scholarly journals Appendix A: Example Of Microsoft Azure Cloud Service: Filemanager

2016 ◽  
pp. 299-308
Keyword(s):  
Cloud Service ◽  
Microsoft Azure

scholarly journals Optimized memory model for hadoop map reduce framework

2019 ◽  
Vol 9 (5) ◽  
pp. 4396
Author(s):  
Archana Bhaskar ◽  
Rajeev Ranjan
Keyword(s):  
Service Provider ◽  
Service Level Agreement ◽  
Computation Time ◽  
Large Data ◽  
Cloud Service ◽  
Map Reduce ◽  
Memory Optimization ◽  
Process Data ◽  
Cloud Service Provider ◽  
Microsoft Azure

Map Reduce is the preferred computing framework used in large data analysis and processing applications. Hadoop is a widely used Map Reduce framework across different community due to its open source nature. Cloud service provider such as Microsoft azure HDInsight offers resources to its customer and only pays for their use. However, the critical challenges of cloud service provider is to meet user task Service level agreement (SLA) requirement (task deadline). Currently, the onus is on client to compute the amount of resource required to run a job on cloud. This work present a novel memory optimization model for Hadoop Map Reduce framework namely MOHMR (Optimized Hadoop Map Reduce) to process data in real-time and utilize system resource efficiently. The MOHMR present accurate model to compute job memory optimization and also present a model to provision the amount of cloud resource required to meet task deadline. The MOHMR first build a profile for each job and computes memory optimization time of job using greedy approach. Experiment are conducted on Microsoft Azure HDInsight cloud platform considering different application such as text computing and bioinformatics application to evaluate performance of MOHMR of over existing model shows significant performance improvement in terms of computation time. Experiment are conducted on Microsoft Azure HDInsight cloud. Overall, good correlation is reported between practical memory optimization values and theoretical memory optimization values.

scholarly journals An Accurate and Efficient Scheduler for Hadoop MapReduce Framework

2018 ◽  
Vol 12 (3) ◽  
pp. 1132 ◽  
Author(s):  
D C Vinutha ◽  
G.T Raju
Keyword(s):  
Service Provider ◽  
Service Level Agreement ◽  
Computation Time ◽  
Large Data ◽  
Cloud Service ◽  
Process Data ◽  
Mapreduce Framework ◽  
Cloud Service Provider ◽  
Hadoop Mapreduce ◽  
Microsoft Azure

MapReduce is the preferred computing framework used in large data analysis and processing applications. Hadoop is a widely used MapReduce framework across different community due to its open source nature. Cloud service provider such as Microsoft azure HDInsight offers resources to its customer and only pays for their use. However, the critical challenges of cloud service provider is to meet user task Service level agreement (SLA) requirement (task deadline). Currently, the onus is on client to compute the amount of resource required to run a job on cloud. This work present a novel makespan model for Hadoop MapReduce framework namely OHMR (Optimized Hadoop MapReduce) to process data in real-time and utilize system resource efficiently. The OHMR present accurate model to compute job makespan time and also present a model to provision the amount of cloud resource required to meet task deadline. The OHMR first build a profile for each job and computes makespan time of job using greedy approach. Furthermore, to provision amount of resource required to meet task deadline Lagrange Multipliers technique is applied. Experiment are conducted on Microsoft Azure HDInsight cloud platform considering different application such as text computing and bioinformatics application to evaluate performance of OHMR of over existing model shows significant performance improvement in terms of computation time. Experiment are conducted on Microsoft Azure HDInsight cloud. Overall good correlation is reported between practical makespan values and theoretical makespan values.

scholarly journals Highlight the Features of AWS, GCP and Microsoft Azure that Have an Impact when Choosing a Cloud Service Provider

2020 ◽  
Vol 8 (5) ◽  
pp. 4124-4232
Keyword(s):  
Service Provider ◽  
Service Providers ◽  
Cloud Service ◽  
Public Cloud ◽  
Cloud Service Provider ◽  
Computing Services ◽  
Cloud Service Providers ◽  
Amazon Web Services ◽  
Cloud Computing Services ◽  
Microsoft Azure

Picking up public cloud service providers is now becoming a harder task in an enterprise organization. This paper will help in reducing more hesitation to choose a public cloud service provider. This paper is highlighting computation, storage, and infrastructure is important to service features that have an impact when choosing cloud service providers. Compare these three (AWS, Microsoft Azure, GCP) CSPs concerning service, price, advantages, and highlight significant service features. Studies discuss the primary reason to choose a CSP that normally enhance features, familiarity with the brand, suitable for organization and security parameters considered when choosing CSP. Amazon Web Services proved its leadership by maintaining about 33% share in the market throughout for several quarters irrespective of the market size increased by a factor of 3. Microsoft has shown prominent performance in SaaS. Since 2008, after introducing PaaS in the form of Google App Engine, Google is continuously enhancing its cloud computing services of Google Cloud Platform.

scholarly journals IoT-платформа для вібраційної діагностики промислового обладнання

2021 ◽  
pp. 125-139
Author(s):  
Ігор Борисович Туркін ◽  
В'ячеслав Андрійович Лезновскій
Keyword(s):  
Artificial Intelligence ◽  
Learning Algorithm ◽  
Diagnostic System ◽  
Cloud Service ◽  
Measuring System ◽  
Industrial Equipment ◽  
Sensors And Actuators ◽  
Vibration Diagnostics ◽  
Iot Platform ◽  
Microsoft Azure

