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Network Function Virtualization (NFV) is an emergent paradigm that is currently transforming the way network services are provisioned and managed. The main idea of NFV is to decouple network functions from the hardware running them. This allows to reduce deployment costs and further improve the flexibility and the scalability of network services. D...
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... It also uses the SDN controller to provision the required bandwidth and to program the appropriate forwarding rules into the switches in order to direct traffic across the VNFs of each service function chain. The design of this module should be noted as being outside the purview Numerous studies have been conducted to design and develop such module, and any of the existing solutions may be used [3,53,54]. ...
... We assume that embedding of service chains has been already performed using an existing SFC placement and chaining algorithm. In particular, we used the SFC provisioning scheme proposed by Racheg et al. [54] to allocate resources for the service chains while satisfying their resource and end-to-end delay requirements. • Operational costs: as electricity prices vary from a region to another [63], the running and management costs for each backup instance were randomly varied, ranging from $0.0116 per hour to $0.0186 per hour. ...
The emergence of Network Function Virtualization enables the deployment of network services in the form of service function chains. In this context, one of the key challenges is to ensure the survivability of these chains in face of single or multiple simultaneous physical node failures. In this paper, we address this challenge and propose solutions to guarantee the survivability of service chains by ensuring that there are enough backups ready to take over when failures occur. Specifically, we put forward a Survivability Management Framework that predicts traffic demand in service function chains and provision enough backups for network functions with minimal costs. To this end, we leverage the AutoRegressive Integrated Moving Average (ARIMA) model to predict future demand. We mathematically model the service chain survivability problem as an integer linear program that determines the minimal number of shared backups and their optimal location in the infrastructure such that backup operational costs are minimized. We also devise two greedy algorithms to deal with the problem in large-scale scenarios. We show, through several simulations, the performance and efficiency of the proposed solutions in different scenarios. We also show that demand prediction could help to avoid unnecessary provisioning of backups, and thereby reduce their operational costs.
... To maximize the total profit of service providers, Racheg et al. 17 proposed an ILP-based model and used heuristic methods to reduce complexity. Li et al. 9 formulated the SFC orchestration problem in EC networks as an integer linear programming to minimize the total resource consumption. ...
Network function virtualization (NFV) is an appealing solution that transforms complex network functions from dedicated hardware to software instances running in a virtualized environment. However, some new challenges will arise when deploying virtual network functions to meet the needs of NFV and edge‐computing (EC) enabled 5G networks. In this paper, we focus on the service function chain (SFC) orchestration problem for EC‐enabled networks to maximize the profit of network service providers. First, the mathematical model of SFC orchestration in NFV and EC‐enabled networks is defined. Then, a two‐stage heuristic algorithm is proposed to optimize the total revenue. Finally, the performance of the method is evaluated by simulation experiments and the results show its effectiveness.
... In this context, the SFC provider would face a major challenge as to how to allocate the resources to the requested SFC in the physical infrastructure. Although this problem has been recently extensively addressed [4,5,6] in the literature, we revisit this problem in this work with the following novel contributions: ...
... It is also worth noting that synchronization costs were not considered in existing literature. 4. We also propose two heuristic algorithms to solve the mapping problem with the same aforementioned goals taking into account the conducted study of the costs of Amazon EC2 instances. ...
With the growing deployment of emergent technologies like software-defined networking, network services are expected to be revolutionized. In this paper, we investigate offering Service Function Chains as a Service (SFCaaS) in NFV environments. We describe the potential business model to offer such a service and then we address the service function chain provisioning and resource allocation problem. As the chain is deployed thanks to virtual machines (i.e., instances) and links, we conduct first a detailed study of the costs of Amazon EC2 instances with respect to their location, size, type and performance. Afterwards, we address the resource allocation problem for service function chains from the SFC provider's perspective. We formulate the problem as an integer linear program aiming at reducing operational costs of the service function chains (i.e., costs of virtual machine instances and links, and synchronization among the instances). To address large-scale instances of the problem, we also propose a new heuristic algorithm to reduce operational costs taking into account the conducted study of the costs of Amazon EC2 instances. We show through extensive simulations that the proposed heuristic significantly reduces operational costs compared to a baseline algorithm inspired by the existing literature.
... Two studies have dealt with the VN embedding problem including routing control. W. Racheg et al. [24] proposed a VNF embedding method that maximizes the revenues of telecommunications carriers by taking into account the difference in the electricity unit price for each area in addition to the power consumption of equipment and network delays. S. Su et al. [25] studied the VNF embedding problem to minimize the energy cost by modeling the starting power of servers and routers in addition to the power consumption of nodes and links when the electricity unit price fluctuates in each time period for each area. ...
