Simulation-based matching of cloud applications

Bonchi, Filippo; Brogi, Antonio; Canciani, Andrea; Soldani, Jacopo

doi:10.1016/j.scico.2017.06.001

Cited by 9 publications

(9 citation statements)

References 23 publications

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“…For example, in complex automobile manufacturing, ceramic production and chemical processes [172] and medical operation training in paediatric urology and surgery [173]. Additionally, it has been applied to study complex systems such as the cloudbased system [174] and group safety especially for underground miners [175].…”

Section: Artificial Intelligence (Ai)mentioning

confidence: 99%

Exponential Disruptive Technologies and the Required Skills of Industry 4.0

Bongomin

Ocen

Nganyi

et al. 2020

Journal of Engineering

137

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The 21st century has witnessed precipitous changes spanning from the way of life to the technologies that emerged. We have entered a nascent paradigm shift (industry 4.0) where science fictions have become science facts, and technology fusion is the main driver. Thus, ensuring that any advancement in technology reach and benefit all is the ideal opportunity for everyone. In this study, disruptive technologies of industry 4.0 were explored and quantified in terms of the number of their appearances in published literature. The study aimed at identifying industry 4.0 key technologies which have been ill-defined by previous researchers and to enumerate the required skills of industry 4.0. Comprehensive literature survey covering the field of engineering, production, and management was done in multidisciplinary databases: Google Scholar, Science Direct, Scopus, Sage, Taylor & Francis, and Emerald Insight. From the electronic survey, 35 disruptive technologies were quantified and 13 key technologies: Internet of Things, Big Data, 3D printing, Cloud computing, Autonomous robots, Virtual and Augmented reality, Cyber-physical system, Artificial intelligence, Smart sensors, Simulation, Nanotechnology, Drones, and Biotechnology were identified. Both technical and personal skills to be imparted into the human workforce for industry 4.0 were reported. The review identified the need to investigate the capability and the readiness of developing countries in adapting industry 4.0 in terms of the changes in the education systems and industrial manufacturing settings. This study proposes the need to address the integration of industry 4.0 concepts into the current education system.

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Section: Artificial Intelligence (Ai)mentioning

confidence: 99%

Exponential Disruptive Technologies and the Required Skills of Industry 4.0

Bongomin

Ocen

Nganyi

et al. 2020

Journal of Engineering

137

View full text Add to dashboard Cite

show abstract

“…The few works that exists [40,41,42] propose a private simulation tool for evaluating their approaches on topologies, power consumption, latency and so on. In other papers [43,44] the authors treat also the simulation by using TOSCA standard. But, the proposed solutions simulate the management operations in TOSCA services templates and they do not evaluate the elasticity or the cost of the infrastructure.…”

Section: Mde Approachesmentioning

confidence: 99%

“…The proposed MDE approach for cloud simulation based on OCCIware metamodel in this article is an initial step in this direction. [33,18,34] No No Yes No CloudMF [36] Yes No No No CMCloudSim [37] No No No Yes SimGrid [38], B. Javadi et al [39] No No No Limited EMDCloudSim [18] No No Yes Limited OtherSim [40,41,42,43,44] No Yes No No In this article, we have proposed a model-driven engineering approach based on the OCCIware metamodel and CloudSim toolkit. This approach takes the advantage of the MDE approach by allowing the simulation of any kind of cloud computing resources and by enabling the users to extend the simulation tool for other capabilities in a high level of abstraction and to model, edit and generate their application with minimum effort and time consuming.…”

Section: Mde Approachesmentioning

confidence: 99%

Model-Driven Simulation of Elastic OCCI Cloud Resources

Ahmed-Nacer

Kallel

Zalila

et al. 2020

The Computer Journal

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Deploying a cloud configuration in a real cloud platform is mostly cost- and time- consuming, as large number of cloud resources have to be rented for the time needed to run the configuration. Thereafter, cloud simulation tools are used as a cheap alternative to test cloud configuration. However, most of the existing cloud simulation tools require extensive technical skills and do not support simulation of any kind of cloud resources. In this context, using a model-driven approach can be helpful as it allows developers to efficiently describe their needs at a high level of abstraction. To do, we propose, in this article, a model-driven engineering approach based on the Open Cloud Computing Interface(OCCI) standard metamodel and CloudSim toolkit. We firstly extend OCCI metamodel for the supporting simulation of any kind of cloud resources. Afterward, to illustrate the extensibility of our approach, we enrich the proposed metamodel by new simulation capabilities. As proof of concept, we study the elasticity and pricing strategies of Amazon Web Services (AWS). This article benefits from OCCIware Studio to design an OCCI simulation extension and to provide a simulation designer for designing cloud configurations to be simulated. We detail the approach process from defining an OCCI simulation extension until the generation and the simulation of the OCCI cloud configurations. Finally, we validate the proposed approach by providing a realistic experimentation to study its usability, the resources coverage rate and the cost. The results are compared with the ones computed from AWS.

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“…The cloud server remotely guides the maintenance work on-site. However, given that most cloud servers are stationed far from terminal devices and users [Bonchi, Brogi, Canciani et al (2017)], the mobility of on-site maintenance personnel may lead to an intermittent Internet connection. Moreover, AR glasses require fast real-time performance.…”

Section: Related Workmentioning

confidence: 99%

Context-Based Intelligent Scheduling and Knowledge Push Algorithms for AR-Assist Communication Network Maintenance

Rui¹,

Qin²,

Li³

et al. 2019

Computer Modeling in Engineering &Amp; Sciences

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Maintenance is an important aspect in the lifecycle of communication network devices. Prevalent problems in the maintenance of communication networks include inconvenient data carrying and sub-optimal scheduling of work orders, which significantly restrict the efficiency of maintenance work. Moreover, most maintenance systems are still based on cloud architectures that slow down data transfer. With a focus on the completion time, quality, and load balancing of maintenance work, we propose in this paper a learning-based virus evolutionary genetic algorithm with multiple quality-ofservice (QoS) constraints to implement intelligent scheduling in an edge network. The algorithm maintains the diversity of the population and improves the speed of convergence using a fitness function and a learning-based population generation mechanism. The test results demonstrate that the algorithm delivers good performance in terms of load balancing and QoS guarantee. We also propose a knowledge push algorithm based on a context model for intelligently pushing relevant knowledge according to the given conditions. The simulation results demonstrate that our scheme can improve the efficiency of on-site maintenance.

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Simulation-based matching of cloud applications

Cited by 9 publications

References 23 publications

Exponential Disruptive Technologies and the Required Skills of Industry 4.0

Exponential Disruptive Technologies and the Required Skills of Industry 4.0

Model-Driven Simulation of Elastic OCCI Cloud Resources

Context-Based Intelligent Scheduling and Knowledge Push Algorithms for AR-Assist Communication Network Maintenance

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