Reconfiguration of Optical-NFV Network Architectures Based on Cloud Resource Allocation and QoS Degradation Cost-Aware Prediction Techniques

Eramo, V.; Lavacca, Francesco G.; Catena, Tiziana; Giorgio, Flavio Di

doi:10.1109/access.2020.3035749

Cited by 18 publications

(13 citation statements)

References 48 publications

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“…However, some work still needs to be done to develop our results further to a more comprehensive solution. For example, we can extend our work to the integration of a set of constraints on the quality of services in a service demand, to an investigation of multiple optimization objectives, or to take into account dynamic traffic in service demands [33], [34]. We also should consider the development of an adaptive traffic engineering solution for NFV in multiple providers environments as in [35].…”

Section: Discussionmentioning

confidence: 99%

Traffic Engineering Based on Reinforcement Learning for Service Function Chaining With Delay Guarantee

Pham¹

2021

IEEE Access

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Network Function Virtualization (NFV) is an approach that provides a network service provider with agility and cost-efficiency in managing 6G network services. Standard traffic engineering rules are known limited in assuring a very stringent delay requirement in NFV when a traffic flow is required to follow a sequence of network functions scattered in data center networks. This paper proposes an innovative model and algorithm of traffic engineering for service function chaining (SFC) to maximize cost-efficiency under a delay-guarantee constraint. We first formulate the problem as a mixed-integer linear programming model for obtaining the optimal solution. We then propose an algorithm based on the reinforcement learning principles for finding an approximation solution in a large-scale problem with the dynamics of service demands. Numerical results under both real-world datasets and synthetic network topologies demonstrate that our proposed model and algorithm allow an NFV service provider (NSP) to place a virtual network function and steer a traffic flow efficiently in terms of energy cost for a delay-guarantee SFC. Importantly, the results provide an insight into the optimal and approximation solutions for an NSP to select a suitable traffic engineering approach with regard to network dynamics.

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Section: Discussionmentioning

confidence: 99%

Traffic Engineering Based on Reinforcement Learning for Service Function Chaining With Delay Guarantee

Pham¹

2021

IEEE Access

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“…We have proposed the ATPA algorithm based on Seasonal Auto-Regressive Integrated Moving Average (SARIMA) [5,6] prediction with asymmetric loss function; because estimation errors are inevitable and because the error sign can have an impact on the network cost depending on the resource allocation and QoS degradation costs, we have proposed a solution in which the defined loss function gives higher (lower) weight to errors that result in a higher (lower) network cost. We have shown how the proposed solution allows for 30% network cost reduction with respect to the case in which a symmetric cost function as RMSE is chosen.…”

Section: Related Work and Research Contributionmentioning

confidence: 99%

“…We have proposed the Asymmetric Traffic Prediction-based Allocation (ATPA) algorithm [5][6][7] in which the resources are allocated on the basis of a traffic prediction based on both resource allocation and QoS degradation costs. The allocation cost is the rent cost of the processing cores.…”

Section: Introductionmentioning

confidence: 99%

“…The QoS penalty cost is a compensation cost for the user when cloud resources are under-allocated and QoS degradation (i.e., packet loss or delay increase) occurs. The prediction models used in [5] are based on both traditional (i.e., Seasonal AutoRegressive Integrated Moving Average) and Artificial Intelligence (AI)-based (i.e., Long Short Term Memory) models.…”

Section: Introductionmentioning

confidence: 99%

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Proposal and Investigation of a Convolutional and LSTM Neural Network for the Cost-Aware Resource Prediction in Softwarized Networks

Eramo¹,

Valente²,

Catena³

et al. 2021

Future Internet

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Resource prediction algorithms have been recently proposed in Network Function Virtualization architectures. A prediction-based resource allocation is characterized by higher operation costs due to: (i) Resource underestimate that leads to quality of service degradation; (ii) used cloud resource over allocation when a resource overestimate occurs. To reduce such a cost, we propose a cost-aware prediction algorithm able to minimize the sum of the two cost components. The proposed prediction solution is based on a convolutional and Long Short Term Memory neural network to handle the spatial and temporal correlations of the need processing capacities. We compare in a real network and traffic scenario the proposed technique to a traditional one in which the aim is to exactly predict the needed processing capacity. We show how the proposed solution allows for cost advantages in the order of 20%.

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“…Due to the emergence of cloud-based services, network operators are seeking a better solution to implement advanced network infrastructures such as network function virtualization. The simplest approach is to directly build these network architectures on the top of the optical network [1,2]. However, considering the large variety of the requirements for interconnections between data centers [3,4], it is inefficient to utilize a conventional fixed-grid WDM optical network to support these advanced network infrastructures.…”

Section: Introductionmentioning

confidence: 99%

LBFA: A Load-Balanced and Fragmentation-Aware Resource Allocation Algorithm in Space-Division Multiplexing Elastic Optical Networks

Zhang¹,

Yeung²,

Jin³

2021

Photonics

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We consider a space-division multiplexing elastic optical network (SDM-EON) that supports super-channels (SChs). A Sch comprises a set of contiguous frequency slots on multiple cores in a multi-core fiber. The problem of finding a lightpath using SChs involves routing, modulation, spectrum and core assignment (RMSCA). To minimize the request blocking probability (RBP), two critical issues must be addressed. First, routing and modulation assignment (RMA) should not cause hotspots, or overutilized links. Second, spectrum and core assignment (SCA) should aim at minimizing fragmentation, or small frequency slot blocks that can hardly be utilized by future requests. In this paper, a pre-computation method is first proposed for better load balancing in RMA. Then an efficient fragmentation-aware SCA is proposed based on a new fragmentation metric that measures both the spectral and spatial fragmentation. With the enhanced RMA and SCA, a joint load-balanced and fragmentation-aware algorithm called LBFA is designed to solve the RMSCA problem. As compared with the existing algorithms, simulation results show that our LBFA provides significant reduction in RBP.

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Reconfiguration of Optical-NFV Network Architectures Based on Cloud Resource Allocation and QoS Degradation Cost-Aware Prediction Techniques

Cited by 18 publications

References 48 publications

Traffic Engineering Based on Reinforcement Learning for Service Function Chaining With Delay Guarantee

Traffic Engineering Based on Reinforcement Learning for Service Function Chaining With Delay Guarantee

Proposal and Investigation of a Convolutional and LSTM Neural Network for the Cost-Aware Resource Prediction in Softwarized Networks

LBFA: A Load-Balanced and Fragmentation-Aware Resource Allocation Algorithm in Space-Division Multiplexing Elastic Optical Networks

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