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A Scalable and Composable Analytics Platform for distributed Wide-Area Tracking 20

A Scalable and Composable Analytics Platform for Distributed...

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20th international conference on distributed computing and networking (ICDCN)

作者： Khochare, Aakash Simmhan, Yogesh Indian Inst Sci Dept Computat & Data Sci Bangalore Karnataka India

ISBN: (纸本)9781450360944

Smart cities are a manifestation of the Internet of Things (IoT). The push for smart cities has led to the proliferation of video cameras in public spaces [1]. London is expected to deploy 642,000 cameras by 2020. These cameras will be used primarily for surveillance of the city for urban safety. At the same time, such smart city deployments have also seen edge and fog computing paradigms gain preference over the cloud-only computing model [2]. This is due to the network proximity of the edge and fog devices to the data sources, offering lower latency access to data and lower bandwidth requirements to push high volume data, such as video streams, to the cloud. © 2019 Association for computing Machinery.

关键词： Smart city

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TaskTorrent: A Lightweight distributed Task-Based Runtime System in C++ 3

TaskTorrent: A Lightweight Distributed Task-Based Runtime Sy...

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3rd IEEE/ACM Annual Parallel Applications Workshop, Alternatives to MPI+X, PAW-ATM 2020

作者： Cambier, Leopold Qian, Yizhou Darve, Eric ICME Stanford University United States ME and ICME Stanford University United States

ISBN: (纸本)9781728154503

We present TaskTorrent, a lightweight distributed task-based runtime in C++.TaskTorrent uses a parametrized task graph to express the task DAG, and one-sided active messages to trigger remote tasks asynchronously. As a result the task DAG is completely distributed and discovered in parallel. It is a C++14 library and only depends on MPI. We explain the API and the implementation. We perform a series of benchmarks against StarPU and ScaLAPACK. Micro benchmarks show it has a minimal overhead compared to other solutions. We then apply it to two large linear algebra problems. TaskTorrent scales very well to thousands of cores, exhibiting good weak and strong scalings. © 2020 IEEE.

关键词： Linear algebra

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A distributed Decision Making Model for Cloudlets Network: A Fog to Cloud computing Approach 34th

A Distributed Decision Making Model for Cloudlets Network: A...

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34th international conference on Advanced Information networking and Applications, AINA 2020

作者： Ben Lazreg, Amel Ben Arbia, Anis Youssef, Habib University of Sousse Sousse Tunisia PRINCE Research Laboratory Sousse Tunisia Higher Institute of Computer Science and Communication Techniques Hammam Sousse Sousse Tunisia

ISBN: (纸本)9783030440404

During the last decade, most Cloud-based applications have been designed based on a centralized architecture, built around a Cloud server, which orchestrates operations, saves data and makes decisions. Nowadays, industrial applications impose more efficiency and reliability which cannot be ensured by a centralized architecture. In fact, industrial environments connectivity, latency and storage constitute major problems for such applications. Recent years have witnessed the emergence of fog computing as a solution, deployed to offer better performance with respect to latency, elasticity, reliability and inter-operability as compared to a centralized architecture. Such solution relies on the use of cooperating Cloudlets that are bringing Cloud computing services closer to the end-users. In this paper, we propose a new model for a distributed decision making solution in a Cloud environment based on an autonomic fog computing approach relying on self-knowledge techniques. In our work, we have used autonomic Cloudlets deployed as intelligent nodes in an industrial environment. Such Cloudlets are able to process some given tasks in order to make local decisions which will be used later by the Cloud server. Cloudlets are capable to manage local constraints like connectivity, latency, and synchronization with the Cloud. Moreover, the network situations are analyzed with respect to stated constraints using a case-based reasoning approach. Indeed, given the industrial setting, the appropriate type of connection available is automatically (WIFI, Ethernet, GPRS, 3G, etc.) The proposed architecture relies on proven technical solutions with respect to coordination, cooperation, autonomous behavior, communication, etc. The proposed architecture has been designed and deployed for validation, in an industrial company. © 2020, Springer Nature Switzerland AG.

关键词： Fog computing

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A Hybrid Approach for Routing Path Computation in Software Defined Ultra Dense Network 2

A Hybrid Approach for Routing Path Computation in Software D...

