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23rd Asia-Pacific Software Engineering Conference (APSEC)

作者： Cecchinel, Cyril Mosser, Sebastien Collet, Philippe Univ Cote Azur CNRS I3S Nice France

ISBN: (纸本)9781509055753

Smart buildings and smart cities rely on interconnected sensor networks that collect data about their environment to support various applications. Developing and deploying the data collection architectures of these systems is a challenging problem. The specificities of the sensor platforms compel software engineers to work at a low level. This makes this activity tedious, producing code that badly exploits the network architecture, and hampering reuse of data collection policies. Moreover, several data collection programs cannot be guaranteed to be deployable on a shared infrastructure. In this paper, we present an automated approach that supports (i) the definition of data collection policies at a higher level of abstraction, (ii) the representation of the diverse platforms and the network topology, and (iii) the automatic composition and deployment of the policies on top of heterogeneous sensing infrastructures following different strategies. The approach is tooled and has been assessed on both realistic and simulated deployments.

关键词： sensor network automated deployment infrastructure reuse

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automated deployment of a microservice-based monitoring infrastructure 1st

Automated deployment of a microservice-based monitoring infr...

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1st International Conference on Cloud Forward - From Distributed to Complete Computing (CFC)

作者： Ciuffoletti, Augusto Univ Pisa Dept Comp Sci I-56122 Pisa Italy

We explore the specification and the automated deployment of a monitoring infrastructure in a container-based distributed system. This result shows that highly customizable monitoring infrastructures can be effectively provided as a service, and that a key step in this process is the definition of an expandable abstract model for them. So we start defining a simple model of the monitoring infrastructure that provides an interface between the user and the cloud management system. The interface follows the guidelines of Open Cloud Computing Interface (OCCI), the cloud interface standard proposed by the Open Grid Forum. The definition is simple and generic and it is a first step towards the definition of a standard interface for Monitoring Services. It allows the definition of complex, hierarchical monitoring infrastructure by composing multiple instances of two basic components, one for measurement and another for data distribution,. We illustrate how the monitoring functionalities that are defined through the interface are implemented as microservices embedded in containers. The internals of each microservice reflects the distinction between core functionalities which are bound to the standard, and custom plugin modules. We describe the engine that automatically deploys a system of microservices that implements the monitoring infrastructure. Special attention is paid to preserve the distinction between core and custom functionalities, and the on demand nature of a cloud service. A proof of concept demo is available through the Docker hub and consists of two multi-threaded Java applications that implement the two basic components. (C) 2015 The Authors. Published by Elsevier B.V.

关键词： on-demand monitoring cloud computing Open Cloud Computing Interface (OCCI) microservice automated deployment containers

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Predictions-on-chip: model-based training and automated deployment of machine learning models at runtime For multi-disciplinary design and operation of gas turbines

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SOFTWARE AND SYSTEMS MODELING 2021年第3期20卷 685-709页

作者： Pilarski, Sebastian Staniszewski, Martin Bryan, Matthew Villeneuve, Frederic Varro, Daniel McGill Univ Montreal PQ Canada Siemens Canada Ltd Montreal PQ Canada Siemens Energy Inc Charlotte NC USA Siemens Energy Inc Orlando FL USA

The design of gas turbines is a challenging area of cyber-physical systems where complex model-based simulations across multiple disciplines (e.g., performance, aerothermal) drive the design process. As a result, a continuously increasing amount of data is derived during system design. Finding new insights in such data by exploiting various machine learning (ML) techniques is a promising industrial trend since better predictions based on real data result in substantial product quality improvements and cost reduction. This paper presents a method that generates data from multi-paradigm simulation tools, develops and trains ML models for prediction, and deploys such prediction models into an active control system operating at runtime with limited computational power. We explore the replacement of existing traditional prediction modules with ML counterparts with different architectures. We validate the effectiveness of various ML models in the context of three (real) gas turbine bearings using over 150,000 data points for training, validation, and testing. We introduce code generation techniques for automated deployment of neural network models to industrial off-the-shelf programmable logic controllers.

关键词： Prediction-at-runtime Machine learning Neural networks automated deployment Code generation Gas turbine engines

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Modeling and automated deployment of Serverless Applications using TOSCA 11

Modeling and Automated Deployment of Serverless Applications...

