625 research outputs found
Distributed modular RT-systems for detector DAQ, trigger and control applications
A modular approach to development of distributed modular system architecture for detector control, data acquisition and trigger data processing is proposed. A multilevel parallel-pipeline model of data acquisition, processing and control is proposed and discussed. Multiprocessor architecture with SCI-based interconnections is proposed as good high-performance system for parallel-pipeline data processing. A network (Ethernet -100) can be used for loading, monitoring and diagnostic purposes independent of basic interconnections. The modular cPCI-based structures with high speed modular interconnections are proposed for DAQ and control applications. For distributed control RT-systems, to construct the effective (cost-performance) systems the same platform of an Intel compatible processor board should be used. The basic computer multiprocessor nodes consist of high-power PC MB (Industrial Computer Systems), which are interconnected by SCI modules and link to embedded microprocessor-based sub-systems for control applications. The required number of multiprocessor nodes should be interconnected by SCI for parallel-pipeline data processing in real time (according to the multilevel model) and link to RT-systems for embedded control. (19 refs)
MODELS AND SOLUTIONS FOR THE IMPLEMENTATION OF DISTRIBUTED SYSTEMS
Software applications may have different degrees of complexity depending on the problems they try to solve and can integrate very complex elements that bring together functionality that sometimes are competing or conflicting. We can take for example a mobile communications system. Functionalities of such a system are difficult to understand, and they add to the non-functional requirements such as the use in practice, performance, cost, durability and security. The transition from local computer networks to cover large networks that allow millions of machines around the world at speeds exceeding one gigabit per second allowed universal access to data and design of applications that require simultaneous use of computing power of several interconnected systems. The result of these technologies has enabled the evolution from centralized to distributed systems that connect a large number of computers. To enable the exploitation of the advantages of distributed systems one had developed software and communications tools that have enabled the implementation of distributed processing of complex solutions. The objective of this document is to present all the hardware, software and communication tools, closely related to the possibility of their application in integrated social and economic level as a result of globalization and the evolution of e-society. These objectives and national priorities are based on current needs and realities of Romanian society, while being consistent with the requirements of Romania's European orientation towards the knowledge society, strengthening the information society, the target goal representing the accomplishment of e-Romania, with its strategic e-government component. Achieving this objective repositions Romania and gives an advantage for sustainable growth, positive international image, rapid convergence in Europe, inclusion and strengthening areas of high competence, in line with Europe 2020, launched by the European Council in June 2010.information society, databases, distributed systems, e-society, implementation of distributed systems
RELEASE: A High-level Paradigm for Reliable Large-scale Server Software
Erlang is a functional language with a much-emulated model for building reliable distributed systems. This paper outlines the RELEASE project, and describes the progress in the first six months. The project aim is to scale the Erlangâs radical concurrency-oriented programming paradigm to build reliable general-purpose software, such as server-based systems, on massively parallel machines. Currently Erlang has inherently scalable computation and reliability models, but in practice scalability is constrained by aspects of the language and virtual machine. We are working at three levels to address these challenges: evolving the Erlang virtual machine so that it can work effectively on large scale multicore systems; evolving the language to Scalable Distributed (SD) Erlang; developing a scalable Erlang infrastructure to integrate multiple, heterogeneous clusters. We are also developing state of the art tools that allow programmers to understand the behaviour of massively parallel SD Erlang programs. We will demonstrate the effectiveness of the RELEASE approach using demonstrators and two large case studies on a Blue Gene
RELEASE: A High-level Paradigm for Reliable Large-scale Server Software
Erlang is a functional language with a much-emulated model for building reliable distributed systems. This paper outlines the RELEASE project, and describes the progress in the rst six months. The project aim is to scale the Erlang's radical concurrency-oriented programming paradigm to build reliable general-purpose software, such as server-based systems, on massively parallel machines. Currently Erlang has inherently scalable computation and reliability models, but in practice scalability is constrained by aspects of the language and virtual machine. We are working at three levels to address these challenges: evolving the Erlang virtual machine so that it can work effectively on large scale multicore systems; evolving the language to Scalable Distributed (SD) Erlang; developing a scalable Erlang infrastructure to integrate multiple, heterogeneous clusters. We are also developing state of the art tools that allow programmers to understand the behaviour of massively parallel SD Erlang programs. We will demonstrate the e ectiveness of the RELEASE approach using demonstrators and two large case studies on a Blue Gene
Characterizing and Subsetting Big Data Workloads
Big data benchmark suites must include a diversity of data and workloads to
be useful in fairly evaluating big data systems and architectures. However,
using truly comprehensive benchmarks poses great challenges for the
architecture community. First, we need to thoroughly understand the behaviors
of a variety of workloads. Second, our usual simulation-based research methods
become prohibitively expensive for big data. As big data is an emerging field,
more and more software stacks are being proposed to facilitate the development
of big data applications, which aggravates hese challenges. In this paper, we
first use Principle Component Analysis (PCA) to identify the most important
characteristics from 45 metrics to characterize big data workloads from
BigDataBench, a comprehensive big data benchmark suite. Second, we apply a
clustering technique to the principle components obtained from the PCA to
investigate the similarity among big data workloads, and we verify the
importance of including different software stacks for big data benchmarking.
Third, we select seven representative big data workloads by removing redundant
ones and release the BigDataBench simulation version, which is publicly
available from http://prof.ict.ac.cn/BigDataBench/simulatorversion/.Comment: 11 pages, 6 figures, 2014 IEEE International Symposium on Workload
Characterizatio
MAGDA: A Mobile Agent based Grid Architecture
Mobile agents mean both a technology
and a programming paradigm. They allow for a
flexible approach which can alleviate a number
of issues present in distributed and Grid-based
systems, by means of features such as migration,
cloning, messaging and other provided mechanisms.
In this paper we describe an architecture
(MAGDA â Mobile Agent based Grid Architecture)
we have designed and we are currently
developing to support programming and execution
of mobile agent based application upon Grid
systems
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