Virtual Infrastructure Planning in Optical Networks for Cloud Computing

Many organizations, both large and small, are investigating the potential of storage architectures for their companies. Few years ago, we built our own virtualized cloud for REMLABNET and we still are taking benefits of this decision. This item handels with using Cloud computing platform for providing Remote laboratories. This work shows, how it is possible to save money if we use centralized system for more consumers. Every consumer can use access to centralized portal in the Cloud computing from Consortium REMLABNET. Every item is focused on enviroments of universities, where this cloud is existing and this is what we want to use for remote labs. This is item from practice knowledge and experiences about system function and managing virtual platform and next construction this proposal.

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Private Virtual Infrastructure: A Model for Trustworthy Utility Cloud Computing

10.21236/ada545050 ◽

2010 ◽

Cited By ~ 5

Author(s):

F. J. Krautheim ◽

Dhananjay S. Phatak ◽

Alan T. Sherman

Keyword(s):

Cloud Computing ◽

Virtual Infrastructure

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Research Progress of Next Generation Optical Networks Technology for Cloud Computing

Laser & Optoelectronics Progress ◽

10.3788/lop52.070003 ◽

2015 ◽

Vol 52 (7) ◽

pp. 070003

Author(s):

李明 Li Ming ◽

张引发 Zhang Yinfa ◽

任帅 Ren Shuai ◽

王鲸鱼 Wang Jingyu ◽

廖晓敏 Liao Xiaomin

Keyword(s):

Cloud Computing ◽

Optical Networks ◽

Research Progress ◽

Next Generation

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Stochastic Virtual Infrastructure Planning in Elastic Cloud Deploying Optical Networking

Optical Fiber Communication Conference ◽

10.1364/ofc.2012.ow1a.3 ◽

2012 ◽

Cited By ~ 5

Author(s):

Markos P. Anastasopoulos ◽

Anna Tzanakaki ◽

Konstantinos Georgakilas

Keyword(s):

Optical Networking ◽

Infrastructure Planning ◽

Virtual Infrastructure

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Research on the Resource Joint Scheduling in Optical Networks for Cloud Computing

2014 7th International Conference on Intelligent Computation Technology and Automation ◽

10.1109/icicta.2014.149 ◽

2014 ◽

Author(s):

Li Ming ◽

Zhang Yinfa ◽

Ren Shuai ◽

Wang Peng ◽

Wang Kun

Keyword(s):

Cloud Computing ◽

Optical Networks

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A Comparative Analysis of NSGA-II and NSGA-III for Autoscaling Parameter Sweep Experiments in the Cloud

Scientific Programming ◽

10.1155/2020/4653204 ◽

2020 ◽

Vol 2020 ◽

pp. 1-17

Author(s):

Virginia Yannibelli ◽

Elina Pacini ◽

David Monge ◽

Cristian Mateos ◽

Guillermo Rodriguez

Keyword(s):

Cloud Computing ◽

Optimization Algorithm ◽

Virtual Machines ◽

State Of The Art ◽

Multiobjective Evolutionary Algorithms ◽

Nsga Ii ◽

Trade Off ◽

Computing Paradigm ◽

Virtual Infrastructure ◽

And Storage

The Cloud Computing paradigm is focused on the provisioning of reliable and scalable virtual infrastructures that deliver execution and storage services. This paradigm is particularly suitable to solve resource-greedy scientific computing applications such as parameter sweep experiments (PSEs). Through the implementation of autoscalers, the virtual infrastructure can be scaled up and down by acquiring or terminating instances of virtual machines (VMs) at the time that application tasks are being scheduled. In this paper, we extend an existing study centered in a state-of-the-art autoscaler called multiobjective evolutionary autoscaler (MOEA). MOEA uses a multiobjective optimization algorithm to determine the set of possible virtual infrastructure settings. In this context, the performance of MOEA is greatly influenced by the underlying optimization algorithm used and its tuning. Therefore, we analyze two well-known multiobjective evolutionary algorithms (NSGA-II and NSGA-III) and how they impact on the performance of the MOEA autoscaler. Simulated experiments with three real-world PSEs show that MOEA gets significantly improved when using NSGA-III instead of NSGA-II due to the former provides a better exploitation versus exploration trade-off.

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