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distributed processing for Multi-Relay Assisted OFDM With Index Modulation

IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2019年第2期18卷 1318-1331页

作者： Dang, Shuping Li, Jun Wen, Miaowen Mumtaz, Shahid Guangxi Huanan Commun Co Ltd R&D Ctr Nanning 530007 Peoples R China King Abdullah Univ Sci & Technol Comp Elect & Math Sci & Engn Div Thuwal 239556900 Saudi Arabia Guangzhou Univ Sch Mech & Elect Engn Guangzhou 510006 Guangdong Peoples R China South China Univ Technol Sch Elect & Informat Engn Guangzhou 510641 Guangdong Peoples R China Inst Telecomunicacoes Campus Univ Santiago P-3810193 Aveiro Portugal

Orthogonal frequency-division multiplexing with index modulation (OFDM-IM) has become a high-profile modulation scheme for the fifth-generation (5G) wireless communications and has thus been extended to multi-hop scenarios in order to improve the network coverage and energy efficiency. However, the extension of OFDM-IM to multi-relay cooperative networks is not trivial, since it is required that a complete OFDM block should be received and decoded as an entity in one node. This requirement prevents the employment of multiple relays to forward fragmented OFDM blocks on individual subcarriers. In this regard, we propose a distributed processing scheme for multi-relay assisted OFDM-IM, by which multiple relays are selected to forward signals in a per-subcarrier manner to provide optimal error performance for two-hop decode-and-forward (DF) OFDM-IM systems. Specifically, a single selected relay only needs to decode partial information carried on certain active subcarriers and forward just as for traditional OFDM systems without IM. After receiving all signals on active subcarriers forwarded by different relays, the destination can reconstruct the complete OFDM block and retrieve the full information. We analyze the average block error rate and modulation capacity of the two-hop OFDM-IM system employing the proposed distributed DF protocol and verify the analysis by numerical simulations.

关键词： Orthogonal frequency-division multiplexing with index modulation (OFDM-IM) distributed system distributed processing multi-carrier relay selection decode-and-forward (DF) relaying

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distributed Hypergraph processing Using Intersection Graphs

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2022年第7期34卷 3182-3195页

作者： Gu, Yu Yu, Kaiqiang Song, Zhen Qi, Jianzhong Wang, Zhigang Yu, Ge Zhang, Rui Northeastern Univ Dept Comp Sci Shenyang 110819 Peoples R China Univ Melbourne Sch Comp & Informat Syst Parkville Vic 3010 Australia Ocean Univ China Coll Informat Sci & Engn Qingdao 266100 Shandong Peoples R China

The advent of online applications such as social networks has led to an unprecedented scale of data and complex relationships among data. Hypergraphs are introduced to represent complex relationships that may involve more than two entities. A hypergraph is a generalized form of a graph, where edges are generalized to hyperedges. Each hyperedge may consist of any number of vertices. The flexibility of hyperedges also brings challenges in distributed hypergraph processing. In particular, a hypergraph is more difficult to be partitioned and distributed among k workers with balanced partitions. In this paper, we propose to convert a hypergraph into an intersection graph before partitioning by leveraging the inherent shared relationships among hypergraphs. We explore the intersection graph construction method and the corresponding partition strategy which can achieve the goal of evenly distributing vertices and hyperedges across workers, while yielding a significant communication reduction. We also design a distributed processing framework named Hyraph that can directly run hypergraph analysis algorithms on our intersection graphs. Experimental results on real datasets confirm the effectiveness of our techniques and the efficiency of the Hyraph framework.

关键词： Hypergraphs shared relationships intersection graphs distributed processing graph processing

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The Future of GNU Radio: Heterogeneous Computing, distributed processing, and Scheduler-as-a-Plugin

The Future of GNU Radio: Heterogeneous Computing, Distribute...

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MILCOM, Military Communications Conference

作者： Joshua Morman Marc Lichtman Marcus Müller Peraton Labs Red Bank NJ Microsoft Reston VA Karlsruhe Institute of Technology Karlsruhe Germany

The next major iteration of GNU Radio - version 4.0 has been under development for the past 2 years. GNU Radio 4.0 represents more than an incremental improvement; it uses an entirely new architecture with an emphasis on heterogeneous computing, scheduler-as-a-plugin, and distributed signal processing to take advantage of cloud computing. In this paper we go over the weaknesses and limitations of GNU Radio version 3, and how they are being addressed as part of 4.0. Using preliminary results showing various forms of processing gain, we demonstrate why re-architecting GNU Radio is necessary to provide a software defined radio framework suitable for the complex workflows of future applications. We outline the vision that is driving the restructuring of the already powerful GNU Radio framework and summarize what is left before 4.0 is released.

关键词： Military communication distributed processing Cloud computing Computer architecture Signal processing Heterogeneous networks Software

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6th IEEE Workshop on Parallel and distributed processing for Computational Social Systems (ParSocial 2022)

6th IEEE Workshop on Parallel and Distributed Processing for...

