Modeling and Optimization of the Network Functions Placement using Constraint Programming

Laghrissi, Abdelquoddouss; Retal, Sara; Idrissi, Abdellah

doi:10.1145/3010089.3010137

Cited by 6 publications

(4 citation statements)

References 22 publications

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“…With the same objectives, the work in [118] proposes a modeling, using constraint programming, for the placement of both S-GWs and P-GWs, to minimize the number of VNF instances, the number of S-GW relocations and the length of the path between the P-GW and end users. Several types of services, end users requirements and geographically distributed DCs are taken into consideration.…”

Section: B S-gw and P-gw Placementmentioning

confidence: 99%

A Survey on the Placement of Virtual Resources and Virtual Network Functions

Laghrissi

Taleb

2019

IEEE Commun. Surv. Tutorials

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222

110

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Cloud computing and network slicing are essential concepts of forthcoming 5G mobile systems. Network slices are essentially chunks of virtual computing and connectivity resources, configured and provisioned for particular services according to their characteristics and requirements. The success of cloud computing and network slicing hinges on the efficient allocation of virtual resources (e.g. VCPU, VMDISK) and the optimal placement of Virtualized Network Functions (VNFs) composing the network slices. In this context, this paper elaborates issues that may disrupt the placement of VNFs and VMs. The paper classifies the existing solutions for VM Placement (VMP) based on their nature, whether the placement is dynamic or static, their objectives, and their metrics. The paper then proposes a classification of VNF Placement (VNFP) approaches, first, regarding the general placement and management issues of VNFs, and second, based on the target VNF type.

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Section: B S-gw and P-gw Placementmentioning

confidence: 99%

A Survey on the Placement of Virtual Resources and Virtual Network Functions

Laghrissi

Taleb

2019

IEEE Commun. Surv. Tutorials

Self Cite

222

110

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“…The LP achieves significant performance in term of the request acceptance rate, load balancing, and resource utilization. The dynamical S-GW and P-GW positions was defined in [23] based on both an improved version of forward checking and the constraint satisfaction problem (CSP), which guaranteed the automatic adaptation to the service requirements. Jointly considering the VNF placement, node switch on/off, and traffic routing, Malandrino et al [24] implemented an optimization module upon the NFV orchestrator and software defined network (SDN) controller to enable current network conditions based swift and high-quality decisions.…”

Section: Related Workmentioning

confidence: 99%

Bayesian Coalition Formation Game for Virtual 5G Core Network Functions

et al. 2019

IEEE Access

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The network function virtualization (NFV) enables the virtual network functions (VNFs) to run as software components upon a virtualization system hosted in a cloud. Recently, due to its high flexibility and elasticity in terms of network services and functions deploying, NFV has been considered as one of the key enabling techniques of the fifth-generation (5G) wireless systems. Moreover, the creation of the core network functions is the key to the implementation of 5G. Currently, there are extensive studies researching into how to deploy cooperatively VNFs over a federated cloud for multiple cloud providers (CPs) to create the 5G Core. These studies have a critical assumption that each CP participating in the cooperation is fully informed of all the information about other CPs. However, the assumption may not hold in practice. Since CPs adopt diverse virtualization technologies (e.g., XEN, KVM, or containers), the cost to create an instance of one VNF for each CP may be different. Moreover, the cost information is private and will not be shared with each other. Consequently, it is difficult for each CP to determine whether to cooperate with others and the way of cooperation. Therefore, in this paper, we utilize the Bayesian coalition formation game (BCFG) to tackle this challenging situation with the unknown information about CPs and formulate the optimal coalition that can deploy cooperatively each VNFs of 5G Core. Specifically, we address the optimal number of VNF instances to instantiate, the number of virtual resources for each instance, and the placement decision in a specific CP over a federated cloud. We propose a BCFG-based non-myopic and full negotiation approach for the creation of the virtual 5G Core network functions. Simulation results demonstrate the comprehensive performance of the proposed method in terms of convergence properties and the profits that each participating CP can obtain.

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“…The concept of NFV is to run network functions as software on standard VMs and through a virtualization platform. Besides NFV, SDN enables the interworking of different VNFs running on different VMs instantiated within the same data center (DC) or across multiple DCs to obtain a flexible, mobile, and dynamic network, rapidly deployable in the cloud [7], [8], [28].…”

mentioning

confidence: 99%

Towards Edge Slicing: VNF Placement Algorithms for a Dynamic & Realistic Edge Cloud Environment

Laghrissi¹,

Taleb²,

Bagaa³

et al. 2017

GLOBECOM 2017 - 2017 IEEE Global Communications Conference

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To support the much desired ultra-short latency of 5G mobile systems, many micro-data centers will be deployed in the vicinity of mobile users, defining a distributed edge cloud. Over this edge cloud, it is important to create optimal network slices to support different 5G verticals. Optimality is defined in terms of cost efficiency and QoS support. Therefore, it is important to understand the behavior of mobile users in terms of mobile service consumption. In this paper, we present, on one hand, a tool for developing a spatio-temporal model of mobile service usage over a particular geographical area. This tool will help to define the behavior of mobile users in terms of mobility patterns and mobile service consumption. On the other hand, based on this tool, we present a benchmark of some interesting Virtualized Network Functions (VNF) placement algorithms, among them our enhanced version of the predictive placement strategy. The comparison is based on data overload, overload of Virtual Machines (VMs) and QoS.

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Modeling and Optimization of the Network Functions Placement using Constraint Programming

Cited by 6 publications

References 22 publications

A Survey on the Placement of Virtual Resources and Virtual Network Functions

A Survey on the Placement of Virtual Resources and Virtual Network Functions

Bayesian Coalition Formation Game for Virtual 5G Core Network Functions

Towards Edge Slicing: VNF Placement Algorithms for a Dynamic & Realistic Edge Cloud Environment

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