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

Son, Jungmin; He, TianZhang; Buyya, Rajkumar

doi:10.1002/spe.2755

Cited by 36 publications

(30 citation statements)

References 32 publications

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“…In this section, the results of the conducted experimental studies for the proposed solution are presented. The CloudSimSDN-NFV simulator [62] was used in these studies. The results collected from the proposed algorithm are compared against the least full first (LFF) strategy [62].…”

Section: Resultsmentioning

confidence: 99%

“…The CloudSimSDN-NFV simulator [62] was used in these studies. The results collected from the proposed algorithm are compared against the least full first (LFF) strategy [62]. The LFF strategy tries to place the VNF on the least loaded server node, which has the least amount of resources allocated in terms of computing energy and network bandwidth.…”

Section: Resultsmentioning

confidence: 99%

“…Figure 3 shows the average link bandwidth utilization of each level when the VNFRP algorithm was adopted and the corresponding values for the LFF strategy [62]. Both algorithms were evaluated using different VNF replicas.…”

Section: Network Load Balancing and Link Bandwidth Utilizationmentioning

confidence: 99%

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Efficient Placement of Service Function Chains in Cloud Computing Environments

2021

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The widespread adoption of network function virtualization (NFV) leads to providing network services through a chain of virtual network functions (VNFs). This architecture is called service function chain (SFC), which can be hosted on top of commodity servers and switches located at the cloud. Meanwhile, software-defined networking (SDN) can be utilized to manage VNFs to handle traffic flows through SFC. One of the most critical issues that needs to be addressed in NFV is VNF placement that optimizes physical link bandwidth consumption. Moreover, deploying SFCs enables service providers to consider different goals, such as minimizing the overall cost and service response time. In this paper, a novel approach for the VNF placement problem for SFCs, called virtual network functions and their replica placement (VNFRP), is introduced. It tries to achieve load balancing over the core links while considering multiple resource constraints. Hence, the VNF placement problem is first formulated as an integer linear programming (ILP) optimization problem, aiming to minimize link bandwidth consumption, energy consumption, and SFC placement cost. Then, a heuristic algorithm is proposed to find a near-optimal solution for this optimization problem. Simulation studies are conducted to evaluate the performance of the proposed approach. The simulation results show that VNFRP can significantly improve load balancing by 80% when the number of replicas is increased. Additionally, VNFRP provides more than a 54% reduction in network energy consumption. Furthermore, it can efficiently reduce the SFC placement cost by more than 67%. Moreover, with the advantages of a fast response time and rapid convergence, VNFRP can be considered as a scalable solution for large networking environments.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Efficient Placement of Service Function Chains in Cloud Computing Environments

2021

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“…The emergence of modern technologies such as Cloud Computing, Software Defined Networking (SDN), Network Functions Virtualization (NFV), Edge Computing and the Internet of Things (IoT) have rapidly transformed end-to-end service provisioning over the past decade [1] and have smart services for subscription. On -demand cloud user requests with well-defined Service Level Agreements (SLA) often have different Quality of Service (QoS) requirements for processing and delivery.…”

Section: Introductionmentioning

confidence: 99%

CloudSimHypervisor: Modeling and Simulating Network Slicing in Software-Defined Cloud Networks

