CCVP: Cost-efficient centrality-based VNF placement and chaining algorithm for network service provisioning

Ahvar, Shohreh; Phyu, Hnin Pann; Buddhacharya, Sachham Man; Ahvar, Ehsan; Crespi, Noël; Glitho, Roch

doi:10.1109/netsoft.2017.8004104

Cited by 37 publications

(30 citation statements)

References 19 publications

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“…Constraint (14) ensures that ω v,cpu will be 1 if the CPU capacity of node v is overloaded, while Constraint (15) ensures that ψ cpu will be at least as high as the amount of CPU overload of any node (the appearance of ψ cpu in the objective function will guarantee that it will be exactly the maximum amount of CPU overload and not higher than that). Constraints (16), (17) do the same for memory overloads and Constraints (18), (19) do the same for the overload of link capacity. 5) Interplay of the constraints: To illustrate the interplay of the constraints, we assume that we need to optimize the embedding shown in Fig.…”

Section: A Constraintsmentioning

confidence: 99%

“…DECREASE first removes as many edges as possible (lines 3-6); when a further decrease is necessary but no more edges can be removed, it reduces the next flow on each link by the same factor to achieve the required reduction (lines 7-9). INCREASE first checks if new instances need to be created to be consistent with the template (lines [12][13][14][15][16], then tries to increase the existing flows (lines [17][18][19]. If this is not sufficient to achieve the necessary increase, it creates further instances and flows (lines 20-23).…”

Section: Heuristic Approachmentioning

confidence: 99%

“…for all arc (c(i), j) leaving output k of c(i) do 13: if i ∈ I OL with c(i ) = j and ii ∈ E OL then 14: ϕ := CREATEINSTANCEANDFLOW( j, i, ∆λ) 15: ∆λ := ∆λ − (data rate of ϕ) 16: 17: for all ϕ ∈ Φ do while ∆λ > 0 do 21: (c(i), j): random arc leaving output k of c(i) 22: ϕ := CREATEINSTANCEANDFLOW( j, i, ∆λ) 23: ∆λ := ∆λ − (data rate of ϕ) 24: /* Create an instance of component j with flow from instance i of high data rate (capped at cutoff) */ 25: procedure CREATEINSTANCEANDFLOW( j,i,cutoff) 26: for all v ∈ V do 27: create temporary instance i of j on v…”

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confidence: 99%

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JASPER: Joint Optimization of Scaling, Placement, and Routing of Virtual Network Services

Dräxler

Karl

Mann

2018

IEEE Trans. Netw. Serv. Manage.

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To adapt to continuously changing workloads in networks, components of the running network services may need to be replicated (scaling the network service) and allocated to physical resources (placement) dynamically, also necessitating dynamic re-routing of flows between service components. In this paper, we propose JASPER, a fully automated approach to jointly optimizing scaling, placement, and routing for complex network services, consisting of multiple (virtualized) components. JASPER handles multiple network services that share the same substrate network; services can be dynamically added or removed and dynamic workload changes are handled. Our approach lets service designers specify their services on a high level of abstraction using service templates. From the service templates and a description of the substrate network, JASPER automatically makes scaling, placement and routing decisions, enabling quick reaction to changes. We formalize the problem, analyze its complexity, and develop two algorithms to solve it. Extensive empirical results show the applicability and effectiveness of the proposed approach. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.(iii) the network flows between the service components can be re-routed through different, more suitable paths.Given this large number of degrees of freedom for finding the best adaptation, deciding scaling, placement, and routing independently can result in sub-optimal decisions for the network and the running services. Consider a service platform provider hosting a dynamically changing set of network services, where each network service serves dynamically changing user groups that produce dynamically changing data rates. Trade-offs among the conflicting goals of network services and platform operators can be highly non-trivial, for example:• Placing a compute-intensive service component on a node with limited resources near the source of requests (e.g., the location of users, content servers, etc.) minimizes latency but placing it on a more powerful node further away in the network minimizes processing time. • Letting a single instance of a data-processing component serve multiple sources minimizes compute resource consumption but using dedicated instances near the sources minimizes network load. • Changing the current configuration to a better one will hopefully pay off in the long run but keeping the current configuration avoids reconfiguration costs. • Fulfilling the resource requirements of one service versus the requirements of another service. To deal with these challenges, we propose JASPER, a comprehensive approach for the Joint optimizAtion of Scaling, PlacEment, and Routing of virtual network services. In JASPER, each network service is described by a service template, containing information about the components of the network service, the interconnections between the components, and the resource requirements of the components. Both...

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Section: A Constraintsmentioning

confidence: 99%

Section: Heuristic Approachmentioning

confidence: 99%

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confidence: 99%

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JASPER: Joint Optimization of Scaling, Placement, and Routing of Virtual Network Services

Dräxler

Karl

Mann

2018

IEEE Trans. Netw. Serv. Manage.

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“…Our previous work [17] considered a complete cost model including license, computing and communication cost. They introduced a cost adaptive model for low and high traffics and different units of cost.…”

Section: Related Workmentioning

confidence: 99%

“…We implemented one of the recently proposed cost-efficient VNF placement and chaining algorithms (CCVP [17]) which considers VNF capacity and requirement in taking decision for VNF placement and chaining. In CCVP, the same service with the same order is used for all the flows.…”

Section: Use Casementioning

confidence: 99%

SET: a Simple and Effective Technique to improve cost efficiency of VNF placement and chaining algorithms for network service provisioning

Ahvar

Mirzaei

Leguay

et al. 2018

2018 4th IEEE Conference on Network Softwarization and Workshops (NetSoft)

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Network Functions Virtualization (NFV) is a promising solution to provide cost-efficient, scalable and rapid deployment of network services. It allows the implementation of fine-grained services as a chain of Virtual Network Functions (VNFs). In order to place the VNF chains in the network, several cost-efficient methods have been already proposed. However, a few works have considered order of VNFs to reduce the cost. In this paper, we propose a Simple and Effective Technique (SET), which can be easily combined with VNF placement methods to dramatically improve their cost efficiency by considering different possible orders for the VNFs in the chain. As a proof-of-concept, we combine the proposed technique with one of the recent cost-efficient VNF placement and chaining algorithms called CCVP. The results show that the combination can yield significantly better cost than CCVP operating solo.

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Optimal VNF Placement: Addressing Multiple Min-Cost Solutions

Jahedi

Kunz

2019

E-Business and Telecommunications

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CCVP: Cost-efficient centrality-based VNF placement and chaining algorithm for network service provisioning

Cited by 37 publications

References 19 publications

JASPER: Joint Optimization of Scaling, Placement, and Routing of Virtual Network Services

JASPER: Joint Optimization of Scaling, Placement, and Routing of Virtual Network Services

SET: a Simple and Effective Technique to improve cost efficiency of VNF placement and chaining algorithms for network service provisioning

Optimal VNF Placement: Addressing Multiple Min-Cost Solutions

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