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Large-Scale intelligent Microservices

Large-Scale Intelligent Microservices

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IEEE international conference on Big data

作者： Mark Hamilton Nick Gonsalves Christina Lee Anand Raman Brendan Walsh Siddhartha Prasad Dalitso Banda Lucy Zhang Lei Zhang William T. Freeman Cognitive Services Research Microsoft MIT Boston USA Cognitive Services Research Microsoft Boston USA Applied AI Microsoft Bellevue USA Grey Systems Lab Microsoft Boston USA Applied AI Microsoft MIT Boston USA Cognitive Services Research Microsoft Bellevue USA CSAIL MIT Boston USA

ISBN: (数字)9781728162515

ISBN: (纸本)9781728162522

Deploying Machine Learning (ML) algorithms within databases is a challenge due to the varied computational footprints of modern ML algorithms and the myriad of database technologies each with their own restrictive syntax. We introduce an Apache Spark-based micro-service orchestration framework that extends database operations to include web service primitives. Our system can orchestrate web services across hundreds of machines and takes full advantage of cluster, thread, and asynchronous parallelism. Using this framework, we provide large scale clients for intelligent services such as speech, vision, search, anomaly detection, and text analysis. This allows users to integrate ready-to-use intelligence into any datastore with an Apache Spark connector. To eliminate the majority of overhead from network communication, we also introduce a low-latency containerized version of our architecture. Finally, we demonstrate that the services we investigate are competitive on a variety of benchmarks, and present two applications of this framework to create intelligent search engines, and real time auto race analytics systems.

关键词： Machine learning algorithms databases Web services Ecosystems Cluster computing Parallel processing Throughput

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Development of IoT/Cloud Integration Frameworks for Autonomous Networked Robots (ANR)

Development of IoT/Cloud Integration Frameworks for Autonomo...

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international Workshop on Research and Education in Mechatronics (REM)

作者： Maki K. Habib Chimsom. I. Chukwuemeka Robotics Control and Smart Systems (RCSS) The American University in Cairo (AUC) Cairo Egypt

ISBN: (数字)9781728162249

ISBN: (纸本)9781728162256

ANR are ubiquitous systems featured by the interaction of multirobots and WSNs, where data is exchanged between these systems leading to wider coverage and perception. Also, the integration of ANR systems with IoT/cloud platform increases their efficiency of the ANR system through expanded data storage for remote data analytics and cloud-based action triggering on their operational environments. This paper presents the development of IoT/cloud integration frameworks supporting ANR functionalities. The developed framework constitutes a base station (BS) IoT/cloud and sensor node (SN) IoT/cloud framework. The BS framework involves the use of IoT gateways to connect to IoT/cloud platforms while in the SN framework connects the sensor node IoTs to the IoT/cloud platform. In the BS framework, the sensor nodes communicate their data to the BS IoT gateway. The BS IoT gateway then communicates the data to the local BS visualization interface and to IoT/cloud platform for cloud visualization and analytics. For the SN IoT/cloud framework, the sensor nodes communicate the data directly to the local BS visualization interface and IoT/cloud platforms without the need for the IoT gateway. The BS framework was then developed and simulated for a large operational environment ANR monitoring application. The developed BS framework involved static and dynamic nodes with a fuzzy logic-based event classification system, a two-tier network connecting the sensor nodes, a local graphical user interface (GUI) for sensor node data visualization and ThingSpeak cloud visualization and analytics. The simulation results showed concurrent interaction and an effective exchange of data among the system components.

关键词： Robot sensing systems Wireless sensor networks Cloud computing Robots Robot kinematics data visualization Base stations

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A Review on the Role of IoT in smart Agriculture with Reference to Efficiency, Sustainability and Precision Farming

A Review on the Role of IoT in Smart Agriculture with Refere...

