Resilience options for provisioning anycast cloud services with virtual optical networks

Bùi, Minh N.; Jaumard, Brigitte; Develder, Chris

doi:10.1109/icc.2014.6883857

Cited by 5 publications

(2 citation statements)

References 17 publications

(22 reference statements)

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“…Further experiments should confirm these hypotheses. Also, we suggest to evaluate the impact of the server locations (e.g., scattered vs. paired as in [10]), and investigate a broader range of time-varying traffic patterns. …”

Section: Discussionmentioning

confidence: 99%

A scalable model for multi-period virtual network mapping for resilient multi-site data centers

Wang

Jaumard

Develder

2015

2015 17th International Conference on Transparent Optical Networks (ICTON)

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Abstract-In the currently dominant cloud computing paradigm, applications are being served in data centers (DCs) which are connected to high capacity optical networks. For cost efficiency reasons, in both DC and optical network domains, virtualization of the physical hardware is exploited. In a DC, it means that multiple so-called virtual machines (VMs) are being hosted on the same physical server. Similarly, the network is partitioned into separate virtual networks, thus providing isolation between distinct virtual network operators (VNOs). Thus, the problem of virtual network mapping arises: how to decide which physical resources to allocate for a particular virtual network? In this paper, we study that problem in the context of cloud computing with multiple DC sites. This introduces additional flexibility, due to the anycast routing principle: we have the freedom to decide at what particular DC location to serve a particular application. We can exploit this choice to minimize the required resources when solving the virtual network mapping problem. This paper builds on our earlier work and solves the resilient virtual network mapping problem that optimally decides on the mapping of both network and data center resources, considering timevarying traffic conditions and protecting against possible failures of both network and DC resources. Previously, we developed a model to solve the multi-period traffic case one step at a time: given the virtual network mapping in period t, we determine the (possibly changed) mapping for t + 1. Compared to that previous work, we now (i) define a truly multi-period model path formulation exploiting column generation, and (ii) demonstrate its scalability on a nation-wide network with traffic that varies across multiple periods.

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Section: Discussionmentioning

confidence: 99%

A scalable model for multi-period virtual network mapping for resilient multi-site data centers

Wang

Jaumard

Develder

2015

2015 17th International Conference on Transparent Optical Networks (ICTON)

Self Cite

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“…Survivable CN with anycast (SCNAnyCast) [144] minimizes delay and/or cost in CN mapping with replicated VMs in case of DC and link failures. However, the synchronization between VM replicas requires additional mapping, hence [145], [160] propose an extension of SCNAnyCast, called (SCN-Joint-AnyCast-Rep), that performs joint optimization of synchronization bandwidth while minimizing RT. A more comprehensive and cost-efficient approach using anycast routing with VM replication is the reliability-aware VDC embeding (RA-VDC-Emb) approach [146] that maps, replicates, and migrates VMs in case of server or communication failures.…”

Section: Resiliency In Cloud Integrated Infrastructurementioning

confidence: 99%

A Survey on Resiliency Techniques in Cloud Computing Infrastructures and Applications

Colman-Meixner

Develder

Tornatore

et al. 2016

IEEE Commun. Surv. Tutorials

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127

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Abstract-Today's businesses increasingly rely on cloud computing, which brings both great opportunities and challenges. One of the critical challenges is resiliency: disruptions due to failures (either accidental or because of disasters or attacks) may entail significant revenue losses (e.g., US$ 25.5 billion in 2010 for North America). Such failures may originate at any of the major components in a cloud architecture (and propagate to others): (i) the servers hosting the application, (ii) the network interconnecting them (on different scales, inside a data center, up to wide-area connections), or (iii) the application itself. We comprehensively survey a large body of work focusing on resilience of cloud computing, in each (or a combination) of the server, network, and application components.First, we present the cloud computing architecture and its key concepts. We highlight both the infrastructure (servers, network) and application components. A key concept is virtualization of infrastructure (i.e., partitioning into logically separate units), and thus we detail the components in both physical and virtual layers. Before moving to the detailed resilience aspects, we provide a qualitative overview of the types of failures that may occur (from the perspective of the layered cloud architecture), and their consequences.The second major part of the paper introduces and categorizes a large number of techniques for cloud computing infrastructure resiliency. This ranges from designing and operating the facilities, servers, networks, to their integration and virtualization (e.g., also including resilience of the middleware infrastructure).The third part focuses on resilience in application design and development. We study how applications are designed, installed, and replicated to survive multiple physical failure scenarios as well as disaster failures.

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Time-varying resilient virtual network mapping for multi-location cloud data centers

Bùi

Wang

Jaumard

et al. 2014

2014 16th International Conference on Transparent Optical Networks (ICTON)

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Optical networks constitute a fundamental building block that has enabled the success of cloud computing. Virtualization, a cornerstone of cloud computing, today is applied in the networking field: physical network infrastructure is logically partitioned into separate virtual networks, thus providing isolation between distinct virtual network operators (VNOs). Hence, the problem of virtual network mapping has arisen: how to decide which physical resources to allocate for a particular virtual network? In a cloud context, not just network connectivity is required, but also data center (DC) resources located at multiple locations, for computation and/or storage. Given the underlying anycast routing principle, the network operator has some freedom to which specific DC to allocate these resources.In this paper, we solve a resilient virtual network mapping problem that optimally decides on the mapping of both network and multi-location data center resources resiliently using anycast routing, considering time-varying traffic conditions. In terms of resilience, we consider the so-called VNO-resilience scheme, where resilience is provided in the virtual network layer. To minimize physical resource capacity requirements, we allow reuse of both network and DC resources. The failures we protect against include both network and DC resource failures: we hence allocate backup DC resources, and also account for synchronization between primary and backup DC.As optimization criteria, we not only consider resource usage minimization, but also aim to limit virtual network reconfigurations from one time period to the next. We propose a scalable column generation approach to solve the dynamic resilient virtual network mapping problem, and demonstrate it in a case study on a nationwide US backbone network.

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Resilience options for provisioning anycast cloud services with virtual optical networks

Cited by 5 publications

References 17 publications

A scalable model for multi-period virtual network mapping for resilient multi-site data centers

A scalable model for multi-period virtual network mapping for resilient multi-site data centers

A Survey on Resiliency Techniques in Cloud Computing Infrastructures and Applications

Time-varying resilient virtual network mapping for multi-location cloud data centers

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