Virtual Network Embedding with Coordinated Node and Link Mapping

Chowdhury, Nahid-Ur-Rahman; Rahman, Muntasir Raihan; Boutaba, Raouf

doi:10.1109/infcom.2009.5061987

Cited by 694 publications

(615 citation statements)

References 16 publications

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“…Both the virtual network embedding problem [15,17,25,67] and the testbed mapping problem [54] map nodes and links in a virtual network to a substrate network taking into account various resource constraints, including CPU, network bandwidth, and permissible delays. Traditional techniques used in solving these problems cannot be applied to our public cloud scenario simply because treating the entire datacenter as a substrate network would exceed the instance sizes they can handle.…”

Section: Virtual Network Embeddingmentioning

confidence: 99%

ClouDiA: a deployment advisor for public clouds

et al. 2015

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An increasing number of distributed data-driven applications are moving into shared public clouds. By sharing resources and operating at scale, public clouds promise higher utilization and lower costs than private clusters. To achieve high utilization, however, cloud providers inevitably allocate virtual machine instances non-contiguously; i.e., instances of a given application may end-up in physically distant machines in the cloud. This allocation strategy can lead to large differences in average latency between instances. For a large class of applications, this difference can result in significant performance degradation, unless care is taken in how application components are mapped to instances. In this paper, we propose ClouDiA, a general deployment advisor that selects application node deployments minimizing either (i) the largest latency between application nodes, or (ii) the longest critical path among all application nodes. ClouDiA employs a number of algorithmic techniques, including mixed-integer programming and constraint programming techniques, to efficiently search the space of possible mappings of application nodes to instances. Through experiments with synthetic and real applications in Amazon EC2, we show that mean latency is a robust metric to model

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Section: Virtual Network Embeddingmentioning

confidence: 99%

ClouDiA: a deployment advisor for public clouds

et al. 2015

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“…(1), we also utilize the other objective function given by Eq. (8) to maximize the total residual (free) capacity at all edges.…”

Section: Objectivementioning

confidence: 99%

“…More information on virtual network provisioning with respect to theoretical, as well as computational aspects, can be found, e.g., in [10,18]. Examples of efficient resource assignment algorithms can be in turn found in [8,17,23,24].…”

Section: Introductionmentioning

confidence: 99%

Methods of Network Resource Provisioning for the Future Internet IIP Initiative

et al. 2015

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In this paper, we present specification, design and implementation aspects of a network resource provisioning module introduced for the Polish Initiative of Future Internet called System IIP. In particular, we propose a set of novel LP optimization models of network resource provisioning designed to minimize the network resource consumption, either bandwidth or node's computational power, as well as to maximize the residual capacity. Next, we analyze characteristics of the proposed models, outline the most important implementation aspects of our network resource provisioning module, as well as the issues of integration of this module with the respective network management module of System IIP.

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“…Even if the mapping of all the virtual nodes is accomplished, the mapping of virtual links is still complicated. As a result, there are many VN mapping algorithms that can map as many VNs onto the physical network of edge-of-things computing as possible and minimize the VN mapping costs [26][27][28][29][30]. The VN mapping algorithm proposed in [26] maps the VNs under the guidance of minimizing mapping costs.…”

Section: Introductionmentioning

confidence: 99%

Reliable design for virtual network requests with location constraints in edge-of-things computing

Zhang

Sangaiah

2018

J Wireless Com Network

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How to efficiently map virtual networks (VNs) onto a shared physical network is a challenging issue in the field of network virtualization in edge-of-things computing. Since an efficient VN mapping approach can reduce network resource consumption, lower latency, and enhance service reliability, it is important for both customers and network service providers. In this paper, we study the problem of mapping multiple VNs with geographic location constraints onto a physical network while considering the survivability and reliability requirements of each VN request in edge-ofthings based data centers. We present the model of this problem and propose a Geographic-Guided Survivable Multiple VN Mapping (GG-SMVNM) algorithm to efficiently solve this problem, which simultaneously considers resource sharing and mapping VN links and nodes in edge-of-things computing. Furthermore, we conduct a large amount of simulations to validate and evaluate our proposed approach. The simulation results show that the proposed method is superior to the existing solution.

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Virtual Network Embedding with Coordinated Node and Link Mapping

Cited by 694 publications

References 16 publications

ClouDiA: a deployment advisor for public clouds

ClouDiA: a deployment advisor for public clouds

Methods of Network Resource Provisioning for the Future Internet IIP Initiative

Reliable design for virtual network requests with location constraints in edge-of-things computing

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