The traditional centralized data processing model represented by cloud computing cannot meet the data processing requirements that are gradually tending to the edge. Therefore, a new distributed computing model coordi...
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ISBN:
(纸本)9781728195056
The traditional centralized data processing model represented by cloud computing cannot meet the data processing requirements that are gradually tending to the edge. Therefore, a new distributed computing model coordinated by the end devices, edges and cloud has become the main development direction. However, artificial intelligence algorithms that are widely used in cloud-only approach are difficult to embed in resource constrained distributed frameworks. To address this issue, we propose Triple-partition Network, a neural network model augment with three exit points. The structure of three exit points allows to segment the traditional neural network and deploying them on the end devices, edges, and cloud. By setting up suitable exit points through the Entropy Topsis comprehensive evaluation model, part of the data can exit the network in advance to improve the efficiency of computing services. In this experiment, the classic neural networks (Alexnet, Resnet) are used to study the Triple-partition Network on a state-of-art platform and show that trained Triple-partition Network can greatly reduce the end-to-end latency by over 3x while achieving high accuracy.
SuperB is an international enterprise aiming at the construction of a very high luminosity asymmetric e+e- flavor factory. SuperB will be a partner, together with the LHC and eventually the ILC, in searching for new p...
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ISBN:
(纸本)9781424439614
SuperB is an international enterprise aiming at the construction of a very high luminosity asymmetric e+e- flavor factory. SuperB will be a partner, together with the LHC and eventually the ILC, in searching for new physics. Since the first steps of detector design, the load of computing issues in terms of Monte Carlo algorithm execution requires the exploitation of resources in a full distributedmodel. The SuperB choice is to build a Grid integrated system identifying both the consolidated and upcoming solutions to accomplish the typical HEP class experiment requirements. The model, moreover, has to be flexible, in order to deal with the rapid technological improvements and the expected long life of the experiment. The in progress design and setup of SuperB distributed computing model is presented including the complete discussion of the Grid services selected for the prototype.
In the era of smart cities huge data volumes are continuously generated and collected, thus prompting the need for efficient and distributed data mining approaches. Generalized itemset mining is an established data mi...
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ISBN:
(纸本)9781479942930
In the era of smart cities huge data volumes are continuously generated and collected, thus prompting the need for efficient and distributed data mining approaches. Generalized itemset mining is an established data mining technique, which entails the discovery of multiple-level patterns hidden in the analyzed data by exploiting analyst-provided taxonomies. Among the generalized itemsets, the most peculiar high-level patterns are those with many contrasting correlations among items at different abstraction levels. They represent misleading situations that are worth analyzing separately by experts during manual inspection. This paper proposes a novel cloud-based service, named MGI-CLOUD, to efficiently mine misleading multiple-level patterns, i.e., theMisleading Generalized Itemsets, on a distributedcomputing environment. MGI-CLOUD consists of a set of distributed MapReduce jobs running in the cloud. As a case study, the system has been contextualized in a real-life scenario, i.e., the analysis of traffic law infractions committed in a smart city environment. The experiments, performed on real datasets, demonstrate the efficiency and effectiveness of MGI-CLOUD.
As a new distributed computing model, crowdsourcing lets people leverage the crowd's intelligence and wisdom toward solving problems. This article proposes a framework for characterizing various dimensions of qual...
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As a new distributed computing model, crowdsourcing lets people leverage the crowd's intelligence and wisdom toward solving problems. This article proposes a framework for characterizing various dimensions of quality control in crowdsourcing systems, a critical issue. The authors briefly review existing quality-control approaches, identify open issues, and look to future research directions.
What can and cannot be computed in a distributed system is a complex function of the system's communication model, timing model, and failure model. Considering a canonical distributed system model, where processes...
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What can and cannot be computed in a distributed system is a complex function of the system's communication model, timing model, and failure model. Considering a canonical distributed system model, where processes execute asynchronously, communicate by reading and writing shared memory, and fail by crashing, this paper surveys important results about computability, and explains the fundamental role that topology plays in the distributed computability theory. The paper also considers different types of additional assumptions that allow impossibility results to be circumvented. These assumptions are known under the names failure detectors and adversaries. Finally, it presents a powerful simulation technique (known under the name BG simulation), which allows to show that, from a computability point of view, t-resilience is not different from wait-freedom. When pieced together, the aim of all the concepts, notions, models, and algorithms presented in the paper, is to provide the reader with a synthetic view of important results on the distributed asynchronous read/write shared-memory model, its power and its limits. (C) 2013 Elsevier B.V. All rights reserved.