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2nd international conference on Sustainable Technologies for Industry 4.0 (STI)

作者： Quader, Mozdaher Abdul Quader, Montaser Abdul Roy, Palash Razzaque, Md Abdur Green Univ Bangladesh Dept Comp Sci & Engn Dhaka Bangladesh Univ Dhaka Green Networking Res Grp Dept Comp Sci & Engn Dhaka Bangladesh

ISBN: (纸本)9781665404891

In an OpenFlow based Software Defined Network (SDN), computation and installation of flow rules can be done proactively or reactively. Even though the proactive approach of SDN provides better performance in terms of transmission delay compared to its reactive counterpart, cannot be implemented in an extensive, unpredictable, and delay-sensitive network like Software Defined Ultra-dense Network (SD-UDN) of distributed Mobile Edge computing (MEC) servers due to the memory limitations of SDN forwarding devices. Furthermore, the reactive approach fails to provide service to delay-sensitive applications. In this paper we have developed a hybrid approach that retains the property of installing flow rules only along the computed path and reduces the initial path computation delay during packet transmission. The proposed approach also reduces reactive computation burdens by resolving significant computation loads proactively and minimizes the total number of node expansions during path computation with A* algorithm. Emulation results in mininet demonstrate an improvement in the proposed approach's performance in terms of initial path computation delay to about 90% compared to the state-of-the-art reactive approaches.

关键词： Software Defined Network Hybrid Approach Proactive Reactive Path Computation Time Mininet

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D2P: From Recursive Formulations to distributed-Memory Codes 19

D2P: From Recursive Formulations to Distributed-Memory Codes

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international conference for High Performance computing, networking, Storage and Analysis (SC)

作者： Hegde, Nikhil Chang, Qifan Kulkarni, Milind Indian Inst Technol Comp Sci & Engn Dharwad Karnataka India Purdue Univ Elect & Comp Engn W Lafayette IN 47907 USA Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47907 USA

ISBN: (数字)9781450362290

ISBN: (纸本)9781450362290

Recursive formulations of programs are straightforward to reason about and write, often have good locality properties, and readily expose parallelism. We observe that it is easier to automatically generate distributed-memory codes for recursive formulations with certain properties: i) inclusive-a recursive method's parameters summarize the data access done within the method body. ii) Intersection-data-set intersection tests among method invocations can be computed efficiently. In this paper we present D2P, a system that automatically generates distributed-memory codes for recursive divide-conquer algorithms with these properties. D2P produces MPI-based implementations starting from shared-memory specifications of the recursive algorithms. We evaluate D2P with recursive Dynamic Programming (DP) algorithms, since these algorithms have the desired properties and are well known. We show that the generated implementations are scalable and efficient: D2P-generated implementations execute faster than implementations generated by recent distributed DP frameworks, and are competitive with (and often faster than) hand-written implementations.

关键词： Recursion distributed memory Inspector-executor

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SLATE: Design of a Modern distributed and Accelerated Linear Algebra Library 19

SLATE: Design of a Modern Distributed and Accelerated Linear...

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international conference for High Performance computing, networking, Storage and Analysis (SC)

作者： Gates, Mark Kurzak, Jakub Charara, Ali Yarkhan, Asim Dongarra, Jack Univ Tennessee Knoxville TN 37996 USA Oak Ridge Natl Lab Oak Ridge TN USA Univ Manchester Manchester Lancs England

ISBN: (数字)9781450362290

ISBN: (纸本)9781450362290

The SLATE (Software for Linear Algebra Targeting Exascale) library is being developed to provide fundamental dense linear algebra capabilities for current and upcoming distributed high-performance systems, both accelerated CPU-GPU based and CPU based. SLATE will provide coverage of existing ScaLAPACK functionality, including the parallel BLAS;linear systems using LU and Cholesky;least squares problems using QR;and eigenvalue and singular value problems. In this respect, it will serve as a replacement for Sca-LAPACK, which after two decades of operation, cannot adequately be retrofitted for modern accelerated architectures. SLATE uses modern techniques such as communication-avoiding algorithms, lookahead panels to overlap communication and computation, and task-based scheduling, along with a modern C++ framework. Here we present the design of SLATE and initial reports of several of its components.

关键词： dense linear algebra distributed computing GPU computing

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Towards Named AI networking: Unveiling the Potential of NDN for Edge AI 19th

Towards Named AI Networking: Unveiling the Potential of NDN ...

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19th international conference on Ad-Hoc Networks and Wireless, ADHOC-NOW 2020

作者： Campolo, Claudia Lia, Gianmarco Amadeo, Marica Ruggeri, Giuseppe Iera, Antonio Molinaro, Antonella University Mediterranea of Reggio Calabria Reggio Calabria Italy University of Calabria Rende Italy

ISBN: (纸本)9783030617455

Thanks to recent advancements in edge computing, the traditional centralized cloud-based approach to deploy Artificial Intelligence (AI) techniques will be soon replaced or complemented by the so-called edge AI approach. By pushing AI at the network edge, close to the large amount of raw input data, the traffic traversing the core network as well as the inference latency can be reduced. Despite such neat benefits, the actual deployment of edge AI across distributed nodes raises novel challenges to be addressed, such as the need to enforce proper addressing and discovery procedures, to identify AI components, and to chain them in an interoperable manner. Named Data networking (NDN) has been recently argued as one of the main enablers of network and computing convergence, which edge AI should build upon. However, the peculiarities of such a new paradigm entails to go a step further. In this paper we disclose the potential of NDN to support the orchestration of edge AI. Several motivations are discussed, as well as the challenges which serve as guidelines for progress beyond the state of the art in this topic. © 2020, Springer Nature Switzerland AG.

