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Exploring Computation Offloading in Vehicular Clouds 8

Exploring Computation Offloading in Vehicular Clouds

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8th IEEE international conference on Cloud networking (CloudNet)

作者： de Souza, Alisson Barbosa Leal Rego, Paulo Antonio de Souza, Jose Neuman Univ Fed Ceara GREat Fortaleza Ceara Brazil

ISBN: (纸本)9781728148328

VANETs (Vehicular Ad Hoc Networks) aim to provide safety and comfort services for drivers on roads and highways. Among these services, there are computation-intensive tasks in real-time applications that need to be executed as quickly as possible. Using VCC (Vehicular Cloud computing) paradigm, where vehicles offer their underutilized computing resources as a service and with the goal of reducing the execution time of tasks, this paper investigates how the computation offloading technique can be leveraged to speed up the execution of tasks under different conditions and in varied scenarios. We present an offloading decision algorithm and several simulations results that show we can achieve better processing times than to execute locally and better success rates than random algorithm.

关键词： VANETs Computation Offloading Vehicular Cloud computing

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Deep Learning for Surface Wave Identification in distributed Acoustic Sensing Data 8

Deep Learning for Surface Wave Identification in Distributed...

引用

8th IEEE international conference on Big Data (Big Data)

作者： Dumont, Vincent Tribaldos, Veronica Rodriguez Ajo-Franklin, Jonathan Wu, Kesheng Lawrence Berkeley Natl Lab Berkeley CA 94720 USA Rice Univ Houston TX USA

ISBN: (纸本)9781728162515

Moving loads such as cars and trains are very useful sources of seismic waves, which can be analyzed to retrieve information on the seismic velocity of subsurface materials using the techniques of ambient noise seismology. this information is valuable for a variety of applications such as geotechnical characterization of the near-surface, seismic hazard evaluation, and groundwater monitoring. However, for such processes to converge quickly, data segments with appropriate noise energy should be selected. distributed Acoustic Sensing (DAS) is a novel sensing technique that enables acquisition of these data at very high spatial and temporal resolution for tens of kilometers. One major challenge when utilizing the DAS technology is the large volume of data that is produced, thereby presenting a significant Big Data challenge to find regions of useful energy. In this work, we present a highly scalable and efficient approach to process real, complex DAS data by integrating physics knowledge acquired during a data exploration phase followed by deep supervised learning to identify "useful" coherent surface waves generated by anthropogenic activity, a class of seismic waves that is abundant on these recordings and is useful for geophysical imaging. Data exploration and training were done on 130 Gigabytes (GB) of DAS measurements. Using parallel computing, we were able to do inference on an additional 170 GB of data (or the equivalent of 10 days' worth of recordings) in less than 30 minutes. Our method provides interpretable patterns describing the interaction of ground-based human activities with the buried sensors.

关键词： Big Data Deep Learning distributed Acoustic Sensing distributed Training Sensor Network Technology

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Sketch and Scale Geo-distributed tSNE and UMAP 8

Sketch and Scale Geo-distributed tSNE and UMAP

引用

8th IEEE international conference on Big Data (Big Data)

作者： Wei, Viska Ivkin, Nikita Braverman, Vladimir Szalay, Alexander S. Johns Hopkins Univ Baltimore MD 21218 USA Amazon Seattle WA USA

ISBN: (纸本)9781728162515

Running machine learning analytics over geographically distributed datasets is a rapidly arising problem in the world of data management policies ensuring privacy and data security. Visualizing high dimensional data using tools such as t-distributed Stochastic Neighbor Embedding (tSNE) and Uniform Manifold Approximation and Projection (UMAP) became a common practice for data scientists. Both tools scale poorly in time and memory. While recent optimizations showed successful handling of 10,000 data points, scaling beyond million points is still challenging. We introduce a novel framework: Sketch and Scale (SnS). It leverages a Count Sketch data structure to compress the data on the edge nodes, aggregates the reduced size sketches on the master node, and runs vanilla tSNE or UMAP on the summary, representing the densest areas, extracted from the aggregated sketch. We show this technique to be fully parallel, scale linearly in time, logarithmically in memory and communication, making it possible to analyze datasets with many millions, potentially billions of data points, spread across several data centers around the globe. We demonstrate the power of our method on two mid-size datasets: cancer data with 52 million 35-band pixels from multiplex images of tumor biopsies;and astrophysics data of 100 million stars with multi-color photometry from the Sloan Digital Sky Survey (SDSS).

关键词： count sketch heavy hitter scalable umap tsne geo-distributed

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A middle-ware approach to leverage the distributed data deduplication capability on HPC and Cloud storage systems 8

A middle-ware approach to leverage the distributed data dedu...