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IEEE International Symposium on Parallel and distributed processing Workshops and Phd Forum (IPDPSW)

作者： John Korah Eunice E. Santos

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

Welcome to the Sixth IEEE Workshop on Parallel and distributed processing for Computational Social Systems (ParSocial 2022). This year the workshop highlights novel algorithms and models that leverage parallel computing with applications in social network and social media analysis. The first set of papers focus on the key individual identification problem in social network analysis. The paper by Vandromme et al entitled “Efficient Parallel PageRank Algorithm for Network Analysis” proposes a more efficient parallel algorithm for PageRank that has been shown to improve the time complexity by a factor of two. In a similar vein, the paper by Sahu et al entitled “Dynamic Batch Parallel Algorithms for Updating PageRank” proposes two parallel algorithms for recomputing PageRank of nodes in a dynamic social network that can scale across various architectures. A related research problem is identifying opinion leaders that can improve information dissemination within communities. The paper entitled “Effect of Community-based Opinion Leaders on Guideline Dissemination in Large-Scale Physician Networks” by Murugappan et al, focuses on the problem of the dissemination of medical guidelines. The authors propose a culturally infused agent based model to analyze the effectiveness of various opinion leader selection strategies and the tradeoffs between the reach and rate of spread of medical guideline information. The next set of papers focus on social media analysis. Systems for large scale ingestion of social media data sets can support a wide range of research problems in computational social systems. A step in this direction is taken by authors Huber et al, who have proposed a parallel system for large scale processing of Reddit data in their paper entitled “A Streaming System for Large-scale Mining of Reddit Data”. On the other hand, authors Abeysinghe et al in their short research paper entitled “Unsupervised User Stance Detection on Tweets Against Web Articles Using Sentence

关键词： Social networking (online) Conferences distributed processing Parallel algorithms Heuristic algorithms Guidelines Analytical models

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Optimized distributed processing in a Vehicular Cloud Architecture 22

Optimized Distributed Processing in a Vehicular Cloud Archit...

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22nd International Conference on Transparent Optical Networks (ICTON)

作者： Behbehani, Fatemah S. Musa, Mohamed Elgorashi, Taisir Elmirghani, J. M. H. Univ Leeds Sch Elect & Elect Engn Leeds LS2 9JT W Yorkshire England

ISBN: (纸本)9781728184234

The introduction of cloud data centres has opened new possibilities for the storage and processing of data, augmenting the limited capabilities of peripheral devices. Large data centres tend to be located away from the end users, which increases latency and power consumption in the interconnecting networks. These limitations led to the introduction of edge processing where small-distributed data centres or fog units are located at the edge of the network close to the end user. Vehicles can have substantial processing capabilities, often un-used, in their on-board-units (OBUs). These can be used to augment the network edge processing capabilities. In this paper, we extend our previous work and develop a mixed integer linear programming (MILP) formulation that optimizes the allocation of networking and processing resources to minimize power consumption. Our edge processing architecture includes vehicular processing nodes, edge processing and cloud infrastructure. Furthermore, in this paper our optimization formulation includes delay. Compared to power minimization, our new formulation reduces delay significantly, while resulting in a very limited increase in power consumption.

关键词： distributed processing energy consumption delay edge nodes vehicular networks

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distributed processing Middleware on Mesh Network for Connectivity Challenged Environments

Distributed Processing Middleware on Mesh Network for Connec...

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IEEE International Conference on Pervasive Computing and Communications (PerCom)

作者： Talusan, Jose Paolo Nara Inst Sci & Technol Nara Japan

ISBN: (纸本)9781538691519

The growth of pervasive computing, such as Internet of Things, increase the number of devices and the amount of context information being generated. Existing frameworks such as Cloud computing simply utilize these IoT devices as end-to-end network gateways. Paradigms such as Edge and Fog computing while able to minimize latency by bringing computing devices nearer to the data source, fail to consider the utilization of computation resource of these IoT devices. For connectivity challenged environments such as rural or disaster areas, cloud and edge/fog systems are not as accessible and devices are limited to wireless sensor networks and cheap commodity computing nodes. The goal of our research is to deliver pervasive computing to these environments by developing a middleware for heterogeneous IoT devices. Utilizing the middleware enables IoT devices to perform distributed machine information processing on available sensor nodes, without Cloud-based computing resources.

关键词： Middleware Cloud computing Performance evaluation Wireless mesh networks Pervasive computing distributed processing distributed databases

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In-Memory Indexed Caching for distributed Data processing 36

In-Memory Indexed Caching for Distributed Data Processing

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36th IEEE International Parallel and distributed processing Symposium (IEEE IPDPS)

作者： Uta, Alexandru Ghit, Bogdan Dave, Ankur Rellermeyer, Jan Boncz, Peter Leiden Univ LIACS Leiden Netherlands Databricks San Francisco CA USA Univ Calif Berkeley Berkeley CA USA Delft Univ Technol Delft Netherlands CWI Amsterdam Netherlands

ISBN: (纸本)9781665481069

Powerful abstractions such as dataframes are only as efficient as their underlying runtime system. The de-facto distributed data processing framework, Apache Spark, is poorly suited for the modern cloud-based data-science workloads due to its outdated assumptions: static datasets analyzed using coarse-grained transformations. In this paper, we introduce the Indexed DataFrame, an in-memory cache that supports a dataframe abstraction which incorporates indexing capabilities to support fast lookup and join operations. Moreover, it supports appends with multi-version concurrency control. We implement the Indexed DataFrame as a lightweight, standalone library which can be integrated with minimum effort in existing Spark programs. We analyze the performance of the Indexed DataFrame in cluster and cloud deployments with real-world datasets and benchmarks using both Apache Spark and Databricks Runtime. In our evaluation, we show that the Indexed DataFrame significantly speeds-up query execution when compared to a nonindexed dataframe, incurring modest memory overhead.