Nyanteh

Abbod

et al. 2021

IEEE Access

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Software-Defined Networking (SDN) is an innovative technology which provides a programmable network control which is decoupled from the physical infrastructure. Network Virtualization (NV) is the phenomenon where a given physical network infrastructure and its resources are abstracted to create multiple logical virtual network slices of the underlying substrate. NV enables independent virtual networks to co-exist on one or more shared physical network infrastructure. Edge computing makes use of the edge resources in close proximity to end-users to reduce service delay and the network traffic volume in the end-to-end networks. Similarly, network slicing which is a key enabling technology for 5G networks is designed to support different services from different platforms at different scales enables sharing of physical network infrastructure on many different virtual network layers. These innovative technologies and strategies have gained significant attention from both academia and industry as they have the potential to maximize network resource utilization and optimize end-to-end network service delivery in 5G solutions deployment. To enable continuous simulation and development of applicable 5G networking concepts using these technologies, there is a need for an accessible and easy-to-learn testbed which is able to efficiently measure the performance of physical and virtual network capacities, provisioning approaches and management of multiple architectural models using large-scale network slicing configurations in a repeatable and controllable manner. These tools and toolkits provide scalable, lightweight and controlled cloud simulation environments necessary to analyse network traffic flows, allocation capacities and policies and the behaviour of multiple heterogeneous networks at an extremely low cost as compared to the huge financial commitments involved in conducting similar experiments in a real-life event. Existing solutions do not support Network Slicing and end-to -end heterogenous network automation which are key enablers of 5G network implementation. Hence in this paper, the CloudSimHypervisor framework is developed in this based on CloudSimSDN-NFV. The complete architecture and features of the CloudSimHypervisor framework and some used cases are presented in this paper. We validate the CloudSimHypervisor framework with two use case experiments in the cloud computing environment: Joint compute and network resource utilization and network traffic prioritization. Results from these experiments display the efficiency of the CloudSimHypervisor in estimating and measuring processing speed, transmission speed, compute and network usage efficiency and energy consumption.

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“…1 Infrastructure as a service (IaaS) is an important service model of the cloud computing paradigm in which providers allocate virtual computing resources as virtual machines (VMs) on physical machines (PMs) to customers over the internet. [2][3][4] According to Gartner, 5 the market for public cloud will reach over $306.9 billion at the end of 2021 and $364 billion at the end of 2022. Gartner expects the hyperscale providers, such as Amazon AWS, Google Cloud Platform, Alibaba, and Microsoft Azure, to account for 70% of the market by the end of 2021.…”

mentioning

confidence: 99%

Availability modeling in redundant OpenStack private clouds

et al. 2021

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In cloud computing services, high availability is one of the quality of service requirements which is necessary to maintain customer confidence. High availability systems can be built by applying redundant nodes and multiple clusters in order to cope with software and hardware failures. Due to cloud computing complexity, dependability analysis of the cloud may require combining state-based and nonstate-based modeling techniques. This article proposes a hierarchical model combining reliability block diagrams and continuous time Markov chains to evaluate the availability of OpenStack private clouds, by considering different scenarios. The steady-state availability, downtime, and cost are used as measures to compare different scenarios studied in the article. The heterogeneous workloads are considered in the proposed models by varying the number of CPUs requested by each customer. Both hardware and software failure rates of OpenStack components used in the model are collected via setting up a real OpenStack environment applying redundancy techniques. Results obtained from the proposed models emphasize the positive impact of redundancy on availability and downtime. Considering the tradeoff between availability and cost, system providers can choose an appropriate scenario for a specific purpose. K E Y W O R D S availability modeling, cloud computing, continuous time Markov chains, hierarchical models, reliability block diagrams INTRODUCTIONCloud computing includes different resources of one or more data centers, such as storage, network and compute, shared through virtualization technology. 1 Infrastructure as a service (IaaS) is an important service model of the cloud computing paradigm in which providers allocate virtual computing resources as virtual machines (VMs) on physical machines (PMs) to customers over the internet. [2][3][4] According to Gartner, 5 the market for public cloud will reach over $306.9 billion at the end of 2021 and $364 billion at the end of 2022. Gartner expects the hyperscale providers, such as Amazon AWS, Google Cloud Platform, Alibaba, and Microsoft Azure, to account for 70% of the market by the end of 2021. 6 Large organization and government agencies usually like to develop their own cloud data centers for satisfying their needs. By contrast, small organizations usually rent cloud resources from cloud providers to solve their own problems. 7

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

Cited by 36 publications

References 32 publications

Efficient Placement of Service Function Chains in Cloud Computing Environments

Efficient Placement of Service Function Chains in Cloud Computing Environments

CloudSimHypervisor: Modeling and Simulating Network Slicing in Software-Defined Cloud Networks

Availability modeling in redundant OpenStack private clouds

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