引用

8th IEEE international conference on Big Data (Big Data)

作者： Chen, Hsing-bung Tang, Sihai Fu, Song Los Alamos Natl Lab HPC Design Grp Los Alamos NM 87545 USA Univ North Texas CSE Dept Denton TX 76203 USA

ISBN: (纸本)9781728162515

the unprecedented growth in the volume and diversity of the data in today's HPC and Enterprise computing environment has posted challenging problems on data management and data space reduction. More than 71% of enterprise and HPC communities are seeking de-duplication technologies to reduce cost and increase the storage efficiency. the importance of applying data de-duplication techniques is critical for active research and development. Current implementations of data de-duplication systems are mainly hardware dependent, system dependent, and platform dependent. Also, most of these implementations are proprietary software and not in open source domains. In this paper, we present a new middle-ware design and implementation approach, named D3M, to support distributed data de-duplication feature on existing file and object storage systems. We also incorporate this proposed D3M middle-ware with the Redhat's Linux device layer de-duplication and compression driver, called VDO (Virtual Data Optimizer). With these two layers of data de-duplication support, we accommodate both client side and server-side data de-duplication features. Finally, we conduct various testing cases on HPC data sets and Enterprise data sets to illustrate the benefits and advantages of applying our bilayer data de-duplication middle-ware solution.

关键词： De-duplication Storage HPC Middle-ware Data Chunking Performance evaluation

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the Vision of 5G and Cell-Free Communication Networks in Malaysia 8

The Vision of 5G and Cell-Free Communication Networks in Mal...

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8th international conference on Information Technology and Multimedia, ICIMU 2020

作者： Mahdi, Mohammed Najah Mohamed, Khalid Sheikhidris Ahmad, Abdul Rahim Subhi, Mohammed Ahmed Institute of Informatics and Computing in Energy Universiti Tenaga Nasional Kajang Malaysia Faculty of Engineering Multimedia University Cyberjaya Malaysia College of Computing Informatics Universiti Tenaga Nasional Kajang Malaysia Universiti Kebangsaan Malaysia Department of Electric Electronics and System Engineering Bangi Malaysia

ISBN: (纸本)9781728173108

5 G cellular networks are supposed to accommodate the ever-increasing data explosion and hyper-dense installation of base stations thanks to their capacity to leverage connectivity and cost-effectiveness. 5G networks can accommodate far more applications concurrently than the existing infrastructure easily and efficiently. this is particularly relevant in view of the fact that the Internet is going to be used by around 41.6 billion linked users by 2025. Many will require guaranteed access to cloud services to function safely, like autonomous vehicle systems and industrial sensors. Whilst 4G networks (which use the Internet Protocol and Wifi, wireless and reaction-based networking) dominated the environment of telecommunications, Artificial intelligence Technology has advanced, along with its numerous sub-categories, including machine learning and deep learning, which was ready for delivering 5G networks for individuals to tackle the decreased bandwidth capacity of 4G. this project encourages us to create location optimization technologies that chooses ideal places for services antennas, ensuring the best potential coverage and use computer training algorithms to distribute resources dynamically to minimise user interference and to save energy. © 2020 IEEE.

关键词： 5G mobile communication systems

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How old users are? Community analysis. 8

How old users are? Community analysis.

引用

Russian Advances in Fuzzy Systems and Soft computing: Selected Contributions to the 8th international conference on "Fuzzy Systems, Soft Soft computing and Intelligent Technologies",FSSCIT 2020

作者： Oliseenko, Valerii Korepanova, Anastasia St. Petersburg State University Mathematics and Mechanics Faculty Universitetskaya Emb. 7-9 St. Petersburg199034 Russia St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences 14-th Linia VI No. 39 St. Petersburg199178 Russia

the presented work is devoted to the development of an approach to the age inference for users on the online social network ***. this approach is based on an analysis of the communities that the user is a member of. the proposed method is based on the hypothesis that users join some of those communities that contain data about class, graduation from school, data about the group at the university, etc. and those that have users’ peers as members. the proposed approach allows for the age inference. the obtained results can benefit research on online social networks, as well as the task of discovering a user vulnerability through social networks. © 2020 CEUR-WS. All rights reserved.

关键词： Social networking (online)

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Smart Street Design for Managing Crowds in Hajj

Smart Street Design for Managing Crowds in Hajj

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international conference on computing for Sustainable Global Development (INDIACom)

作者： Adnan Ahmed Abi Sen Yazed Alsaawy Ahmad Alkhodre Nour Mahmoud Bahbouh Mohamed Alhaboob Faculty of Computer and Information Systems Islamic University Al-Madinah Saudi Arabia Granada University Granada Spain College of Computing and Information Technology Islamic University Al-Madinah Saudi Arabia

Hajj is an annual pilgrimage to Makkah in Saudi Arabia where over two and a half million pilgrims attend. It is one of the most intense crowded events in the world. Unfortunately, Hajj has witnessed several stampedes, fires and other disasters, which have led to the death of thousands of people. In recent years, the hajj management has made several improvements in the infrastructure, which has helped ease congestion in crowded rituals. However, despite the great development and huge contributions in improving the infrastructure of the facilities, the problem of crowd control and congestion still remains a real challenge to the management. Although crowd management during hajj is exemplary, it still remains largely manual. this research proposes an innovative solution by designing digital smart streets based on cheap LED-light screens, and control algorithm in addition to distributed fog nodes, wireless network sensors, and servers for the main computing and management. the proposed system will facilitate the process of controlling crowds, and the way it will function with the help of special signals and colors. these signs and instructions can be quickly and easily followed and adopted by people within the crowd. In this way, crowd management would be able to provide a prompt response to the requests and alerts from the central command. We use many techniques to detect the issues of crowds and locate places of interest based on the fog nodes and digital street processing. A prototype of the proposed system has been simulated to demonstrate its feasibility and ease of application to determine the benefits and features that it is capable to achieve, if implemented in crowded areas during the course of the Hajj.

