This paper introduces an efficient distributed data analysis framework for big data which comprises data processing at the data collecting nodes and the central server end as opposed to the existing framework that onl...
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This paper introduces an efficient distributed data analysis framework for big data which comprises data processing at the data collecting nodes and the central server end as opposed to the existing framework that only comprises data processing at the central server end. As data are being processed at the data collecting end in the proposed framework, the amount of data is reduced to be processed at the server side by the commodity computers. The proposed distributed algorithm works both in low-powered nodes such as sensors and high-speed commodity computers and also performs sequential and parallel processing based on the amount of data received at the central server. Simulation results demonstrate that the proposed distributed algorithm outperforms traditional distributed algorithms in terms of the size of data to be processed at the central server and data processing time.
The traffic demand in mobile access networks has grown substantially in recent years and is expected to continue to do so. The infrastructure of mobile access networks has to keep up with this trend and provide the da...
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ISBN:
(纸本)9781538617342
The traffic demand in mobile access networks has grown substantially in recent years and is expected to continue to do so. The infrastructure of mobile access networks has to keep up with this trend and provide the data rates to satisfy the increasing demands. To achieve this, employing coordination mechanisms is essential to use available front-/backhaul resources efficiently. By exploiting recent network softwarization approaches such as SDN and NFV, these coordination mechanisms can be handled by virtualized control applications (CAs) that can be flexibly positioned in the network. In previous work, we have introduced the Flow processing-aware Control Application Placement Problem (FCAPP) to place these CAs appropriately in the backhaul network of a mobile access network. We have also presented a heuristic approach (FlexCAPF) that places and flexibly reassigns CAs fast and efficiently. But FlexCAPF works logically centralized, which might not be possible in every application scenario. In this work, we therefore provide DistCAPA - an alternative, distributed algorithm for tackling the same tasks as FlexCAPF.
With the advance in mobile network-based systems, dynamic system has become one of the hotspots in fundamental study of distributed systems. In this article, we consider the dynamic system with frequent topology chang...
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With the advance in mobile network-based systems, dynamic system has become one of the hotspots in fundamental study of distributed systems. In this article, we consider the dynamic system with frequent topology changes arising from node mobility or other reasons, which is also referred to as “dynamic network.” With the model of dynamic network, fundamental distributed computing problems, such as information dissemination and election, can be formally studied with rigorous correctness. Our work focuses on the node counting problem in dynamic environments. We first define two new dynamicity models, named (Q, S)-distance and (Q, S)*-distance, which describe dynamic changes of information propagation time against topology changes. Based on these two models, we design three different counting algorithms which basically adopt the approach of diffusing computation. These algorithms mainly differ in communication cost due to different information collection procedures. The correctness of all the algorithms is formally proved and their performance is evaluated via both theoretical analysis and experimental simulations.
Presents a reply to comments on the paper, "Comments on ‘distributed identification of the most critical node for average consensus", (Betrand, A.), IEEE Trans. Signal Process., vol. 65, no. 5, 1265–1267, ...
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Presents a reply to comments on the paper, "Comments on ‘distributed identification of the most critical node for average consensus", (Betrand, A.), IEEE Trans. Signal Process., vol. 65, no. 5, 1265–1267, 2016.
The use of frequency hopping spread spectrum in Bluetooth significantly differentiates its networks from classical radio networks. In order to observe such differences, we studied basic algorithms, in particular neigh...