The environmental load of telecommunication service provision is increasing due to the increase in communication traffic. Virtual networks have recently begun to spread, and flexible virtual network control is expected to reduce the operating costs of telecommunication services. This paper proposes a control method to achieve both reduced power bills and stable network operation when the electricity unit price differs among areas and time periods. To begin with, the problem to minimize power bills is formulated, and to solve it quickly, a heuristic search method is proposed that utilizes network centrality. In addition, we formulate a multi-objective optimization problem characterized by parameter normalization, and simulation results show that the proposed control method can reduce power bills while suppressing the number of network reconfigurations.
... In this context, the SFC provider would face a major challenge as to how to allocate the resources to the requested SFC in the physical infrastructure. Although this problem has been recently extensively addressed [4], [5], [6] in the literature, we revisit this problem in this work with the following novel contributions: 1) We consider two phases to provision an SFC (Fig 1). ...
With the emergence of network softwarization trend, traditional networking services offered by Internet providers are expected to evolve by fully leveraging new recent technologies like network function virtualization and software defined networking. In this paper, we investigate offering Service Function Chains as a Service (SFCaaS) in NFV Environments. We first describe the potential business model to offer such a service. We then conduct a detailed study of the costs of virtual machine instances offered by Amazon EC2 with respect to the location, instance size, and performance in order to guide service chain provisioning and resource allocation. Afterwards, we address the resource allocation problem for service chain functions from the SFC provider's perspective while leveraging the performed cost study. We hence formulate the problem as an Integer Linear Program (ILP) aiming at reducing the SFC provider's operational costs of virtual machine instances and links as well as the synchronization costs among the instances. We also propose a new heuristic algorithm to solve the mapping problem with the same aforementioned goals taking into account the conducted study of the costs of Amazon EC2 instances. We show through extensive simulations that the proposed heuristic significantly reduce operational costs compared to a Baseline algorithm inspired by the existing literature.
... Some of them only focus on VNF placement or SFC deployment and those objective are not comprehensive. [18], [24]. [18] aims to minimize the number of used physical nodes and consider two factors, which are the time-varying workload and basic resource consumption when running VNFs on physical nodes. ...
... However, unlike us, they do not consider the end-to-end delay of SFCRs. [24] formulated a problem on SFC deployment to reduce energy consumption and maximize the provider's profit. They offer an effective profit-driven service chain provisioning scheme for large-scale infrastructures spanning different geographically-distributed sites. ...
The emergence of network function virtualization (NFV) has revolutionized the infrastructure and service management of network architecture. Through virtual network function (VNF), network operators not only can reduce their cost on Capital expenditures (CAPEX), operating expenses (OPEX), and power consumption, but also improve the time and flexibility of network service deployment. However, one of the key challenges of NFV is the VNF placement problem, which can affect both deployment cost and service quality. VNF placement is a well-known NP-complete problem, hence many previous studies formulate the problem as an Integer Linear Programming (ILP) Problem and find the best placement using an ILP solver. Since solving ILP is time-consuming, using an ILP solver is infeasible for large scaled and dynamic workloads in real-time. As a result, various greedy algorithms have also been proposed to find approximate solutions. However, neither of these approaches can make quick and accurate placement decisions for dynamic traffic workload. Therefore, we propose a hybrid method that uses less time to maximize the overall profit of network service deployment. Our evaluations based on real backbone network traffic and topology show that our hybrid approach can achieve up to 38% profit improvement comparing to a pure greedy approach, while achieving x30 times computation time speedup over a pure ILP approach.
... With the aim of maximizing the total profit of cloud provider, Racheg et al. [26] addressed the problem of VNF placement and chaining with end-to-end propagation delay constraints by proposing an ILP model and three algorithms that gradually optimized the computational complexity. Similarly, Ma et al. [27] focused on delay-aware SFC provisioning to maximize the profit of the service provider. ...