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11th IEEE International Conference on Service-Oriented Computing and Applications (SOCA)

作者： Wurster, Michael Breitenbuecher, Uwe Kepes, Kalman Leymann, Frank Yussupov, Vladimir Univ Stuttgart Inst Architecture Applicat Syst Stuttgart Germany

ISBN: (纸本)9781538691335

The serverless computing paradigm brings multiple benefits to application developers who are interested in consuming computing resources as services without the need to manage physical capacities or limits. There are several deployment technologies and languages available suitable for deploying applications to a single cloud provider. However, for multi-cloud application deployments, multiple technologies have to be used and orchestrated. In addition, the event-driven nature of serverless computing imposes further requirements on modeling such application structures in order to automate their deployment. In this paper, we tackle these issues by introducing an event-driven deployment modeling approach using the standard Topology and Orchestration Specification for Cloud Applications (TOSCA) that fully employs the suggested standard lifecycle to provision and manage multi-cloud serverless applications. To show the feasibility of our approach, we extended the existing TOSCA-based ecosystem OpenTOSCA.

关键词： Serverless Multi-Cloud Modeling automated deployment TOSCA

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Multi-stage Scheduling with Scalable Resources for automated deployment in Platform as a Service Cloud 7

Multi-stage Scheduling with Scalable Resources for Automated...

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Seventh International Symposium on Parallel Architectures, Algorithms and Programming

作者： Zhu, Jie Sha, Chao Nanjing Univ Posts & Telecommun Inst Comp Technol Sch Comp Sci & Technol Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781467391160

With the automated deployment services of PaaS (Platform as a Service) cloud, developers can focus only on application code, without concern for the application lifecycle management. The large proportion of the workload of PaaS system comes from the application deployment jobs. In the paper, an application deployment job is modeled as a two-stage flowshop type job with the deadline constraint. We explore the deadline-constraint hybrid flowshop problem for the automated deployment in PaaS Cloud. The scalability of virtual machines are also considered for solving the problem. The objective is to decide the number of scaled-out virtual machines for each stage, and sequence jobs on machines so as to finish jobs before their respective deadlines and minimize the total expenses. Two heuristics are proposed for the considered problem. Computational results show that the proposed algorithms performs well on both effectiveness and efficiency.

关键词： Platform as a Service automated deployment deadline constraint scalability flowshop scheduling

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automated deployment of a Microservice-based Monitoring Infrastructure

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Procedia Computer Science 2015年 68卷 163-172页

作者： Augusto Ciuffoletti Department of Computer Science - University of Pisa P.le B. Pontecorvo Pisa I-56122 Italy

We explore the specification and the automated deployment of a monitoring infrastructure in a container-based distributed system. This result shows that highly customizable monitoring infrastructures can be effectively provided as a service , and that a key step in this process is the definition of an expandable abstract model for them. So we start defining a simple model of the monitoring infrastructure that provides an interface between the user and the cloud management system. The interface follows the guidelines of Open Cloud Computing Interface (OCCI), the cloud interface standard proposed by the Open Grid Forum. The definition is simple and generic and it is a first step towards the definition of a standard interface for Monitoring Services. It allows the definition of complex, hierarchical monitoring infrastructure by composing multiple instances of two basic components, one for measurement and another for data distribution,. We illustrate how the monitoring functionalities that are defined through the interface are implemented as microservices embedded in containers. The internals of each microservice reflects the distinction between core functionalities which are bound to the standard, and custom plugin modules. We describe the engine that automatically deploys a system of microservices that implements the monitoring infrastructure. Special attention is paid to preserve the distinction between core and custom functionalities, and the on demand nature of a cloud service. A proof of concept demo is available through the Docker hub and consists of two multi-threaded Java appli- cations that implement the two basic components.

关键词： on-demand monitoring cloud computing Open Cloud Computing Interface (OCCI) microservice automated deployment containers

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Research on automated deployment Method of Smart Grid Spatio-temporal Information Service Platform Based on Cloud Computing

Research on Automated Deployment Method of Smart Grid Spatio...

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2018 International Conference on Power, Energy and Environmental Engineering（ICPEEE 2018)

作者： Ji-ye WANG Nan ZENG Cheng-yue YANG Guang ZHAO Department of Information and Communication State Grid Corporation of China State Grid Information & Telecommunication Group Co.Ltd.

The Smart Grid Spatio-temporal Information Service Platform(SG-SISP) Based on Cloud Computing has broken the traditional centralized deployment mode of information system,integrated various computing resources with the network,and realized the cloud mode transformation to distributed cluster *** article sets up a set of automated management mechanisms for cloud services and provides a unified set of application lifecycle management approaches to reduce the complexity of cloud application deployment,deployment,and *** the same time,the design of dynamic migration technology solutions to provide flexible cloud *** the same time,automation management,that is,the completion of automated deployment,monitoring and dynamic application migration design,effectively help GIS operation and maintenance managers to quickly implement the deployment plan.

