In the past 20 years, there have been a lot of distributed computing technologies, such as middleware technology, grid technology, mobile agent technology, P2 P technology and recently introduced Web Service technolog...
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In the past 20 years, there have been a lot of distributed computing technologies, such as middleware technology, grid technology, mobile agent technology, P2 P technology and recently introduced Web Service technology. Each technology has been recognized to a certain extent, solving the problem of distributed computing in a specific range. But there are some unsolved problems in the existing distributed computing technology, which affect the application and popularization of the distributed computing technology. The integration of a variety of distributed computing technologies to meet the needs of distributed computing is the research direction in the future. It is also the key to the smooth development of distributed computing.
Teaching topics related to high performance computing and parallel and distributed computing in a hands-on manner is challenging, especially at introductory, undergraduate levels. There is a participation challenge du...
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ISBN:
(纸本)9781728159768
Teaching topics related to high performance computing and parallel and distributed computing in a hands-on manner is challenging, especially at introductory, undergraduate levels. There is a participation challenge due to the need to secure access to a platform on which students can learn via hands-on activities, which is not always possible. There are also pedagogic challenges. For instance, any particular platform provided to students imposes constraints on which learning objectives can be achieved. These challenges become steeper as the topics being taught target more heterogeneous, more distributed, and/or larger platforms, as needed to prepare students for using and developing Cyberinfrastructure. To address the above challenges, we have developed a set of pedagogic activities that can be integrated piecemeal in university courses, starting at freshman levels. These activities use simulation so that students can experience hands-on any relevant application and platform scenarios. This is achieved by capitalizing on the capabilities of the WRENCH and SimGrid simulation frameworks. After describing our approach and the pedagogic activities currently available, we present results from an evaluation performed in an undergraduate university course.
When Evolutionary Algorithms (EAs) are used for Artificial Neural Networks (ANNs) training, the most valuable advantage is the potential for this training to be done in parallel or even using distributed computing. Wi...
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ISBN:
(纸本)9783319734415;9783319734408
When Evolutionary Algorithms (EAs) are used for Artificial Neural Networks (ANNs) training, the most valuable advantage is the potential for this training to be done in parallel or even using distributed computing. With the capabilities of modern mobile devices, for example their use for distributed computations, they can be used much more extensively for scientific calculations. It is well known that distributed computing systems are limited by their communication bandwidth, because of network latency. In such environment some EAs are pretty suitable for distributed implementation. This is because of their high level of parallelism and relatively less intensive network communication needs. Subset of distributed computing is volunteer computing where users donate some of the computing power provided by devices under their control. This research proposes Android Live Wallpaper volunteer computing implementation of a system used for financial time series prediction. The forecasting module is organized as ANN, which is trained by hybrid combination of Backpropagation and EAs.
Motivated by mobile edge computing and wireless data centers, we study a wireless distributed computing framework where the distributed nodes exchange information over a wireless interference network. Our framework fo...
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ISBN:
(纸本)9781538647813
Motivated by mobile edge computing and wireless data centers, we study a wireless distributed computing framework where the distributed nodes exchange information over a wireless interference network. Our framework follows the structure of MapReduce. This framework consists of Map, Shuffle, and Reduce phases, where Map and Reduce are computation phases and Shuffle is a data transmission phase. In our setting, we assume that the transmission is operated over a wireless interference network. We demonstrate that, by duplicating the computation work at a cluster of distributed nodes in the Map phase, one can reduce the amount of transmission load required for the Shuffle phase. In this work, we characterize the fundamental tradeoff between computation load and communication load, under the assumption of one-shot linear schemes. The proposed scheme is based on side information cancellation and zero-forcing, and we prove that it is optimal in terms of computation-communication tradeoff. The proposed scheme outperforms the naive TDMA scheme with single node transmission at a time, as well as the coded TDMA scheme that allows coding across data, in terms of the computation-communication tradeoff.
In the avionics cloud computing environment, the resource distributed in different task area, whatever partitioned by physical of logical, need to be cooperate together to compute a task. For the avionic computing env...
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ISBN:
(纸本)9781538683033
In the avionics cloud computing environment, the resource distributed in different task area, whatever partitioned by physical of logical, need to be cooperate together to compute a task. For the avionic computing environment, a set of distributed framework are presented in this paper, which include computing model, hardware framework and software model. The framework almost contains the technical character of the classical open framework, and can directly used for avionics products manufacture.
Global digital transformation requires more productive large scale distributed systems. Such systems should meet lots of requirements, such as high availability, low latency, reliability. However, new challenges becom...
