Content placement in 5G‐enabled edge/core data center networks resilient to link cut attacks

Natalino, Carlos; Sousa, Amaro de; Wosinska, Lena; Furdek, Marija

doi:10.1002/net.21930

Cited by 12 publications

(7 citation statements)

References 31 publications

(48 reference statements)

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“…Silva et al [199], Canale et al [47], Sen et al [195], Canale et al [48], Martins et al [141], Ghedini et al [92], Barbosa et al [25], Castillo et al [50], Lin and Zhou [135], and Vass et al [212] analyze problems in survivability, reliability, or fault‐tolerance for different required structures and/or operations. Kucharzak et al [129], Fouquet et al [86], Yang et al [225], Cheng et al [54], Myslitski et al [150], Busing et al [45], and Natalino et al [151] examine novel post‐perturbation routing or path selection protocols. Houthooft et al [114], Papadimitriou and Fortz [167], Gomes et al [97], Hmaity et al [107], Girão‐Silva et al [94], and Kalesnikau et al [123] consider problems that integrate network design or operations pre‐perturbation with effective routing post‐perturbation.…”

Section: Optimization Models and Network Robustnessmentioning

confidence: 99%

In search of network resilience: An optimization‐based view

et al. 2020

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Fifty years of research in Networks coincides with 50 years of advances in resilience theory and applications. The purpose of this review is to identify how these two technical communities influenced each other in the past and can bolster each other in the future. Advances in resilience theory show that there are at least four ways networks demonstrate resilience: robustness, rebound, extensibility, and adaptability. Research published in Networks and by the broader network optimization community has focused primarily on technical methods for robustness and rebound. We review this literature to organize seminal problems and papers on the ability of networks to manage increasing stressors and return to normal activities after a stressful event. In contrast, the Networks community has made less progress addressing issues for network extensibility and adaptability. Extensibility refers to the ability to stretch current operations to surprising situations and adaptability refers to the ability to sustain operations into the future. We discuss ways to harness existing network optimization methods to study these forms of resilience and outline their limitations. We conclude by providing a research agenda that ensures the Networks community remains central to future advances in resilience while being pragmatic about the limitations of network optimization for achieving this task.

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Section: Optimization Models and Network Robustnessmentioning

confidence: 99%

In search of network resilience: An optimization‐based view

et al. 2020

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“…Other works, such as [13,14], qualitatively evaluate the impact of content caching inside telecommunication networks in terms of cost and throughput improvement; however, no budget-constraint is considered in these studies. Recent works, such as [15], investigated the content placement problem for resilience against link-cut attacks in a data center network.…”

Section: Related Workmentioning

confidence: 99%

Optimal Cache Deployment for Video-On-Demand in Optical Metro Edge Nodes under Limited Storage Capacity

et al. 2020

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Network operators must continuously explore new network architectures to satisfy increasing traffic demand due to bandwidth-hungry services, such as video-on-demand (VoD). A promising solution which enables offloading traffic consists of terminating VoD requests locally through deploying caches at the network edge. However, deciding the number of caches to deploy, their locations in the network and their dimensions in terms of storage capacity is not trivial and must be jointly optimized, to reduce costs and utilize network resources efficiently. In this paper, we aim to find the optimal deployment of caches in a hierarchical metro network, which minimizes the overall network resource occupation for VoD services, in terms of number of caches deployed across the various network levels, their locations and their dimensions (i.e., storage capacity), under limited storage capacity. We first propose an analytical model which serves as a tool to find the optimal deployment as a function of various parameters, such as popularity distribution and location of metro cache. Then, we present a discrete-event simulator for dynamic VoD provisioning to verify the correctness of the analytical model and to measure the performance of different cache deployment strategies in terms of overall network resource occupation. We prove that, to minimize resource occupation given a fixed budget in terms of storage capacity, storage capacity must be distributed among caches at different layers of the metro network. Moreover, we provide guidelines for the optimal cache deployment strategy when the available storage capacity is limited. We further show how the optimal deployment of caches across the various metro network levels varies depending on the popularity distribution, the metro network topology and the amount of storage capacity available (i.e., the budget invested in terms of storage capacity).

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“…Approaches for strategic placement of network links and nodes aimed at increasing resilience to man-made infrastructure attacks can be found in [9]. Approaches for disaster-resilient design of datacenter networks are presented in [10], while the placement of replicas across distributed cloud networks resilient to targeted fiber cuts is investigated in [11].…”

Section: Related Workmentioning

confidence: 99%

A Framework for Spatial and Temporal Evaluation of Network Disaster Recovery

Gajić

Furdek

Heegaard

2020

2020 32nd International Teletraffic Congress (ITC 32)

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The support of vital societal functions requires a reliable communication network, especially in the presence of crises and disastrous events. Disasters caused by natural factors including earthquakes, fires, floods or hurricanes can disable network elements such as links and nodes and cause widespread disruption in end users connectivity to network services. Effects of disasters can vary over space and time due to disaster escalation and propagation. Network recovery from disasters requires understanding of both the spatial properties of the hazard at hand, and their temporal evolution. While the former has already been addressed in the literature, existing models and measures are unable to capture the temporal aspects of disaster recovery.This paper proposes a framework for spatial and temporal evaluation of network disaster recovery. It allows for modelling random spatial patterns of disasters in a geographical grid. The temporal aspects captured in our framework include changes due to the progression of a potentially shape-changing disaster across the affected area, as well as to the recovery actions of adaptive network reconfiguration and topology reconstruction undertaken by the network operator. The framework applicability is demonstrated on a content delivery network use case example, where we capture the evolving network performance in terms of the average shortest path length between the peers and the content replicas hosted by servers. By providing insights into the spatial and temporal effects of both disaster escalation and remediation measures, our proposed framework lays down the groundwork for flexible disaster modelling and recovery sequence optimization.

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Content placement in 5G‐enabled edge/core data center networks resilient to link cut attacks

Cited by 12 publications

References 31 publications

In search of network resilience: An optimization‐based view

In search of network resilience: An optimization‐based view

Optimal Cache Deployment for Video-On-Demand in Optical Metro Edge Nodes under Limited Storage Capacity

A Framework for Spatial and Temporal Evaluation of Network Disaster Recovery

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