关键词： Spatio-Temporal Information Service Platform Cloud Computing automated deployment Smart Grid

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A mechanism of automated monitoring deployment in grid environment 08

A mechanism of automated monitoring deployment in grid envir...

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Proceedings of the 5th International ICST Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness

作者： Yinfeng Wang Kwang Mong Sim Xiaoshe Dong Hong Kong Baptist University Hong Kong Baptist University Kowloon Tong Kowloon Xi'an Jiaotong Univesity Shannxi Xi'an

ISBN: (纸本)9789639799264

Constructing monitoring systems beforehand cannot satisfy the requirement of dynamically organizing resources. This paper describes a script-based approach to automate the deployment of grid monitoring service components. An automated deployment monitoring mechanism uses the information of dynamically joined nodes to deploy system components, which avoids potential errors and inconvenience by manual deployment. The mechanism is Grid Monitoring Architecture compliant, and provides user-defined XML interfaces that is scalable and integrates with existing grid platforms and monitoring systems smoothly. To decrease the deployment complexity, this paper proposes an N-ary-tree based algorithm that reduces the automated deployment complexity from linearity rate to logarithm and provides more than 10 times improvement on deployment time. Empirical results show that the automated deployment monitoring mechanism can rapidly deploy monitoring services, and monitors heterogeneous and dynamic grid resources efficiently.

关键词： grid resource management monitoring service automated deployment

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The deployment and Implementation of Cloud Platform for Remote Automatic Correction of Artificial Intelligence Models

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2025年第4期21卷 3466-3474页

作者： Ku, Je Chiao Chen, Shang Liang Natl Cheng Kung Univ Inst Mfg Informat & Syst Tainan 701401 Taiwan

Despite the advanced research in artificial intelligence (AI) technology within the academic community, enterprises have yet to fully integrate this technology into practical applications. In the manufacturing sector, deploying professional IT personnel to implement AI for just a few machines is not ideal, posing a significant challenge for traditional factories. Additionally, the rising consumer demand for highly personalized and high-value-added products necessitates increased flexibility in production design and implementation. This study aims to establish an AI model cloud service platform that possesses the following features: 1) remote dynamic model adjustment, 2) automated continuous integration, 3) automated continuous deployment, 4) administrator web status display interface, 5) data security protection mechanism, 6) global remote deployment, and 7) multiscenario operations including deployment mechanisms, model adjustment mechanisms, server setup mechanisms, and more. The platform aims to address the issue of production lines struggling to integrate AI into practical applications. The innovation of this research lies in the proposed system architecture and platform for introducing AI into the manufacturing industry. Enterprises can use this study to establish their AI integration systems and deploy relevant technologies, or they can directly apply the research outcomes to their industrial control environments. This approach reduces the difficulties enterprises face when introducing AI technology, accelerates the transition from production automation to intelligent manufacturing, enhances production efficiency and sustainability, and improves the operational performance and management of the manufacturing industry, thereby strengthening global competitiveness.

关键词： Artificial intelligence Cloud computing Training Computational modeling Production Monitoring Containers Virtual machines Manufacturing Biological system modeling automated deployment automated integration cloud service dynamic model correction model monitoring smart manufacturing

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automated Spark Clusters deployment for Big Data with Standalone Applications Integration 17th

Automated Spark Clusters Deployment for Big Data with Standa...

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17th Conference of the Spanish-Association-for-Artificial-Intelligence (CAEPIA)

作者： Fernandez, A. M. Torres, J. F. Troncoso, A. Martinez-Alvarez, F. Univ Pablo Olavide Div Comp Sci Seville 41013 Spain

ISBN: (纸本)9783319446363;9783319446356

The huge amount of data stored nowadays has turned big data analytics into a very trendy research field. Spark has emerged as a very powerful and widely used paradigm for clusters deployment and big data management. However, to get started is still a very tough task, due to the excessive requisites that all nodes must fulfil. Thus, this work introduces a web service specifically designed for an easy and efficient Spark cluster management. In particular, a service with a friendly graphical user interface has been developed to automate the deploying of clusters. Another relevant feature is the possibility of integrating any algorithm into the web service. That is, the user only needs to provide the executable file and the number of required inputs for a proper parametrization. Finally, an illustrative case study is included to show ad hoc algorithms usage (the MLlib implementation for k-means, in this case) across the nodes of the configured cluster.

关键词： Big data Spark Algorithms automated deployment

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