引用

Electronics and Sustainable Communication Systems (ICESC), 2020 international conference on

作者： Prajyot R Yesankar Prajwal Bandre Prateek Verma Amit G Gudadhe Palash Gourshettiwar Chetan Puri Department of Computer Science & Design Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Wardha Maharashtra India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

Agriculture is changing into a more sustainable and productive sector as a result of the fusion of modern technologies, especially the Internet of Things (Io'T), with traditional farming methods. This shift, dubbed “smart Agriculture,” rethinks how farmers conduct cultivation, managing resources, and sustainability by using the power of online access, data analytics, and automation. A network of linked systems and gadgets that gather, transfer, and evaluate real-time data forms the basis of smart agriculture. With previously unheard-of precision and efficiency, farmers may now remotely track and supervise a variety of aspects of their farming activities thanks to this connectivity. The transformative influence of IoT on productivity, sustainability, or precision farming highlights the need for it in agriculture. A crucial element of smart agriculture is precision farming, which entails exact management of resources like pesticides, fertilizers, and water as well as real-time data processing. This study examines the ways in which integrated geospatial technologies, weather forecasting, and IoT -driven automation and actuation support productive and sustainable farming methods. The emphasis on aquaculture monitoring also draws attention to the wide range of uses for IoT in agriculture that go beyond conventional crop growing. The crucial part that IoT plays in meeting the growing need for food production on a worldwide scale while using scarce resources. Precision farming, management of water, and crop monitoring are made possible by Internet of Things technologies including sensors, drones, or automated machinery. These apps support environmental sustainability in agriculture, improve resource efficiency, and enable data-driven decision-making. This research work goes into more detail on the fundamental components of smart agriculture, such as sensor technologies, infrastructure connectivity, automation, data transfer, acquisition, and integration with geospatial tec

关键词： smart agriculture Productivity Automation Crops Weather forecasting Sustainable development Monitoring Water resources intelligent sensors Farming

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data Fusion in Autonomous Vehicles Research, Literature Tracing from Imaginary Idea to smart Surrounding Community

Data Fusion in Autonomous Vehicles Research, Literature Trac...

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international conference on Fog and Mobile Edge computing (FMEC)

作者： Shadi AlZu'bi Yaser Jararweh Al Zaytoonah University of Jordan Amman Jordan Jordan University of Science and Technology Irbid Jordan

ISBN: (数字)9781728172163

ISBN: (纸本)9781728172170

Many significant challenges related to transportation systems have been raised recently, these include the high accidents rate, road congestion, emission of gases, and environment pollution. Furthermore, transportation crashes caused injuries in accident. Researchers investigated virtual methodologies to automate the transportation process that known as intelligent Transport System (ITS). In the last 20 years, ITS have been employed efficiently to enhance the performance of transportation systems, improve security of travel, and provide several choices for travelers. The idea of virtual technologies integration is a novel in transportation field. Collecting data from multi-sources played a significant improvement in ITS, this can help stakeholders for processing different forms of data. This huge amount of data can significantly help in the revolution of ITS. This paper focused on several Autonomous vehicles (AVs) related technologies that employed efficiently in ITS. This review integrates and synthesizes monitoring AVs using data fusion from different data sources. Furthermore, recent technologies and software toolboxes are presented in this paper to facilitate the researchers job in improving the data and sensor fusion in AVs.

关键词： Roads Automobiles Autonomous vehicles Robot sensing systems Cameras data integration

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Collaborative Computation Offloading for smart Cities in Mobile Edge computing

Collaborative Computation Offloading for Smart Cities in Mob...

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IEEE international conference on Cloud computing, CLOUD

作者： Hualong Huang Kai Peng Xiaolong Xu College of Engneering Huaqiao university Quanzhou China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing University of Science and Technology Nanjing China

ISBN: (数字)9781728187808

ISBN: (纸本)9781728187815

With the emergence of the Internet of things (IOT), smart cities have changed from concept to reality. Meanwhile, those countless IOT devices are scattered in every corner of the city which generate a mass of sensing big data and IoT services every minute. However, the computing capability of IoT devices is so constrained that IoT devices fail to process computational-intensive services. Mobile edge computing (MEC) is an emergent architecture for reinforcing the computing capabilities of IoT devices to cope with the resource-hungry IoT services. In the MEC system, IoT devices are capable of offloading part of IoT services to the cloudlet for execution. Although offloading can prominently mitigate the computing burden on IoT devices, it may result in enormous transmission cost and consuming resource of cloudlets. Therefore, it poses major challenges to how to achieve trade-offs in terms of time cost, energy cost of IOT devices and resource utilization of cloudlets. In consideration of the challenge, we devise a collaborative computation offloading approach to acquire the above trade-off in the collaboration of IoT device-cloudlet-cloud three ends. Firstly, the balancing strategies of the trade-off are obtained by leveraging the multi-objective evolutionary algorithm based on decomposition (MOEA/D). We then employ a multi-criterion decision-making method that combines the entropy weight (EW) method and technique for order preference by similarity to an ideal solution (TOPSIS), is known as (EW-TOPSIS), to acquire the optimum offloading decision in the acquired balancing strategies. Finally, extensive experiments and theoretical analysis validate the effectiveness of the proposed method.