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The use of frequency hopping spread spectrum in Bluetooth significantly differentiates its networks from classical radio networks. In order to observe such differences, we studied basic algorithms, in particular neighbour discovery and message exchange algorithms. Some of the major differences are found in the procedures of device discovery and link establishment, which are studied in this paper. We focus on their impact on Bluetooth networks' distributed algorithms. We show through detailed simulation experiments that minor modifications to the Bluetooth specifications or their implementation may significantly affect the performance of well-known neighbour discovery algorithms. We then study the impact of the procedures of link establishment with the purpose of finding time-efficient implementations of communication rounds for Bluetooth networks. We study OrderedExchange and RandomExchange as both algorithms implement communication rounds in Bluetooth, but use the PAGE and PAGE SCAN states differently. Theoretical analysis shows that RandomExchange has a better time complexity, while simulation experiments show that OrderedExchange significantly outperforms RandomExchange in networks with a practical size (110 nodes and less). We use the previous results to improve the time efficiency of Bluetooth scatternet formation algorithms through the introduction of the time-efficient algorithm OrderedExchangeCMIS. We believe that the study of some other basic algorithms (such as broadcasting, spanningtree and election) will lead to a better understanding of Bluetooth networks, and as a consequence, to more efficient algorithms that fully leverage the strength of this type of network.
In this article, we develop a distributed algorithm for learning a large neural network that is deep and wide. We consider a scenario where the training dataset is not available in a single processing node, but distri...
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ISBN:
(纸本)9781538646595
In this article, we develop a distributed algorithm for learning a large neural network that is deep and wide. We consider a scenario where the training dataset is not available in a single processing node, but distributed among several nodes. We show that a recently proposed large neural network architecture called progressive learning network (PLN) can be trained in a distributed setup with centralized equivalence. That means we would get the same result if the data be available in a single node. Using a distributed convex optimization method called alternating-direction-method-of-multipliers (ADMM), we perform training of PLN in the distributed setup.
The problem of distributed learning and channel access is considered in a cognitive network with multiple secondary users. The availability statistics of the channels are initially unknown to the secondary users and a...
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The problem of distributed learning and channel access is considered in a cognitive network with multiple secondary users. The availability statistics of the channels are initially unknown to the secondary users and are estimated using sensing decisions. There is no explicit information exchange or prior agreement among the secondary users and sensing and access decisions are undertaken by them in a completely distributed manner. We propose policies for distributed learning and access which achieve order-optimal cognitive system throughput (number of successful secondary transmissions) under self play, i.e., when implemented at all the secondary users. Equivalently, our policies minimize the sum regret in distributed learning and access, which is the loss in secondary throughput due to learning and distributed access. For the scenario when the number of secondary users is known to the policy, we prove that the total regret is logarithmic in the number of transmission slots. This policy achieves order-optimal regret based on a logarithmic lower bound for regret under any uniformly-good learning and access policy. We then consider the case when the number of secondary users is fixed but unknown, and is estimated at each user through feedback. We propose a policy whose sum regret grows only slightly faster than logarithmic in the number of transmission slots.
In the information age of the 21st century, a large amount of information is collected and applied. However, due to the heterogeneity of system environment for data storage and computing, how to mine these distributed...
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In the information age of the 21st century, a large amount of information is collected and applied. However, due to the heterogeneity of system environment for data storage and computing, how to mine these distributed data sources has become a valuable research topic that attracted more and more attention. In this paper, we firstly presented the problem scenario and main challenges confronting with the problem of distributed data mining on multiple sourced heterogeneous data sets. Then, we surveyed research works related to the problem and elicited their main features on different technology domains to show current distributed solutions for different data mining algorithm categories. Finally, we reviewed in detail the research works and discussed the challenges remained in the distributed data mining problem for multiple sourced heterogeneous data sets.
In this paper we consider the distributed root-tracking problem for a sum of time-varying regression functions over a network, where each agent only has local observation and aims at finding the root of the global reg...
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In this paper we consider the distributed root-tracking problem for a sum of time-varying regression functions over a network, where each agent only has local observation and aims at finding the root of the global regression function. Since the regression functions are time-varying, we have to track the changing roots. A distributed stochastic apprximation algorithm is introducted for solving the problem and that the consensus and convergence of the estimates are proved.
In this paper, an algorithm with discontinuous characteristics is proposed to solve distributed optimization problems of sum type. The finite-time convergence of the algorithm is proved and the settling time bound is ...
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In this paper, an algorithm with discontinuous characteristics is proposed to solve distributed optimization problems of sum type. The finite-time convergence of the algorithm is proved and the settling time bound is given. Then the effectiveness of the algorithm is checked by an example.
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