Placement and Performance Analysis of Virtual Multicast Networks in Fat-Tree Data Center Networks

Duan, Jun; Yang, Yuanyuan

doi:10.1109/tpds.2015.2514285

Cited by 14 publications

(7 citation statements)

References 30 publications

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“…It is to be noted that equal importance is given to both types of the resources. For better understanding, Let us assume an example of a virtual network consisting of three VMs as a, b, and c with memory and CPU resource demand of (10, 2), (23,3), and (15, 1) units, respectively. Let, VM a, b, and c be deployed on PM A, B, and C, respectively.…”

Section: Pm Failure Impactmentioning

confidence: 99%

“…Extensive researches have been emphasized on different aspects of VNE problem, such as energy-aware [15] location constraint [16], physical network topology [3], etc. Considering the location preferences of the users, authors in [16], formulate the VNE problem as a graph bisection problem, which achieves integrated VM and virtual link mapping.…”

Section: Related Workmentioning

confidence: 99%

“…Fat-Tree [2] network topology is widely used in commercial data centers in order to establish the connection among PMs through core switches, aggregation switches and edge switches as the basic connecting devices. The resources of the PMs are provided to the tenants by creating multiple Virtual Machines (VMs), ensuring the basic privacy properties, better QoS and other user requirements as mentioned at the time of Service Level Agreement (SLA) establishment between the tenant and the IP [3].…”

Section: Introductionmentioning

confidence: 99%

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Failure Aware Semi-Centralized Virtual Network Embedding in Cloud Computing Fat-Tree Data Center Networks

Dehury

Sahoo

2022

IEEE Trans. Cloud Comput.

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In Cloud Computing, the tenants opting for the Infrastructure as a Service (IaaS) send the resource requirements to the Cloud Service Provider (CSP) in the form of Virtual Network (VN) consisting of a set of inter-connected Virtual Machines (VM). Embedding the VN onto the existing physical network is known as Virtual Network Embedding (VNE) problem. One of the major research challenges is to allocate the physical resources such that the failure of the physical resources would bring less impact onto the users' service. Additionally, the major challenge is to handle the embedding process of growing number of incoming users' VNs from the algorithm design point-of-view. Considering both of the above-mentioned research issues, a novel Failure aware Semi-Centralized VNE (FSC-VNE) algorithm is proposed for the Fat-Tree data center network with the goal to reduce the impact of the resource failure onto the existing users. The impact of failure of the Physical Machines (PMs), physical links and network devices are taken into account while allocating the resources to the users. The beauty of the proposed algorithm is that the VMs are assigned to different PMs in a semi-centralized manner. In other words, the embedding algorithm is executed by multiple physical servers in order to concurrently embed the VMs of a VN and reduces the embedding time. Extensive simulation results show that the proposed algorithm can outperform over other VNE algorithms.

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Section: Pm Failure Impactmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Failure Aware Semi-Centralized Virtual Network Embedding in Cloud Computing Fat-Tree Data Center Networks

Dehury

Sahoo

2022

IEEE Trans. Cloud Comput.

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“…2) Multicast in DCs: DC multicast has been studied from a different point of views. For example, frameworks proposed in [19], [20] studied the resource allocation and embedding of multicast virtual networks. Mainly they focused on how to place and restore VMs to provide high performance nonblocking multicast virtual networks while reducing hardware cost in Fat-Tree DCs.…”

Section: Limitations Of the State Of The Artmentioning

confidence: 99%

Bert: Scalable Source Routed Multicast for Cloud Data Centers

Alqahtani

Hamdaoui

2020

2020 International Wireless Communications and Mobile Computing (IWCMC)

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Traditional IP multicast routing is not suitable for cloud data center (DC) networks due to the need for supporting large numbers of groups with large group sizes. State-of-the-art DC multicast routing approaches aim to overcome the scalability issues by, for instance, taking advantage of the symmetry of DC topologies and the programmability of DC switches to compactly encode multicast group information inside packets, thereby reducing the overhead resulting from the need to store the states of flows at the network switches. However, although these scale well with the number of multicast groups, they do not do so with group sizes, and as a result, they yield substantial traffic control overhead and network congestion. In this paper, we present Bert, a scalable, source-initiated DC multicast routing approach that scales well with both the number and the size of multicast groups, and does so through clustering, by dividing the members of the multicast group into a set of clusters with each cluster employing its own forwarding rules. Compared to the state-of-the-art approach, Bert yields much lesser traffic control overhead by significantly reducing the packet header sizes and the number of extra packet transmissions, resulting from the need for compacting forwarding rules across the switches.

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“…The fat-tree DCN, as a special instance and variation of the Clos networks, has been widely adopted as the topology for DCNs since it can build large-scale traffic networks by only using fewer switches [1].…”

Section: Introductionmentioning

confidence: 99%

Application and Analysis of Multicast Blocking Modelling in Fat‐Tree Data Center Networks

Guo

Liu

et al. 2018

Complexity

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Multicast can improve network performance by eliminating unnecessary duplicated flows in the data center networks (DCNs). Thus it can significantly save network bandwidth. However, the network multicast blocking may cause the retransmission of a large number of data packets and seriously influence the traffic efficiency in data center networks, especially in the fat-tree DCNs with multirooted tree structure. In this paper, we build a multicast blocking model and apply it to solve the problem of network blocking in the fat-tree DCNs. Furthermore, we propose a novel multicast scheduling strategy. In the scheduling strategy, we select the uplink connecting to available core switch whose remaining bandwidth is close to and greater than the three times of bandwidth multicast requests so as to reduce the operation time of the proposed algorithm. Then the blocking probability of downlink in the next time-slot is calculated in multicast subnetwork by using Markov chains theory. With the obtained probability, we select the optimal downlink based on the available core switch. In addition, theoretical analysis shows that the multicast scheduling algorithm has close to zero network blocking probability as well as lower time complexity. Simulation results verify the effectiveness of our proposed multicast scheduling algorithm.

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Placement and Performance Analysis of Virtual Multicast Networks in Fat-Tree Data Center Networks

Cited by 14 publications

References 30 publications

Failure Aware Semi-Centralized Virtual Network Embedding in Cloud Computing Fat-Tree Data Center Networks

Failure Aware Semi-Centralized Virtual Network Embedding in Cloud Computing Fat-Tree Data Center Networks

Bert: Scalable Source Routed Multicast for Cloud Data Centers

Application and Analysis of Multicast Blocking Modelling in Fat‐Tree Data Center Networks

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