Effective memory reusability based on user distributions in a cloud architecture to support manufacturing ubiquitous computing

Hsu, Ping-Yu; Hsieh, Su-Tzu; Chuang, Yu-Cheng

doi:10.1080/0951192x.2015.1067912

Cited by 3 publications

(3 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The first category concerns deploying manufacturing software on local or global clouds, i.e., a "manufacturing version" of cloud computing. The second category has a broader scope, [409] manufacturing resources global Assembly Line [402] manufacturing services Assembly Line [403] shared memories WSAN [401] mobile network nodes NCS [406] network services NCS [405] virtual resources hybrid IIoT [407] network devices local cutting across production, management, design and engineering abilities in a manufacturing business. Unlike with classic computing and data storage, manufacturing involves physical equipment, monitors, materials and so on.…”

Section: Data Enabling Technology Articles On Energy Managementmentioning

confidence: 99%

“…In this approach, services are first clustered into abstract services, and then a clustering network of the abstract services is established. In [403], the authors design an effective load-adjusted allocation algorithm for enhancing memory reusability and improving the performance of servers by balancing their workloads. In [401], the authors consider industrial WSAN with mobile nodes and propose a fixedpath mobile node handover strategy, assisted by cloud services and an ants-colony algorithm.…”

Section: Data Enabling Technology Articles On Energy Managementmentioning

confidence: 99%

See 1 more Smart Citation

Data Management in Industry 4.0: State of the Art and Open Challenges

2019

View full text Add to dashboard Cite

Information and communication technologies are permeating all aspects of industrial and manufacturing systems, expediting the generation of large volumes of industrial data. This article surveys the recent literature on data management as it applies to networked industrial environments and identifies several open research challenges for the future. As a first step, we extract important data properties (volume, variety, traffic, criticality) and identify the corresponding data enabling technologies of diverse fundamental industrial use cases, based on practical applications. Secondly, we provide a detailed outline of recent industrial architectural designs with respect to their data management philosophy (data presence, data coordination, data computation) and the extent of their distributiveness. Then, we conduct a holistic survey of the recent literature from which we derive a taxonomy of the latest advances on industrial data enabling technologies and data centric services, spanning all the way from the field level deep in the physical deployments, up to the cloud and applications level. Finally, motivated by the rich conclusions of this critical analysis, we identify interesting open challenges for future research. The concepts presented in this article thematically cover the largest part of the industrial automation pyramid layers. Our approach is multidisciplinary, as the selected publications were drawn from two fields; the communications, networking and computation field as well as the industrial, manufacturing and automation field. The article can help the readers to deeply understand how data management is currently applied in networked industrial environments, and select interesting open research opportunities to pursue.

show abstract

Section: Data Enabling Technology Articles On Energy Managementmentioning

confidence: 99%

Section: Data Enabling Technology Articles On Energy Managementmentioning

confidence: 99%

Data Management in Industry 4.0: State of the Art and Open Challenges

2019

View full text Add to dashboard Cite

show abstract

“…The controller dynamically adjusts the right amount of CPU and memory required to meet the response time using the estimated coefficient that determines the degree of influence of CPU, memory, or both on the application performance change. An approach described in [78] aims at increasing the efficiency of memory utilisation through an efficient user allocation mechanism. The profile-oriented clustering algorithm (POCA) positioned users with similar profiles into the same application servers to share and save memory space by reusing the application programs.…”

Section: B Resource and User Allocationmentioning

confidence: 99%

Survey of Memory Management Techniques for HPC and Cloud Computing

Pupykina

Agosta

2019

IEEE Access

View full text Add to dashboard Cite

The emergence of new classes of HPC applications and usage models, such as real-time HPC and cloud HPC, coupled with the increasingly heterogeneous nature of HPC architectures, requires a renewed investigation of memory management solutions. Traditionally, memory is shared by an operating system using segmentation and paging techniques. At the same time, new classes of applications require Quality of Service (QoS) guarantees. As such, the typical practice of reserving a subset of the supercomputer to a single application becomes less attractive, leading to the exploration of cloud technologies. In this context, a viable scenario is that of multiple applications, with different QoS levels, coexisting on the same deeply heterogeneous HPC infrastructure and sharing resources. However, for this scenario to succeed in practice, resources, including memory, need to be allocated with a vision that includes both the application requirements and the current and future state of the overall system. In this survey, challenges of memory management in HPC and Cloud Computing, different memory management systems and optimisation techniques to increase memory utilisation are discussed in detail. INDEX TERMS Clouds, high performance computing, memory management, resource management. ANNA PUPYKINA received the higher education degree in data processing and control systems engineering from Samara State Aerospace University, Russia, in 2009. She is currently pursuing the Ph.D. degree with the Politecnico di Milano, under the supervision of Prof. G. Agosta. She was with Russian State Humanitarian University (RSUH), Togliatti, and also with the Department of Applied Mathematics and Informatics, Togliatti State University (TSU). Her main research interests include runtime management algorithms and programming model extensions for deeply heterogeneous systems. GIOVANNI AGOSTA received the Laurea degree in computer engineering and the Ph.D. degree in information technology from the Politecnico di Milano, Italy, in 2000 and 2004, respectively. He is currently a tenured Researcher. He has authored more than 80 articles in international journals, conferences, and workshops. His main research interest includes compiler technology. He was a recipient of four HiPEAC Awards and three Best Paper Awards. He has been involved in eight European funded projects, since 2010, holding workpackage and task leader responsibilities. He is a Full Member of the HiPEAC Network and the Organizer of two workshops colocated with the HiPEAC Conference.

show abstract

A novel hybrid decision-making model for team building in cloud service environment

Fan

Chu

et al. 2019

International Journal of Computer Integrated Manufacturing

View full text Add to dashboard Cite

Effective memory reusability based on user distributions in a cloud architecture to support manufacturing ubiquitous computing

Cited by 3 publications

References 11 publications

Data Management in Industry 4.0: State of the Art and Open Challenges

Data Management in Industry 4.0: State of the Art and Open Challenges

Survey of Memory Management Techniques for HPC and Cloud Computing

A novel hybrid decision-making model for team building in cloud service environment

Contact Info

Product

Resources

About