The subject of study in the article is a digital platform for vibration diagnostics of industrial equipment. The aim is to increase the informativeness of vibration diagnostics processes of industrial equipment by developing and implementing IoT-oriented solutions based on the concept of intelligent sensors and actuators according to the IEEE standard 1451.0-2007. Tasks: to substantiate the feasibility of using platform-oriented technologies for vibration diagnostics of industrial equipment and choose a cloud service for the implementation of the platform, to develop software and hardware solutions for IoT-platform for vibration diagnostics of industrial equipment; calibrate the vibration diagnostic system and check the accuracy of the measurement. The methods used are microservice approach, multilevel architecture, methods for assessing the condition of equipment by vibration indicators. The following results were obtained. The Microsoft Azure IoT platform, which provides the infrastructure for creating and managing cloud applications, was chosen as the cloud computing platform for the industrial equipment vibration diagnostic system. Azure Internet of Things Suite is a Microsoft Azure IoT service designed to integrate and organize data flows, analyze, and present data in a format that helps people make informed decisions. The architecture of the IoT-system of vibration diagnostics of industrial equipment developed and presented in the article is three-level. The level of autonomous sensors provides reading of vibration acceleration indicators and through the digital wireless data transmission channel BLE transmits data to the Hub level, which is implemented based on a single-board microcomputer BeagleBone. The computing power of BeagleBone provides work with artificial intelligence algorithms. At the third level of the server platform, the tasks of diagnosing and predicting the state of the equipment are solved, for which the Dictionary Learning algorithm implemented in the Python programming language is used. Conclusions. Tests of the IoT system for vibration diagnostics of industrial equipment were performed using a special stand, which allows the calibration of sensors and verification of the accuracy of the measuring system. The correctness of the entire system is confirmed by the coincidence of expected and measured results. The direction of development of the IoT-system for vibration diagnostics of industrial equipment is the development of additional microservices, which will add the possibility of using modern artificial intelligence technologies for complex diagnostics and forecasting of equipment status.

Multimedia system and database simulation based on internet of things and cloud service platform

2020 ◽  
pp. 1-12
Author(s):  
Zhang Caiqian ◽  
Zhang Xincheng
Keyword(s):  
Internet Of Things ◽  
Cloud Service ◽  
Multimedia System ◽  
The Internet ◽  
Multimedia Database ◽  
Cloud Platform ◽  
Service Platform ◽  
Device Management ◽  
Cloud Service Platform ◽  
The Internet Of Things

The existing stand-alone multimedia machines and online multimedia machines in the market have certain deficiencies, so they cannot meet the actual needs. Based on this, this research combines the actual needs to design and implement a multi-media system based on the Internet of Things and cloud service platform. Moreover, through in-depth research on the MQTT protocol, this study proposes a message encryption verification scheme for the MQTT protocol, which can solve the problem of low message security in the Internet of Things communication to a certain extent. In addition, through research on the fusion technology of the Internet of Things and artificial intelligence, this research designs scheme to provide a LightGBM intelligent prediction module interface, MQTT message middleware, device management system, intelligent prediction and push interface for the cloud platform. Finally, this research completes the design and implementation of the cloud platform and tests the function and performance of the built multimedia system database. The research results show that the multimedia database constructed in this paper has good performance.

Development of Cloud Service for Interconnect Testing on HEMS Devices

2013 ◽  
Vol 133 (4) ◽  
pp. 818-819
Author(s):  
Hiroshi Sugimura ◽  
Kazuo Sekiya ◽  
Tomoki Watanabe ◽  
Masao Isshiki
Keyword(s):  
Cloud Service ◽  
Interconnect Testing

A Group Collaboratable Proof of Retrievability Scheme for Cloud Data Storage

2014 ◽  
Vol 13 (7) ◽  
pp. 4625-4632
Author(s):  
Jyh-Shyan Lin ◽  
Kuo-Hsiung Liao ◽  
Chao-Hsing Hsu
Keyword(s):  
Cloud Computing ◽  
Data Storage ◽  
Cloud Service ◽  
Third Party ◽  
Group Collaboration ◽  
Cloud Data ◽  
Cloud Data Storage ◽  
Important Trend ◽  
Data Update ◽  
Proof Of Retrievability

Cloud computing and cloud data storage have become important applications on the Internet. An important trend in cloud computing and cloud data storage is group collaboration since it is a great inducement for an entity to use a cloud service, especially for an international enterprise. In this paper we propose a cloud data storage scheme with some protocols to support group collaboration. A group of users can operate on a set of data collaboratively with dynamic data update supported. Every member of the group can access, update and verify the data independently. The verification can also be authorized to a third-party auditor for convenience.

Cloud Service for Seismic Influence Field Analysis Based on GeoKSCloud: A Case Study in Fujian Province

2013 ◽  
Vol 15 (5) ◽  
pp. 695
Author(s):  
Jianwei WU ◽  
Chongcheng CHEN ◽  
Xiaozhu WU ◽  
Jianfeng LIN ◽  
Zhao HUANG ◽  
...  
Keyword(s):  
Cloud Service ◽  
Field Analysis ◽  
Fujian Province
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