scholarly journals Private Virtual Infrastructure: A Model for Trustworthy Utility Cloud Computing

2010 ◽  
Author(s):  
F. J. Krautheim ◽  
Dhananjay S. Phatak ◽  
Alan T. Sherman
Keyword(s):  
Cloud Computing ◽  
Virtual Infrastructure

scholarly journals Road to Strengthen of Virtual Infrastructure and Security of Remote Laboratories on Trnava University in Trnava

2020 ◽  
Vol 16 (12) ◽  
pp. 33
Author(s):  
Pavel Beňo ◽  
František Schauer ◽  
Sandra Šprinková ◽  
Miroslav Šimko ◽  
Tomáš Komenda
Keyword(s):  
Cloud Computing ◽  
System Function ◽  
Computing Platform ◽  
Virtual Infrastructure ◽  
Remote Laboratories ◽  
Remote Labs ◽  
Cloud Computing Platform ◽  
Practice Knowledge ◽  
Virtual Platform ◽  
Centralized System

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.

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

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.

scholarly journals Optimization of System Framework for Secure Communication and Data Virtualization on Cloud Computing

2020 ◽  
Vol 9 (11) ◽  
pp. 399-405
Keyword(s):  
Cloud Computing ◽  
Access Control ◽  
Data Storage ◽  
Resource Utilization ◽  
Secure Communication ◽  
Communication Model ◽  
Dynamic Allocation ◽  
Computing Model ◽  
Virtual Infrastructure ◽  
Data Virtualization

The consequent deployment of vital infrastructure to provide the secure communication and virtualization not only rectifies the challenges and difficulties but also benefits with saving the process of digitization. With a bang in evolution of cloud computing the uniqueness of every organization affects the virtualization of its information communication technology and applications. The answer for such organizations is to stipulate the precise cloud computing model equivalent to the deliberated and operational goals. This research article emphasized at budding and optimizing a framework for virtual infrastructure and cloud computing model to support the organization stakeholders. Moreover it also improves the cloud security with a protected virtual infrastructure and communication model with better performance. On account of data storage security necessities and distinctiveness of cloud computing environment, a method for secured data & storage based on dynamic allocation and access control mechanism is presented. Dynamic resource allocation is applied for resource utilization that focused on data virtualization and memory virtualization, for protection and access control a modified KPABE algorithm is integrated. The combination of these methods resulting on the optimize resource use, centralizing of storage. The implementation and comparison of results revealed that the proposed modified KP-ABE has performed effectively than the other security standards and especially for resource utilization. The proposed method serves for efficient data storage, access control solutions and computation in cloud environment

A Virtual Machine Placement and Reconfiguration Framework for Cloud Computing Platforms

2014 ◽  
Vol 5 (2) ◽  
pp. 1-22
Author(s):  
Wei Chen ◽  
Xiaoqiang Qiao ◽  
Jun Wei ◽  
Hua Zhong ◽  
Tao Huang
Keyword(s):  
Cloud Computing ◽  
Resource Utilization ◽  
Optimization Method ◽  
Vm Placement ◽  
On Demand ◽  
Virtual Infrastructure ◽  
Runtime Reconfiguration ◽  
Computing Platforms ◽  
Multiple Resource ◽  
The Cost

As a rising application paradigm and technology, cloud computing can leverage the efficient pooling of on-demand, self-managed virtual infrastructure. How to maximize the resource utilization and how to reduce the cost of configuration are essential issues in cloud computing. In this paper, the authors propose a framework to achieve these objectives by optimizing VM placement and deciding when and how to perform the VM reconfigurations. The authors leverage the vector arithmetic to model the objective of balancing the multiple resource utilization and propose an optimization method for the static VM placement. Then the authors propose a two-level runtime reconfiguration policy, including the local adjustment and the parallel migration, to minimize the reconfiguration cost. Finally, the authors implement a prototype to validate and evaluate the proposed mechanism with a set of preliminary experiments, which shows that our work can maximize the resource utilization while effectively reducing the cost of the runtime reconfiguration.

scholarly journals Multilayered Autoscaling Performance Evaluation: Can Virtual Machines and Containers Co–Scale?

2019 ◽  
Vol 29 (2) ◽  
pp. 227-244 ◽  
Author(s):  
Vladimir Podolskiy ◽  
Anshul Jindal ◽  
Michael Gerndt
Keyword(s):  
Cloud Computing ◽  
Performance Evaluation ◽  
Cost Reduction ◽  
Virtual Machines ◽  
Measurement Tool ◽  
Image Size ◽  
Significant Cost ◽  
Virtual Infrastructure ◽  
Significant Cost Reduction ◽  
A Company

Abstract The wide adoption of cloud computing by businesses is due to several reasons, among which the elasticity of the cloud virtual infrastructure is the definite leader. Container technology allows increasing the flexibility of an application by adding another layer of virtualization. The containers can be dynamically created and terminated, and also moved from one host to another. A company can achieve a significant cost reduction and increase the manageability of its applications by allowing the running of containerized microservice applications in the cloud. Scaling for such solutions is conducted on both the virtual infrastructure layer and the container layer. Scaling on both layers needs to be synchronized so that, for example, the virtual machine is not terminated with containers still running on it. The synchronization between layers is enabled by multilayered cooperative scaling, implying that the autoscaling solution of the virtual infrastructure layers is aware of the decisions of the autoscaling solution on the container layer and vice versa. In this paper, we introduce the notion of cooperative multilayered scaling and the performance of multilayered autoscaling solutions evaluated using the approach implemented in ScaleX (previously known as Autoscaling Performance Measurement Tool, APMT). We provide the results of the experimental evaluation of multilayered autoscaling performance for the combination of virtual infrastructure autoscaling of AWS, Microsoft Azure and Google Compute Engine with pods horizontal autoscaling of Kubernetes by using ScaleX with four distinct load patterns. We also discuss the effect of the Docker container image size and its pulling policy on the scaling performance.

Virtual Infrastructure Planning in Optical Networks for Cloud Computing

2015 ◽  
Vol 52 (5) ◽  
pp. 050604
Author(s):  
张引发 Zhang Yinfa ◽  
李明 Li Ming ◽  
任帅 Ren Shuai ◽  
王鲸鱼 Wang Jingyu ◽  
王坤 Wang Kun
Keyword(s):  
Cloud Computing ◽  
Optical Networks ◽  
Infrastructure Planning ◽  
Virtual Infrastructure

scholarly journals Services for Implementation of Cloud Technologies for Education

2015 ◽  
Vol 4 ◽  
pp. 383
Author(s):  
Česlovas Christauskas ◽  
Regina Misevičienė
Keyword(s):  
Cloud Computing ◽  
Virtual Infrastructure ◽  
Computing Technologies ◽  
University Of Technology ◽  
Cloud Technologies ◽  
Reduction Of Costs ◽  
Computing Models

<p><em>In this paper, we analyzed the cloud computing technologies presenting overview of cloud computing models and possibilities of virtual infrastructure desktops and their benefits. We studied reduction of costs and savings of virtual desktops. Virtual desktops in Kaunas University of Technology we presented at the end.</em></p>

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