enables great flexibility and increase available computational power. Furthermore, the presence of virtual nodes allows a full and efficient Grid usage, indeed the presented architecture can be used by different users that run different applications
Experimental Study of Remote Job Submission and Execution on LRM through Grid Computing Mechanisms
Remote job submission and execution is fundamental requirement of distributed
computing done using Cluster computing. However, Cluster computing limits usage
within a single organization. Grid computing environment can allow use of
resources for remote job execution that are available in other organizations.
This paper discusses concepts of batch-job execution using LRM and using Grid.
The paper discusses two ways of preparing test Grid computing environment that
we use for experimental testing of concepts. This paper presents experimental
testing of remote job submission and execution mechanisms through LRM specific
way and Grid computing ways. Moreover, the paper also discusses various
problems faced while working with Grid computing environment and discusses
their trouble-shootings. The understanding and experimental testing presented
in this paper would become very useful to researchers who are new to the field
of job management in Grid.Comment: Fourth International Conference on Advanced Computing & Communication
Technologies (ACCT), 201
Global Grids and Software Toolkits: A Study of Four Grid Middleware Technologies
Grid is an infrastructure that involves the integrated and collaborative use
of computers, networks, databases and scientific instruments owned and managed
by multiple organizations. Grid applications often involve large amounts of
data and/or computing resources that require secure resource sharing across
organizational boundaries. This makes Grid application management and
deployment a complex undertaking. Grid middlewares provide users with seamless
computing ability and uniform access to resources in the heterogeneous Grid
environment. Several software toolkits and systems have been developed, most of
which are results of academic research projects, all over the world. This
chapter will focus on four of these middlewares--UNICORE, Globus, Legion and
Gridbus. It also presents our implementation of a resource broker for UNICORE
as this functionality was not supported in it. A comparison of these systems on
the basis of the architecture, implementation model and several other features
is included.Comment: 19 pages, 10 figure
Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms
The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent ādevicesā, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew ācognitive devicesā are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications
A Study to Optimize Heterogeneous Resources for Open IoT
Recently, IoT technologies have been progressed, and many sensors and
actuators are connected to networks. Previously, IoT services were developed by
vertical integration style. But now Open IoT concept has attracted attentions
which achieves various IoT services by integrating horizontal separated devices
and services. For Open IoT era, we have proposed the Tacit Computing technology
to discover the devices with necessary data for users on demand and use them
dynamically. We also implemented elemental technologies of Tacit Computing. In
this paper, we propose three layers optimizations to reduce operation cost and
improve performance of Tacit computing service, in order to make as a
continuous service of discovered devices by Tacit Computing. In optimization
process, appropriate function allocation or offloading specific functions are
calculated on device, network and cloud layer before full-scale operation.Comment: 3 pages, 1 figure, 2017 Fifth International Symposium on Computing
and Networking (CANDAR2017), Nov. 201
A Middleware for the Internet of Things
The Internet of Things (IoT) connects everyday objects including a vast array
of sensors, actuators, and smart devices, referred to as things to the
Internet, in an intelligent and pervasive fashion. This connectivity gives rise
to the possibility of using the tracking capabilities of things to impinge on
the location privacy of users. Most of the existing management and location
privacy protection solutions do not consider the low-cost and low-power
requirements of things, or, they do not account for the heterogeneity,
scalability, or autonomy of communications supported in the IoT. Moreover,
these traditional solutions do not consider the case where a user wishes to
control the granularity of the disclosed information based on the context of
their use (e.g. based on the time or the current location of the user). To fill
this gap, a middleware, referred to as the Internet of Things Management
Platform (IoT-MP) is proposed in this paper.Comment: 20 pages, International Journal of Computer Networks & Communications
(IJCNC) Vol.8, No.2, March 201
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