关键词： Cloud computing smart cities Collaboration Sensors Internet of Things Resource management Edge computing

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Wireless Sensor Network for Traffic Monitoring

Wireless Sensor Network for Traffic Monitoring

引用

international conference on Promising Electronic Technologies (ICPET)

作者： Mohammd Fadi AbdelHaq Adnan Salman Advanced Computing AnNajah National University Nablus Palestine AnNajah National University Nablus Palestine

ISBN: (数字)9780738111391

ISBN: (纸本)9781665403818

intelligent Traffic Systems (ITS) management plays an important role in modern transportation systems. An important area in ITS research is the development of traffic monitoring systems that collect traffic information such as vehicle counting, speed measurements, and vehicle classification. This information allows for developing smart transportation systems that take into consideration the current traffic state in order to generate smart decisions. In this paper, we developed a Wireless Sensor Network (WSN) application for vehicle counting, vehicle classification, and speed measurement based on roadside magnetic sensors. Magnetic sensors can be deployed on the roadside to collect traffic data. Then, the data can be communicated using wireless channels to a central computer to be processed and analyzed. The traffic data will be saved in a database and can be shared with other transportation applications through the use of web services. This approach has many advantages includes real-time traffic monitoring, ensures proper monitoring of the roads, and requires less human intervention. The focus of this paper is on the development of the wireless sensor network and traffic data management. Simulated data is used to evaluate the system instead of deploying magnetic sensors. The software implementation of this paper is available at https://***/FadiHaq/WSN_TrafficMonitoring.

关键词： Wireless sensor networks Roads Base stations Wireless communication Magnetic domains Perpendicular magnetic anisotropy Magnetic sensors

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A Predictive-Trend-Aware and Critical-Path-Estimation-Based Method for Workflow Scheduling Upon Cloud Services

A Predictive-Trend-Aware and Critical-Path-Estimation-Based ...

引用

IEEE international conference on Services computing (SCC)

作者： Yi Pan Xiaoning Sun Yunni Xia Wanbo Zheng Xin Luo College of Computer Science Chongqing University Chongqing China Data Science Research Center Faculty of Science Kunming University of Science and Technology Kunming China Chongqing Engineering Research Center of Big Data Application for Smart Cities Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China Hengrui (Chongqing) Artificial Intelligence Research Center Cloudwalk China

ISBN: (数字)9781728187891

ISBN: (纸本)9781728187907

The cloud computing paradigm is featured by its ability to offer elastic computational resource provisioning patterns and deliver on-demand and versatile services. It's thus getting increasingly popular to build business process and workflow-based applications upon cloud computing platforms. However, it remains a difficulty to guarantee cost-effectiveness and quality of service of cloud-based workflows because real-world cloud services are usually subject to real-time performance variations or fluctuations. Existing researches mainly consider that cloud are with constant performance and formulate the scheduling decision-making as a static optimization problem. In this work, instead, we consider that scientific computing processes to be supported by decentralized cloud infrastructures are with fluctuating QoS and aim at managing the monetary cost of workflows with the completion-time constraint to be satisfied. We address the performance-trend-aware workflow scheduling problem by leveraging a time-series-based prediction model and a Critical-Path-Duration-Estimation-based (CPDE for short) scheduling strategy. The proposed method is capable of exploiting real-time trends of performance changes of cloud infrastructures and generating dynamic workflow scheduling plans. To prove the effectiveness of our proposed method, we build a large-prime-number-generation workflow supported by real-world third-party commercial clouds and show that our method clearly beats existing approaches in terms of cost, workflow completion time, and Service-Level-Agreement (SLA) violation rate.

关键词： Cloud computing Processor scheduling Computational modeling Quality of service Predictive models Dynamic scheduling Real-time systems

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Research on Prediction of Medical & IT Sectors in A-shares Based on Random Forest Algorithm

Research on Prediction of Medical & IT Sectors in A-shares B...