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ISBN:
(数字)9781728126920
ISBN:
(纸本)9781728126937
Global digital transformation requires more productive large scale distributed systems. Such systems should meet lots of requirements, such as high availability, low latency, reliability. However, new challenges become more and more important nowadays. One of them is energy efficiency of large scale computing systems. Many service providers prefer to use cheap commodity servers in their distributed infrastructure, what makes the problem of energy efficiency even harder because of hardware inhomogeneity. In this chapter an approach to finding balance between performance and energy efficiency requirements within inhomogeneous distributed computing environment is proposed. The main idea of the proposed approach is to use each node's individual energy consumption models in order to generate distributed system scaling patterns based on the statistical daily workload, and then adjust these patterns to match the current workload, while using PCPB scheduling strategy to optimize hardware utilization. An approach is tested using Matlab modelling. As a result of applying the proposed approach, large-scale distributed computing systems save energy while maintaining a fairly high level of performance and meeting the requirements of the service level agreement (SLA).
Fog and Edge computing provide a large pool of resources at the edge of the network that may be used for distributed computing. Fog infrastructure heterogeneity also results in complex configuration of distributed app...
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ISBN:
(纸本)9781538678992
Fog and Edge computing provide a large pool of resources at the edge of the network that may be used for distributed computing. Fog infrastructure heterogeneity also results in complex configuration of distributed applications on computing nodes. Linux containers are a mainstream technique allowing to run packaged applications and micro services. However, running applications on remote hosts owned by third parties is challenging because of untrusted operating systems and hardware maintained by third parties. To meet such challenges, we may leverage trusted execution mechanisms. In this work, we propose a model for distributed computing on Fog infrastructures using Linux containers secured by Intel's Software Guard Extensions (SGX) technology. We implement our model on a Docker and OpenSGX platform. The result is a secure and flexible approach for distributed computing on Fog infrastructures.
This paper studies the reliability of a distributed computing system where the task on a computer may be aborted if it is still not finished until a time threshold. The mission is regarded as successful if all these t...
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This paper studies the reliability of a distributed computing system where the task on a computer may be aborted if it is still not finished until a time threshold. The mission is regarded as successful if all these tasks are completed by at least one computer before a pre-specified time. An abort policy model is proposed and a multi-valued decision diagram approach is adopted to evaluate the system reliability for any given abort policy. A numerical example is provided to illustrate the applications.
Coded distributed computing introduced by Li et al. in 2015 is an efficient approach to trade computing power to reduce the communication load in general distributed computing frameworks such as MapReduce. In particul...
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ISBN:
(纸本)9781538647813
Coded distributed computing introduced by Li et al. in 2015 is an efficient approach to trade computing power to reduce the communication load in general distributed computing frameworks such as MapReduce. In particular, Li et al. show that increasing the computation load in the Map phase by a factor of r can create coded multicasting opportunities to reduce the communication load in the Reduce phase by the same factor. However, there are two major limitations in practice. First, it requires an exponentially large number of input files (data batches) when the number of computing nodes gets large. Second, it forces every s computing nodes to compute one Map function, which leads to a large number of Map functions required to achieve the promised gain. In this paper, we make an attempt to overcome these two limitations by proposing a novel coded distributed computing approach based on a combinatorial design. We demonstrate that when the number of computing nodes becomes large, 1) the proposed approach requires an exponentially less number of input files;2) the required number of Map functions is also reduced exponentially. Meanwhile, the resulting computation-communication trade-off maintains the multiplicative gain compared to conventional uncoded unicast and achieves the information theoretic lower bound asymmetrically for some system parameters.
Rapid growth of the sheer amount of genome data and intense computation become great challenges for downstream genome analytics. Efficient parallel processing and distributed computing are the two effective schemes to...
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ISBN:
(数字)9781728144849
ISBN:
(纸本)9781728144856
Rapid growth of the sheer amount of genome data and intense computation become great challenges for downstream genome analytics. Efficient parallel processing and distributed computing are the two effective schemes to address the analysis of big data. Range join is a widely used, effective, yet time-consuming operation that finds the overlap between two different sets of genome features. The current widely adopted BEDTools [6] pipeline adopts single-node binary tree approach, while the distributed GenAp scheme fails to exploit the massive parallel computation on modern throughput processors, such as GPU (Graphic Processing Unit). This paper proposes a novel distributed Parallel P-ary search (DP2) that applies novel P-ary analysis to enable high parallelism at algorithmic level, and extensively utilize multiple GPUs at system and architecture level. Efficient computation allocation is implemented to leverage the distributed computing on clusters. The proposed framework can be well integrated with current BEDTools [6] pipeline, and achieves an average of 25× speedup for the actual range-join operation when compared with Binary tree approach of GenAp and a 13× end-to-end (total execution time) speedup in comparison to ADAM.
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