关键词： Wireless sensor networks Wireless networks Prototypes Process control Color Transforms Manuals

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Building Dynamic Mapping with CUPS for Next Generation Automotive Edge computing 8

Building Dynamic Mapping with CUPS for Next Generation Autom...

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8th IEEE international conference on Cloud networking (CloudNet)

作者： Sun, Zhaoxia Du, Ping Nakao, Akihiro Zhong, Lei Onishi, Ryokichi Univ Tokyo Tokyo Japan Toyota Motor Co Ltd Toyota Japan

ISBN: (纸本)9781728148328

With the development of IoT and 5G networks, the demand for the next-generation intelligent transportation system has been growing at a rapid pace. Dynamic mapping has been considered one of the key technologies to reduce traffic accidents and congestion in the intelligent transportation system. However, as the number of vehicles keeps growing, a huge volume of mapping traffic may overload the central cloud, leading to serious performance degradation. In this paper, we propose and prototype a CUPS (control and user plane separation)-based edge computing architecture for the dynamic mapping and quantify its benefits by prototyping. there are a couple of merits of our proposal: (i) we can mitigate the overhead of the networks and central cloud because we only need to abstract and send global dynamic mapping information from the edge servers to the central cloud;(ii) we can reduce the response latency since the dynamic mapping traffic can be isolated from other data traffic by being generated and distributed from a local edge server that is deployed closer to the vehicles than the central server in cloud. the capabilities of our system have been quantified. the experimental results have shown our system achieves throughput improvement by more than four times, and response latency reduction by 67.8% compared to the conventional central cloud-based approach. Although these results are still obtained from the preliminary evaluations using our prototype system, we believe that our proposed architecture gives insight into how we utilize CUPS and edge computing to enable efficient dynamic mapping applications.

关键词： Dynamic mapping CUPS Edge computing 5G

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distributed Architecture of an Intrusion Detection System Based on Cloud computing and Big Data Techniques 8th

Distributed Architecture of an Intrusion Detection System Ba...

引用

8th international conference on Sciences of Electronics, Technologies of Information and Telecommunication, SETIT 2018

作者： Ben Fekih, Rim Jemili, Farah University of Sousse Sousse Tunisia University of Sousse Sousse Tunisia

ISBN: (纸本)9783030210045

An efficient intrusion detection system (IDS) requires more than just a good machine learning (ML) classifier. However, current IDSs offer a limited perspective in handling alerts’ databases. these databases must be local and structured in order to be referenced by these IDSs, offering an obsolete approach to solve advanced attacks and intrusions on distributed systems. With the emergence of big data, cyber-attacks have become a concerning issue worldwide. In situations where data security is paramount, swiftness becomes an obligation in processing and analytic operations. In that aspect, cloud-computing services can deal efficiently with big data issues. they offer storage and distributed analysis as the tools to be featured in our paper. To handle a large scale of alert data, we propose a new distributed IDS model that solves data storage problems, combines multiple heterogeneous sources of alert data, and makes data treatment much faster than local IDSs. For this purpose, this paper presents an approach of IDS using Databricks as a Cloud environment and Spark as a big data analysis tool. © 2020, Springer Nature Switzerland AG.

关键词： Intrusion detection

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A Survey on Load/Power Flow Methods and DG Allocation Using Grasshopper Optimization Algorithm in Distribution Networks 8th

A Survey on Load/Power Flow Methods and DG Allocation Using ...

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8th international conference on Soft computing for Problem Solving (SocProS)

作者： Sambaiah, Kola Sampangi Jayabarathi, T. Vellore Inst Technol Sch Elect Engn Vellore Tamil Nadu India

ISBN: (纸本)9789811501845;9789811501838

Emerging smart grid technologies in electric distribution network (EDN) planning, operation, and automation require fast and efficient power flow methods. this imposes the continual search for speedy and effective power flow algorithms for EDNs. the present paper illustrates a survey and summary of recent developments in power flow methods. Several technical approaches have been presented for optimal power flow methods for power loss identification and distributed generation (DG) allocation in EDNs. the main contribution of the present survey paper is calculation of network power loss using power flow methods in radial distribution networks (RDNs). Here two major power flow methods used are forward and backward sweep (FBS) and direct approach (DA). the methods are compared on the basis of loss reduction and minimum voltage bus identification capability. the present survey provides the researchers a clear idea of power flow methods utilized in EDN performance enhancement. the power flow analysis is carried out on different EDNs, i.e., small, medium and large scale. Later DG allocation is carried out by using a novel meta-heuristic algorithm called grasshopper optimization algorithm (GOA). the results obtained are compared with existing techniques.

关键词： Power flow methods Radial distribution networks distributed generation Power loss

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