Optimization Model for Backup Resource Allocation in Middleboxes With Importance

He, Fujun; Sato, Tomomasa; Oki, Eiji

doi:10.1109/tnet.2019.2930809

Cited by 41 publications

(7 citation statements)

References 34 publications

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“…To improve the resource utilization efficiency, the authors of [23] introduces a new sharing mechanism of redundancy and multi-tenancy technology. [24] proposes a backup resource allocation model for middleboxes considering the importance of functions and both failure probabilities of functions and backup servers. [25] studies a reliabilityaware resource allocation algorithm using the shared protection scheme with active-standby redundancy for SFC.…”

Section: Related Workmentioning

confidence: 99%

Proactive and AoI-aware Failure Recovery for Stateful NFV-enabled Zero-Touch 6G Networks: Model-Free DRL Approach

Shaghaghi,

Zakeri,

Mokari

et al. 2021

Preprint

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Zero-touch network and service management (ZSM) concept introduced by European Telecommunications Standards Institute (ETSI) is a promising solution for full automation in ultra-reliable and ultra-responsive future networks such as sixth generation (6G). The ZSM is an ongoing study by the ETSI and to realized it, machine learning (ML) and network softwarization are indispensable. Despite flexibility and resource efficiency, softwarized networks are vulnerable to various faults and problems such as physical node failure or software malfunctions.

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Section: Related Workmentioning

confidence: 99%

Proactive and AoI-aware Failure Recovery for Stateful NFV-enabled Zero-Touch 6G Networks: Model-Free DRL Approach

Shaghaghi,

Zakeri,

Mokari

et al. 2021

Preprint

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“…Content may change prior to final publication. [9] Resource exhaustion, hardware failure, and SLA violations No Experimentation [12] Resource revocation risk No Experimentation [13] Data leakage No Graph Theory and heuristic [14] Reduce the risk of co-resident attack No Simulation [15] Reduce the risk of co-resident attack No Simulation [16] Reduce the risk of co-resident attack No Simulation [17] Virtual resources to protect the task execution Yes Semi-Markov Decision Process [18] Virtual resources to protect the task execution No Semi-Markov Decision Process [19] Virtual resources to protect the task execution No Semi-Markov Decision Process [20] Virtual resources to protect the task execution No Markov reward model and simulated annealing [21] Security overhead to protect the task execution Yes Genetic Algorithm [22] Security overhead to protect the task execution Yes Deep Reinforcement Learning [23] Computation and communication uncertainties Yes Game Theory [24] Risk-neutral user, risk-averse user, risk-seeking user Yes Simulation [25] IDS at the edge of the network Yes Stochastic Differential Equation [26] Service failure No Graph Theory [27] Service and server failure No System Optimization and Heuristics Our work…”

Section: Security Risk-aware Edge Server Orchestration a Related mentioning

confidence: 99%

“…Service survivability in the presence of failure is addressed in [26] and [27] from a perspective of a NFV deployment. In [26], Kanizo et al proposed to use a VNF to implement and deploy backup schemes for network functions that ensure high levels of survivability while reducing resource consumption.…”

Section: Security Risk-aware Edge Server Orchestration a Related mentioning

confidence: 99%

“…In [26], Kanizo et al proposed to use a VNF to implement and deploy backup schemes for network functions that ensure high levels of survivability while reducing resource consumption. He et al [27] proposed a backup resource allocation model where both network services and backup servers fail. Given the limited capacity of backup and the fact the network functions differ in terms of importance, the authors proposed to backup network functions based on their importance.…”

Section: Security Risk-aware Edge Server Orchestration a Related mentioning

confidence: 99%

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When Agile Security Meets 5G

et al. 2020

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5G is a critical infrastructure that will connect the whole society and bridge other critical infrastructure systems. Thus, cybersecurity emerges as crucial tenet within the 5G pathway. In this article, we discuss the concept of agile security within a 5G infrastructure taking into account two of its major technologies: Mobile Edge Computing (MEC) and Network Functions Virtualization (NFV). In this sense, we first discuss the 5G-driven MEC deployment from a NFV perspective. Secondly, we present the concept of agile security and how it can be embedded in the daily activities of mobile network operators (MNOs). Thirdly, we discuss risk management as a key element of the agile security framework. To illustrate its application, we propose the design of an agile security risk-aware edge server mechanism for 5G driven MEC deployment, which uses multiple thresholds and a load-balancing security control to mitigate the risks of resource exhaustion and violation of the service level agreement (SLA) faced by the edge servers while taking advantage of multiple cloud layers to increase the degree of availability and dependability of the system. Numerical results show that the proposed mechanism is able to keep the risk at lower levels. INDEX TERMS 5G Security, Agile Security, Cloud Security, Security Risk Management.

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“…N ETWORK functions (NFs), such as firewalls and load balancers, are able to be implemented in a flexible way to share the infrastructure resources with the help of network function virtualization (NFV) technology. By decoupling NFs from their dedicated physical network equipments, a given service can be decomposed into a set of virtual network functions (VNFs) which can be deployed on commodity servers [2], [3]. Given a set of services that consist of requested VNFs, a critical problem is how to deploy these VNFs, including placing VNFs and allocating computing resources, with meeting the requirements of services.…”

Section: Introductionmentioning

confidence: 99%

Service Deployment Model on Shared Virtual Network Functions With Flow Partition

Zhang

Oki

2022

IEEE Open J. Commun. Soc.

Self Cite

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Network operators can operate services in a flexible way with virtual network functions thanks to the network function virtualization technology. Flow partition allows aggregated traffic to be split into multiple parts, which increases the flexibility. This paper proposes a service deployment model with flow partition to minimize the service deployment cost with meeting service delay requirements. A virtual network function of a service is allowed to have several instances, each of which hosts a part of flows and can be shared among different services, to reduce the initial and proportional cost. We provide the mathematical formulation for the proposed model and transform it to a special case as a mixed integer second-order cone programming (MISOCP) problem. A heuristic algorithm, which is called a flow partition heuristic (FPH), is introduced to solve the original problem in practical time by decomposing it into several steps; each step handles a convex problem. We compare the performances of proposed model with flow partition and conventional model without flow partition. We consider the formulated MISOCP problem with adopting a strategy of even splitting to divide flows in a special case, which is called an even spitting heuristic (ESH). The performances of FPH and ESH are compared in a realistic scenario. We also consider the formulated MISOCP problem as an original problem and compare it to an FPH-based heuristic algorithm with the even-splitting strategy (FPH-ES), in both realistic and synthetic scenarios. The numerical results reveal that the proposed model saves the service deployment cost compared to the conventional one. It improves the maximum admissible traffic scale by 23% in average in our examined cases. We observe that FPH outperforms ESH and ESH outperforms FPH-ES in terms of the service deployment cost in their own focused problems, respectively.

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Optimization Model for Backup Resource Allocation in Middleboxes With Importance

Cited by 41 publications

References 34 publications

Proactive and AoI-aware Failure Recovery for Stateful NFV-enabled Zero-Touch 6G Networks: Model-Free DRL Approach

Proactive and AoI-aware Failure Recovery for Stateful NFV-enabled Zero-Touch 6G Networks: Model-Free DRL Approach

When Agile Security Meets 5G

Service Deployment Model on Shared Virtual Network Functions With Flow Partition

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