关键词： Internet of things

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Data Collection and Node Counting by Opportunistic Communication

Data Collection and Node Counting by Opportunistic Communica...

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international-Federation-for-Information-Processing (IFIP) networking conference (IFIP networking)

作者： Li, Tong Kouyoumdjieva, Sylvia T. Karlsson, Gunnar Hui, Pan Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Hong Kong Peoples R China KTH Royal Inst Technol Dept Network & Syst Engn Stockholm Sweden Univ Helsinki Dept Comp Sci Helsinki Finland

ISBN: (纸本)9783903176164

Ever more powerful mobile devices are nowadays capable of collectively carrying out reasonably demanding computational tasks without offloading the processing to an edge server or a distant cloud-computing service. In this work, we explore such distributed computing and study how it is affected by the mobility as well as the number of nodes that collaborate. We choose distributed counting of a number of nodes in an enclosed area as application. Such application is useful for estimating attendance at events and measuring occupancy for facility management, as needed for monitoring of crowdedness with respect to safety and evacuation, climate control and comfort. For this application, we are interested in determining the time until all nodes know the correct number of nodes in the space where they reside. Our study shows the effect of mobility on the distributed process. We find that the process of collecting data opportunistically from a closed set of nodes is well described by empirical laws that we derive. We discuss the results and suggest further work needed to understand opportunistic computation and to develop it as a new model of computation among collectives of mobile nodes.

关键词： opportunistic computing distributed computing data collection counting

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An Autonomous Food Wastage Control Warehouse: distributed Ledger and Machine Learning based Approach 11

An Autonomous Food Wastage Control Warehouse: Distributed Le...

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11th international conference on computing, Communication and networking Technologies, ICCCNT 2020

作者： Nikish Kumar, S.V. Balasubramaniam, Saiharsha Tharagesh, Sanjay R. S. Kumar, Priyanka Janavi, B. Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering India

ISBN: (纸本)9781728168517

Food is an essential aspect of our lives. Back in those days, people consumed seasonal fruits and vegetables which were available locally and on a daily basis. But things have changed when refrigeration units were invented. People were now able to store and consume food products which were nonseasonal also. In today's world, warehouses and cold chains are of striking importance because most of the food products that we see in our supermarkets and grocery stores come from them. Due to improper management of food products these warehouses have become an epicenter for food wastage. Since most of the traditional warehouses are manual or RDBMS based, they can be easily tampered. With these considerations in this paper, we have proposed an autonomous warehouse system which will tackle food wastage by using Machine Learning and Blockchain technology. © 2020 IEEE.

关键词： Warehouses

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Optimizing Iterative Algorithms for Social Network Sharding

Optimizing Iterative Algorithms for Social Network Sharding

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IEEE international conference on Big Data

作者： Zishi Deng Torsten Suel Computer Science & Eng. New York University New York US

ISBN: (纸本)9781665445993

There has recently been significant interest in applications that require computations on massive graph structures, including scenarios where the graph is too large to be processed on a single machine. In this case, the graph needs to be partitioned into subgraphs that can be assigned to individual machines, in a process called graph or social network sharding. Given the sizes of the graphs involved, it is necessary or at least highly desirable that the partitioning itself can also be performed in a distributed manner, instead of running a sequential partitioning algorithm on a single *** study such distributed algorithms for graph sharding, where the goal is to create subgraphs of roughly equal size that minimize the number of edges crossing subgraph boundaries. In particular, we focus on two well-known approaches that can be efficiently i mplemented i n M apReduce a nd r elated distributed computing paradigms: the Balanced Label Propagation algorithm of Ugander and Backstrom, and the method of Duong et al. based on the Bayesian Stochastic Block Modeling approach of Hofman and Wiggins. Our contributions are as follows: (1) We perform the first direct experimental comparison of the two approaches, which were independently proposed and published. (2) We propose and evaluate several enhancements of Balanced Label Propagation that result in improved graph shardings. (3) We propose and evaluate hybrid methods that perform label propagation both on individual nodes, as suggested by Ugander and Backstrom, and on stochastic blocks inferred using the approach of Duong et al.

关键词： Social networking (online) conferences Computational modeling Stochastic processes Big Data Partitioning algorithms Iterative methods

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