Vertical/Horizontal Resource Scaling Mechanism for Federated Clouds

Shie, Meng-Ru; Lee, Yi-Fang; Lin, Yu‐Chun; Lai, Kuan‐Chou

doi:10.1109/icisa.2014.6847479

Cited by 14 publications

(15 citation statements)

References 8 publications

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“…Liu et al [5] focused on vertical and horizontal scaling mechanisms that allocate resources according to cost/ e f f iciency and the execution pattern of the applications. In horizontal scaling (scaling out/in), new VMs are added or released as needed.…”

Section: Related Workmentioning

confidence: 99%

VM auto-scaling methods for high throughput computing on hybrid infrastructure

et al. 2015

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Cloud computing provides on-demand resource provisioning and scalable resources dynamically for the efficient use of computing resources. Scientific applications recently need a very large number of loosely coupled tasks to be handled efficiently. In response, current computing environments often consist of heterogeneous resources such as cloud computing. To effectively use cloud resources, autoscaling methods that consider diverse metrics such as CPU utilization and costs of resource usage have been studied widely. However it still remains a challenge to automatically and timely allocate resources such that deadline violation and application types are considered. In this paper, we propose auto-scaling methods that consider specific conditions such as application types, task dependency, user-defined deadlines and data transfer times within a hybrid computing infrastructure. Our hybrid computing infrastructure consists of local cluster and cloud resources using HTCaaS. We observe noticeable improvements in performance when our auto-B Yoonhee Kim scaling methods for bag-of-tasks and workflow applications is applied.

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Section: Related Workmentioning

confidence: 99%

VM auto-scaling methods for high throughput computing on hybrid infrastructure

et al. 2015

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“…There are two types of scalability of information systems: vertical and horizontal or shared nothing [32].…”

Section: Scalabilitymentioning

confidence: 99%

Supercomputing Technologies as Drive for Development of Enterprise Information Systems and Digital Economy

Loginovsky

Shestakov

Shinkarev

2020

JSFI

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The article presents an analysis of approaches to the development of enterprise information systems that are in use today. One of the major trends that predetermines the agenda of information technology is the focus on parallel computing of large volumes of data using supercomputing technologies. The article considers the resulting ubiquitous move to distributed patterns of building enterprise information systems and avoiding monolithic architectures. The emphasis is placed on the importance of such fundamental characteristics of enterprise information systems as reliability, scalability, and maintainability. The article justifies the importance of machine learning in the context of effective big data analysis and competitive gain for business, vital for both maintaining a leading position in the market and surviving in conditions of global instability and digitalization of economy. Transition from storing the current state of a enterprise information system to storing a full log and history of all changes in the event stream is proposed as an instrument of achieving linearization of the data stream for subsequent parallel computing. There is a new view that is being shaped of specialists at the intersection of engineering and analytical disciplines, who would be able to effectively develop scalable systems and algorithms for data processing and integration of its results into company business processes.

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“…By increasing or decreasing the computing power of virtual machine is necessary for reducing the communication overhead. The focuses on the vertical /horizontal scaling approach for reducing the minimum turnaround time and for improving the cost efficiency [5].…”

Section: Introductionmentioning

confidence: 99%

CPU Resizing Vertical Scaling on Cloud

P¹,

Ellappan²,

Mehanathen³

2015

IJFCC

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Abstract-In a cloud computing, provisioned virtual machines are insufficient for computing the services it needs to be fine tuned by adding the additional resources or boosting the existing resources. A static method of resource allocation becomes inefficient under the different load. Based on the heavy load virtual machine can be adopted for dynamically changes of its behavior. There are two ways of scaling in the cloud scenario as horizontal scaling and vertical scaling. In a horizontal scaling, if the virtual machine resources are suffer from providing a service a new virtual machine was created and launched immediately. A threshold value has been maintained for scale out and scale down the virtual machine. Horizontal scaling is a traditional and well suitable approach for cloud computing environment. But the limitation of this approach is required separate load balancer to distribute the load between the virtual machines. In a vertical scaling resources are boosted by maximizing the virtual machine capabilities without shutting down the virtual machines. It's not like a traditional scaling approach to set the threshold and adding the resources. Here the resources are monitored by certain interval and based on the analysis resources are added to existing virtual machine. But lots of challenges are there in this approach. The limitation of this approach is we can't scale up the virtual machine up to the physical machine capability. Here we presented and analysis our approach for increasing the CPU capability of virtual machine. Likewise we can increase other resources like memory, vDisk and bandwidth. presented architecture resides on top of the virtual machine monitor and acts based on the scheduling algorithm.

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Vertical/Horizontal Resource Scaling Mechanism for Federated Clouds

Cited by 14 publications

References 8 publications

VM auto-scaling methods for high throughput computing on hybrid infrastructure

VM auto-scaling methods for high throughput computing on hybrid infrastructure

Supercomputing Technologies as Drive for Development of Enterprise Information Systems and Digital Economy

CPU Resizing Vertical Scaling on Cloud

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