What can and cannot be computed in a distributed system is a complex function of the system's communication model, timing model, and failure model. Considering a canonical distributed system model, where processes...
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What can and cannot be computed in a distributed system is a complex function of the system's communication model, timing model, and failure model. Considering a canonical distributed system model, where processes execute asynchronously, communicate by reading and writing shared memory, and fail by crashing, this paper surveys important results about computability, and explains the fundamental role that topology plays in the distributed computability theory. The paper also considers different types of additional assumptions that allow impossibility results to be circumvented. These assumptions are known under the names failure detectors and adversaries. Finally, it presents a powerful simulation technique (known under the name BG simulation), which allows to show that, from a computability point of view, t-resilience is not different from wait-freedom. When pieced together, the aim of all the concepts, notions, models, and algorithms presented in the paper, is to provide the reader with a synthetic view of important results on the distributed asynchronous read/write shared-memory model, its power and its limits. (C) 2013 Elsevier B.V. All rights reserved.
Large volumes of data are being produced by various modern applications at an ever increasing rate. These applications range from wireless sensors networks to social networks. The automatic analysis of such huge data ...
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ISBN:
(纸本)9780769550220
Large volumes of data are being produced by various modern applications at an ever increasing rate. These applications range from wireless sensors networks to social networks. The automatic analysis of such huge data volume is a challenging task since a large amount of interesting knowledge can be extracted. Association rule mining is an exploratory data analysis method able to discover interesting and hidden correlations among data. Since this data mining process is characterized by computationally intensive tasks, efficient distributed approaches are needed to increase its scalability. This paper proposes a novel cloud-based service, named SEARUM, to efficiently mine association rules on a distributed computing model. SEARUM consists of a series of distributed MapReduce jobs run in the cloud. Each job performs a different step in the association rule mining process. As a case study, the proposed approach has been applied to the network data scenario. The experimental validation, performed on two real network datasets, shows the effectiveness and the efficiency of SEARUM in mining association rules on a distributed computing model.
We present an approach to data mining on arbitrary graph data that uses a cloud-based distributed computing model for dynamic provisioning of computing resources as the graph model grows or shrinks. Further, we introd...
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ISBN:
(纸本)9781479904051
We present an approach to data mining on arbitrary graph data that uses a cloud-based distributed computing model for dynamic provisioning of computing resources as the graph model grows or shrinks. Further, we introduce the concept of logging graph changes as a basis for calculating properties of dynamic graphs. We briefly describe queries that leverage the dynamic graph model, for instance, by using a snapshot of the original graph while an algorithm executes or adapting query results as the graph changes. To demonstrate the feasibility of our approach, we conducted an initial evaluation, which shows that our parallel computingmodel can dramatically improve load times. Raw data imported into our system is processed faster on larger compute clusters.
Due to the advent of multicore machines, shared memory distributed computing models taking into account asynchrony and process crashes are becoming more and more important. This paper visits models for these systems a...
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ISBN:
(数字)9783642222122
ISBN:
(纸本)9783642222115;9783642222122
Due to the advent of multicore machines, shared memory distributed computing models taking into account asynchrony and process crashes are becoming more and more important. This paper visits models for these systems and analyses their properties from a computability point of view. Among them, the base snapshot model and the iterated model are particularly investigated. The paper visits also several approaches that have been proposed to model failures (mainly the wait-free model and the adversary model) and gives also a look at the BG simulation. The aim of this survey is to help the reader to better understand the power and limits of distributedcomputing shared memory models.
The capability of dynamically adapting to distinct runtime conditions is an important issue when designing distributed systems where negotiated quality of service (QoS) cannot always be delivered between processes. Pr...
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The capability of dynamically adapting to distinct runtime conditions is an important issue when designing distributed systems where negotiated quality of service (QoS) cannot always be delivered between processes. Providing fault tolerance for such dynamic environments is a challenging task. Considering such a context, this paper proposes an adaptive programming model for fault-tolerant distributedcomputing, which provides upper-layer applications with process state information according to the current system synchrony ( or QoS). The underlying system model is hybrid, composed by a synchronous part ( where there are time bounds on processing speed and message delay) and an asynchronous part ( where there is no time bound). However, such a composition can vary over time, and, in particular, the system may become totally asynchronous ( e. g., when the underlying system QoS degrade) or totally synchronous. Moreover, processes are not required to share the same view of the system synchrony at a given time. To illustrate what can be done in this programming model and how to use it, the consensus problem is taken as a benchmark problem. This paper also presents an implementation of the model that relies on a negotiated quality of service ( QoS) for communication channels.
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