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Computers, Information Processing and Advanced Education (CIPAE), 2020 international conference on

作者： Kunrui Chen Lei Yang Shipei Du Longqing Zhang Yungui Chen Liwei Tian School of Computer Science Guangdong University of Science and Technology Dongguan China Dongguan Key Laboratory of Industrial Software for Intelligent Manufacturing Guangdong University of Science and Technology Dongguan China Guangdong University of Science and Technology Dongguan China Dongguan Key Laboratory of Kunpeng Computing School of Computer Science Guangdong University of Science and Technology Dongguan China Guangdong University of Science and Technology Dongguan Key Laboratory of Kunpeng Computing Dongguan China

ISBN: (数字)9798331527662

ISBN: (纸本)9798331527679

This research is set against the backdrop of China's Shanghai Stock Exchange A-share market, selecting three representative stocks from each of two distinct sectors as the research objects. Leveraging the “orange” software, we employed the Random Forest model to predict the closing prices of stocks belonging to these two sectors. The findings indicate that, with respect to these two diverse sectors, the Random Forest model yielded no discernible difference in prediction outcomes.

关键词： Analytical models Accuracy Computational modeling Predictive models Reliability engineering data models Software Stock markets Predictive analytics Random forests

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Improving the quality of education system using data Science Technologies: Survey

Improving the quality of education system using Data Science...

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IEEE conference on Innovative Technologies in intelligent Systems and Industrial Applications (CITISIA)

作者： Pattinige Ravindra R Fernando School of Computing and Mathematics Charles Sturt University Study Centre Melbourne Australia

ISBN: (数字)9781728194370

ISBN: (纸本)9781728194387

Education is the most important and silent weapon in a country for both individual and country's economy. However lower level of adoption in the education system, poor decision making with less accuracy levels, adoption to new curriculums or subjects, teaching and learning styles are the main issues in education systems. These factors also have further long-term consequences for a country such as unemployment rates rises high, lack of suitable workforce for the demanding fields, individual dissatisfaction while being unemployed as well as in the community and socially. Unemployment rates are risen in Australia from past few years and this as a factor will be an ongoing issue if the government does not take any further actions to overcome these issues will definitely be direct hit to their economy in relation to work force in the present and future. Therefore the right technology should be implemented in order to obtain extract insights, obtain accurate decisions and high level adoption in education sector, as an example technologies such as data warehousing, big data, data mining, business intelligence and data analytics are in the peak of other industries such as aviation, retail, banking and other financial institutions. The main objective of this project is to facilitate a guide or a review for having data science technologies implemented in education sector in order to accomplish better education, as well as emphasis potential advantages of data technologies if it has been implemented in and around education systems.

关键词： Education Warehousing Decision making data science data warehouses data mining Unemployment

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Optimal phase swapping in low voltage distribution networks based on smart meter data and optimization heuristics 3rd

Optimal phase swapping in low voltage distribution networks ...

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Proceedings of the 3rd international conference on Harmony Search Algorithm, ICHSA 2017

作者： Mendia, Izaskun Gil-López, Sergio Del Ser, Javier Bordagaray, Ana González Prado, Jesús García Vélez, Manuel TECNALIA Derio48160 Spain University of the Basque Country UPV/EHU Bilbao48013 Spain Bilbao48009 Spain IBERDROLA Distribución Eléctrica S. A. Bilbao48003 Spain

ISBN: (纸本)9789811037276

In this paper a modified version of the Harmony Search algorithm is proposed as a novel tool for phase swapping in Low Voltage Distribution Networks where the objective is to determine to which phase each load should be connected in order to reduce the unbalance when all phases are added into the neutral conductor. Unbalanced loads deteriorate power quality and increase costs of investment and operation. A correct assignment is a direct, effective alternative to prevent voltage peaks and network outages. The main contribution of this paper is the proposal of an optimization model for allocating phases consumers according to their individual consumption in the network of low-voltage distribution considering mono and bi-phase connections using real hourly load patterns, which implies that the computational complexity of the defined combinatorial optimization problem is heavily increased. For this purpose a novel metric function is defined in the proposed scheme. The performance of the HS algorithm has been compared with classical Genetic Algorithm. Presented results show that HS outperforms GA not only on terms of quality but on the convergence rate, reducing the computational complexity of the proposed scheme while provide mono and bi phase connections. © Springer Nature Singapore Pte Ltd. 2017.

关键词： Computational complexity

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