Priority-Aware VM Allocation and Network Bandwidth Provisioning in Software-Defined Networking (SDN)-Enabled Clouds

Son, Jungmin; Buyya, Rajkumar

doi:10.1109/tsusc.2018.2842074

Cited by 48 publications

(26 citation statements)

References 25 publications

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“…CloudSimSDN was introduced in 2015 to provide simulation framework for SDN functionalities on the cloud computing environment. After the first presentation, it is utilized for evaluations in multiple projects, such as oversubscription‐based VM and network allocation policy, and priority‐aware VM allocation policy considering network topology . The tool can simulate dynamic network flow scheduling, joint VM and network optimization, and monitoring CPU and network utilization in both physical host level and VM level.…”

Section: Related Workmentioning

confidence: 99%

“…After the first presentation, it is utilized for evaluations in multiple projects, such as oversubscription-based VM and network allocation policy, 12 and priority-aware VM allocation policy considering network topology. 13 The tool can simulate dynamic network flow scheduling, joint VM and network optimization, and monitoring CPU and network utilization in both physical host level and VM level. Although it provides extensive evaluation of network functionality with potential expandability, it is lack of supporting NFV and edge computing models.…”

Section: Related Workmentioning

confidence: 99%

“…We developed and presented CloudSimSDN in 2015 to simulate SDN functionalities in cloud computing. CloudSimSDN has helped to simulate different allocation and provisioning policies for both computing and networking resources, as well as the workload and application‐aware scheduling methods in clouds . In addition to the basic networking functionality supported by SDN in CloudSimSDN, this paper presents the design and implementation of new features to support NFV functionalities, such as VNF allocation, migration, and autoscaling.…”

Section: Introductionmentioning

confidence: 99%

“…CloudSimSDN has helped to simulate different allocation and provisioning policies for both computing and networking resources, 12 as well as the workload and application-aware scheduling methods in clouds. 13 In addition to the basic networking functionality supported by SDN in CloudSimSDN, this paper presents the design and implementation of new features to support NFV functionalities, such as VNF allocation, migration, and autoscaling. To facilitate these functionalities, the simulation framework is based on the mapping of architecture and components of ETSI NFV management and orchestration (MANO), 14 which includes NFV orchestrator (NFVO), VNF manager (VNFM), and virtual infrastructure manager (VIM).…”

Section: Introductionmentioning

confidence: 99%

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CloudSimSDN‐NFV: Modeling and simulation of network function virtualization and service function chaining in edge computing environments

Son

Buyya

2019

Softw Pract Exp

Self Cite

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Summary Software‐defined networking (SDN) has evolved and brought an innovative paradigm shift in computer networks by utilizing a programmable software controller with open protocols. Network functions, previously served on dedicated hardware, have shifted to network function virtualization (NFV) that enabled functions to be virtualized and provisioned dynamically upon generic hardware. In addition to NFV, edge computing utilizes the edge resources close to end‐users, which can reduce the end‐to‐end service delay and the network traffic volume. Although these innovative technologies gained significant attention from both academia and industry, there are limited tools and simulation frameworks for the effectiveness evaluation in a repeatable and controllable manner. Furthermore, large‐scale experimental infrastructures are expensive to setup and difficult to maintain. Even if they are created, they are not available or accessible for the majority of researchers throughout the world. In this paper, we propose a framework for simulating NFV functionalities in both edge and cloud computing environments. In addition to the basic network functionalities supported by SDN in CloudSimSDN, we added new NFV features, such as virtualized network functions allocation, migration, and autoscaling with the support of corresponding network functionalities, such as flow load balancing, rerouting, and service function chaining (SFC) maintenance. We evaluated our simulation framework with autoscaling and placement policies for SFC in the integrated edge and cloud computing environments. The results demonstrate its effectiveness in measuring and evaluating the end‐to‐end delay, response time, resource utilization, network traffic, and power consumption with different algorithms in each scenario.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

CloudSimSDN‐NFV: Modeling and simulation of network function virtualization and service function chaining in edge computing environments

Son

Buyya

2019

Softw Pract Exp

Self Cite

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“…In addition, computer NRS based cloud reduces energy consumption while optimizing network virtualization. Virtualization Network Features (VNF) is another emerging paradigm network that transmits network functions such as load balancing and intrusion detection Domain Name Service (DNS) when running software applications [75]. Moreover, VNF Computer Engineering and Intelligent Systems www.iiste.org ISSN 2222-1719 (Paper) ISSN 2222-2863 (Online) Vol.…”

Section: Software-defined Networkmentioning

confidence: 99%

The Emergence of AI and IoT on Cloud Computing: Evolution, Technology, Future Research and Challenges

2019

CEIS

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Cloud computing is being widely promoted as the following paradigms and business solutions to the problems that most enterprise Information Technology (IT). Today it plays an important role in modern society and allows a wide range of applications from the infrastructure of social networks such system must cope with the increasing use of various loads and reflects the dependence of the interaction and community automated computer systems while the satisfactory quality of service (QoS) guarantees. Cloud computing is beneficial to the owner of the company, as it eliminates the need for operators to plan ahead for the provision of resources, enabling organizations to start small and increase the resource only if there is an increased demand for services. To understand the current and future challenges of a system of this type, there is a need to identify the key technology for future applications. This work aims to study the history of computing and identify how the use of Artificial intelligence (AI) and Internet of things (IoT) influence cloud computing in order to solve complex problems in the next generation of computing. In addition, the influence of new paradigms and technologies on Cloud Computing were identified and discussed with future research opportunities and an open challenge stand out. Furthermore, this article also provides a conceptual model for cloud futurologist to explore the influence that paradigms and emerging technologies to the evolution of cloud computing are proposed.

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Edge‐driven ubiquitous wireless communication for robust cluster intelligent piano presentation platform considering system security

Wang¹

2022

Int J Communication

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Summary With the popularization of 6G‐driven wireless access networks and the development of edge computing, the connection between piano performance and artificial intelligence algorithms is getting closer and closer. 6G was first used to solve large‐capacity, short‐latency communication problems, and research results in this field abound. However, in practical applications, the deployment of 6G high‐frequency network is a major problem faced by the industry. This paper mainly studies the artificial robust cluster piano demonstration platform from the perspective of the fusion of 6G and edge computing. We firstly consider that the edge computing is a technology in which artificial intelligence algorithms are transplanted to edge devices and chips. The vision of the 6G network is to realize ubiquitous wireless intelligence, which includes three dimensions: mobile ultra‐broadband, super Internet of Things, and artificial intelligence, and realizes the conversion of piano scores into digital signals. Artificial Robust Clustering is a comprehensive technique that incorporates many disciplines, including phonetics, artificial intelligence, computer science, and more. This paper analyzes the fusion technology of 6G and edge computing and studies the robust cluster intelligent piano demonstration platform. The mentioned technologies are combined together to make the designed efficient.

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Priority-Aware VM Allocation and Network Bandwidth Provisioning in Software-Defined Networking (SDN)-Enabled Clouds

Cited by 48 publications

References 25 publications

CloudSimSDN‐NFV: Modeling and simulation of network function virtualization and service function chaining in edge computing environments

CloudSimSDN‐NFV: Modeling and simulation of network function virtualization and service function chaining in edge computing environments

The Emergence of AI and IoT on Cloud Computing: Evolution, Technology, Future Research and Challenges

Edge‐driven ubiquitous wireless communication for robust cluster intelligent piano presentation platform considering system security

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