Application-Aware Resource Allocation for SDN-based Cloud Datacenters

Hong, Weifan; Wang, Kuochen; Hsu, Yi-Huai

doi:10.1109/cloudcom-asia.2013.44

Cited by 20 publications

(6 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…App-RA [146] is an OpenFlow-based network resource allocation that uses a neural network to forecast the number of resources that the application needs in a cloud data center and then assign appropriate VMs to satisfy SLA violations and maximize power savings with the help of CICQ switches [147]. Further, to fulfill the network requirements of various applications, App-RS [148] proposed allocating network resources depending on network requirements parameters of each application using the Lagrange Relaxation based aggregated cost Dijkstra algorithm.…”

Section: Application-aware Resource Allocationmentioning

confidence: 99%

Software-defined networks for resource allocation in cloud computing: A survey

et al. 2021

View full text Add to dashboard Cite

Section: Application-aware Resource Allocationmentioning

confidence: 99%

Software-defined networks for resource allocation in cloud computing: A survey

et al. 2021

View full text Add to dashboard Cite

“…DCs (Data Centers) need intelligent intra-DC and inter-DC traffic control techniques. Therefore, the control plane, which is in charge of managing policies and the data plane, must take great care with aspects such as: availability, maturity, operator preference and functional requirements; which in turn can be: intra-DC traffic that must be flexible to control, adaptive to forwarded entries and dynamic context policies [50]. Studies have focused on control plane options that are tailored to data center requirements, such as a flexible Open Flow protocol for network and open interface control, a generalized multiprotocol label change (GMPLS) with optional Route Calculation (PCE) [51], as it offers maturity, operator-grade, and multi-domain support to control optical networks, slow migrations, and economic return; through the heterogeneous control plane that integrates GMPLS, PCE and SDN.…”

Section: Data Centersmentioning

confidence: 99%

Security in SDN networks and their applications

Tapiero¹

2021

ing. Solidar

View full text Add to dashboard Cite

Introduction: The review article is the product of the research on Security in SDN networks and their applications, developed at the District University in 2020, presenting the latest advances, that have been made in security. Problem: The security weaknesses that SDN networks have had, due to being a new architecture. This has not allowed traditional networks to be replaced. Objective: To carry out a review of the state of the art of SDN networks, focusing research on the security of the control layer and its advances. Methodology: The descriptive method is implemented, consulting databases such as Scopus, IEEE and ScienceDirect, using the following search criteria: SDN networks, security in SDN networks, applications with SDN networks and OpenFlow protocol. It is shown as a research sample: the Asian, European and American continents with years of research from 2014 to 2020. Results: Great advances have been made in terms of security for SDN networks, which allows us to see an early solution to the weaknesses that it currently faces. Conclusion: SDN networks will solve all the challenges they face and will be consolidated as a solid and reliable architecture. Originality: an important focus is taken on the security of SDN networks and the great development that has occurred in this regard is evident. Limitations: SDN networks are a new architecture, so their development has been very little and advances in security have been significantly affected.

show abstract

“…When using the good features of SDN in a Big Data environment, it is expected to be able to benefit its applications in several visible aspects such as those shown in Figure 10, including: (1) Big data processing in Cloud DCs, (2) improvement in data delivery, (3) runtime programming for application optimization, (4) Big Data scientific architectures and (5) Hadoop programming 28 [54]. In this regard [55], [56], [57], [58], [59] include the benefits provided by SDN characteristics for Big data applications.…”

Section: Big Datamentioning

confidence: 99%

“…As explained in [55], where a Cloud DC based on SDN is studied for Big data applications, a model based on SDN / OpenFlow is established, with switches (CICQ) 29 and an App-RA 30 , which allows an allocation of efficient resources and a reduction in energy consumption for each application within the DC.…”

Section: Big Datamentioning

confidence: 99%

State of the art in software defined networking (SDN)

Pérez¹,

Losada²,

Rojas³

et al. 2019

Vis. Electron.

View full text Add to dashboard Cite

The growth of networks at a global level is inevitable due to the increase of users, devices and applications, such as: Internet of Things (IoT), processing and analysis of large amounts of information (Big Data), or streaming audio and video, which has demanded from the systems, greater storage resources and bandwidth. To this purpose, diverse paradigms have emerged for the centralized management of all the components of a network through fully administrable, centralized and dynamic technological platforms; among these is SDN (Software-Defined Networks). This document, consequently, establishes the state-of-art from a documentary research of a categorical type to be used as a frame of reference for research in the area of SDN by the Research Group of New Technologies of Social Application GIDENUTAS ascribed to the University Francisco Jose de Caldas. This is chronologically limited to a review, from 2007 until today, focused on the countries that have promoted the development and implementation of this new paradigm, using databases such as IEEE Xplore, Google Scholar, as well as documents from standardization organizations such as ONF and ITU.

show abstract

Application-Aware Resource Allocation for SDN-based Cloud Datacenters

Cited by 20 publications

References 5 publications

Software-defined networks for resource allocation in cloud computing: A survey

Software-defined networks for resource allocation in cloud computing: A survey

Security in SDN networks and their applications

State of the art in software defined networking (SDN)

Contact Info

Product

Resources

About