关键词： distributed processing Runtime Data analysis Cluster computing Relational databases Libraries Concurrency control

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Guest Editorial Special Issue on Parallel and distributed processing for Computational Social Systems

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2019年第1期6卷 176-177页

作者： Santos, Eunice E. Korah, John IIT Dept Comp Sci Chicago IL 60616 USA

Great strides have been made in computational methods for social systems. However, real-world modeling problems require novel computational techniques to support modeling and analyses with time and resource constraints. The emergence of parallel architectures, in the form of multicore/manycore processors, and distributed platforms, have led to the formulation of new approaches for large-scale information processing, modeling and simulation, and new tools for analysis. Due to the inherent interdisciplinary nature of the research in computational social systems, techniques that utilize parallel and distributed algorithms, systems, and methodologies continue to evolve in diverse disciplines. This special issue provides a platform to bring together these researchers to showcase innovative research in computational social systems that leverage the emerging trends in parallel and distributed processing, algorithms, computational modeling, and high-performance computing. The call for papers was issued in 2017, with the submission deadline of December 29, *** received a number of strong submissions. The papers went through a rigorous review process and six high-quality papers were selected. The special issue papers are distributed across multiple issues of the TRANSACTIONS, specifically the last two issues of 2018 and this issue. We provide brief descriptions of the papers below.

关键词： Special issues and sections Parallel processing distributed processing Computational modeling Multicore processing Program processors Sociotechnical systems

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Graph Filter Design for distributed Network processing: A Comparison between Adaptive Algorithms 10

Graph Filter Design for Distributed Network Processing: A Co...

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10th Conference of the Sensor-Signal-processing-for-Defence (SSPD)

作者： Alinaghi, Atiyeh Weiss, Stephan Stankovic, Vladimir Proudler, Ian Univ Strathclyde Dept Elect & Elect Engn Glasgow Scotland

ISBN: (纸本)9781665433143

Graph filters (GFs) have attracted great interest since they can be directly implemented in a diffused way. Thus it is interesting to investigate GFs to implement signal processing operations in a distributed manner. However, in most GF models, the input signals are assumed to be time-invariant, static, or change at a very low rate. In addition to that, the GF coefficients are usually set to be node-invariant, i.e. the same for all the nodes. Yet, in general, the input signals may evolve with time and the underlying GF may have parameters dependent on the nodes. Therefore, in this paper, we consider dynamic input signals and both types of GF coefficients, node-variant, i.e. vary on different nodes, and node-invariant. Then, we apply LMS and RLS algorithms for GF design, along with two others called adapt-then-combine (ATC) and combined RLS (CRLS) to estimate the GF coefficients. We study and compare the performance of the algorithms and show that in the case of node-invariant GF coefficients, CRLS gives the best performance with lowest mean-square-displacement (MSD), whereas, for node-variant case, RLS represents the best results. The effect of bias in the input signal has also been examined.

关键词： Graph Signal processing Graph Filtering distributed processing Adaptive algorithms

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Evaluating Performance of In-Situ distributed processing on IoT Devices by Developing a Workspace Context Recognition Service

Evaluating Performance of In-Situ Distributed Processing on ...

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IEEE International Conference on Pervasive Computing and Communications (PerCom)

作者： Talusan, Jose Paolo Tiausas, Francis Stirapongsasuti, Sopicha Nakamura, Yugo Mizumoto, Teruhiro Yasumoto, Keiichi Nara Inst Sci & Technol Nara Japan

ISBN: (纸本)9781538691519

With the number of IoT devices expected to exceed 50 billion in 2023, edge and fog computing paradigms are beginning to attract attention as a way to process the massive amounts of raw data being generated. However, these paradigms do not consider the processing capabilities of the existing commodity IoT devices in the wild. In order to solve this challenge, we are developing a new middleware platform called IFoT, which processes various sensor data while considering Quality of Service (QoS) by utilizing the computational resources of heterogeneous IoT devices within an area. This allows smart services to be created and processed in parallel by various IoT devices. In this paper, we show the effectiveness of the IFoT based approach of constructing services. We designed and implemented a workspace context recognition service, utilizing environmental sensor data processed in a distributed manner according to the IFoT framework. We evaluate the QoS of IFoT middle-ware and its feasibility when used on commodity devices such as the Raspberry Pi, through the service.

关键词： Edge Computing Internet of Things Middle-ware distributed processing Activity recognition

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