To meet the increasing traffic demands characterized by large bandwidth and high burstiness, more traffic has been moving to inter-datacenter elastic optical networks (inter-DC EONs) for processing. The integration of two emerging paradigms, network function virtualization (NFV) and software-defined networking (SDN), enables Internet service providers (ISPs) to deploy service function chains (SFCs) from users flexibly while reducing operational and capital expenditures. This paper focuses on the problem of online SFC provisioning in inter-DC-EONs with the aim of maximizing ISP profits, where the challenge in jointly allocating IT and spectrum resources when deploying SFCs is balanced with the deployment costs of processing as many user requests as possible. We design two-phase time-efficient orchestration algorithms for online SFC requests and the strategy of SFC splitting is adopted to improve the utilization of spectrum resources on fiber links. Simulation results show that, compared with the existing algorithm, our proposed algorithms significantly shorten the deployment time, improve total profit of ISP by up to 40% and reduce the blocking probability by up to 35%.
... Motivated by the fact that NFV-enabled edge-computing networks need to be optimized in terms of profit, as shown in [31] or [32], and inspired by related work that not only optimize operational costs but also resource utilization, while making sure that service level agreements are met, as in [33], we contribute with a model that minimizes costs as an objective function when placing VNFs in a generic edge-cloud continuum. In our model, we consider the specific constraints arising from a hybrid SFCs of VMs and containers, which has not been addressed yet. ...
... x ← chooseServer(s, λ, p, v, X p ) go to 40 30: addVNFToServer(v , x) 31: routeDemandToPath(s, p, λ) 32: procedure CHOOSEPATH(s, λ, P s ) 33: p ← getUsedPathForDemandInitPlacement(s, λ, P s ) 34: if p then return p 35: p ← getUsedPathInitialPlacement(s, P s ) 36: if p then return p 37: p ← getUsedPathForSFC(s, P s ) 38: if p then return p 39: return getPathWithShortestDelay(s, λ, P s ) 40: procedure CHOOSESERVER(s, λ, p, v, X p ) 41: ...
... For hybrid SFCs, the VNFs are randomly assigned either VMs or CTs, following a uniform distribution considering all possible combinations. Finally, the penalty parameter ρ is computed as 10% of the selling price for an specific SFC (see [32] or [38]), which depends on the VNFs allocated to provide that specific service. So, to calculate the selling price for every SFC, we consider the same selling prices as for the charges of the cloud provider, so we use K t(v) values as well for every VNF deployed at the edge. ...
Traditionally, Network Function Virtualization (NFV) has been implemented to run on Virtual Machines (VMs) in form of Virtual Network Functions (VNFs). More recently, the so-called Serverless Computing has gained traction in cloud computing, offering Function-as-a-Service (FaaS) platforms that make use of containerization techniques to deploy services. In contrast to VM-based VNFs, where resources are usually reserved and continuously running, FaaS can just be subsets of code implementing small functions allowing for event-driven, on-demand instantiations. Thus, a hybrid VM-Container based Service Function Chains (SFCs) are a natural evolution of NFV architecture. We study a novel problem of optimal placement of hybrid SFCs from an Internet Service Provider (ISP) point of view, whereby VNFs can be instantiated either over VMs or containers in a generic edge and cloud continuum. To this end, we propose a Mixed-Integer Linear Programming model as well as a heuristic solution to solve this optimization problem that considers three objectives unique to the specific VM and container deployment in a carrier network: operational costs for maintaining servers in the edge, costs of placing VNFs in third-party cloud providers and penalty costs applied when SLA agreements are violated in terms of end-to-end delay. We also propose 2-phases optimization process to analyze the effect on performance as a result of replications and migrations of VNFs. The model can be used to highlight scenarios where a combination of VMs and containers can provide most benefits from the monetary costs point of view.
... Any VNF is run on a VNF Instance (VNFI) implemented with one Virtual Machine (VM) to which resources (cores, RAM memory, disk memory) are allocated to execute a VNF of a given type (e.g., a virtual firewall, or a load balancer) [7]- [8] The research activity on NFV has been focusing on both the problem of SFC routing and choosing the servers in which the VNFIs are executed. Some performance indexes are considered as the number of SFCs accepted [9]- [10], the server power consumption [11]- [14], the bandwidth used to interconnect the VNFIs [15]- [17], the cloud resource amount needed to activate the VNFIs [18]- [19] and so on. Most of the papers analyze the problem in the static traffic scenario in which the SFC requests and their offered bandwidth are given [11]. ...
... The resource requirements of any SFC is characterized by a variable bandwidth that will be specified later and it is expressed by the Eqs (14) and (15) reported in Subsections VII-A and VII-B respectively. VNF instances can be instantiated in the cloud infrastructure to support these SFs. ...
The introduction of Network Function Virtualization (NFV) leads to a new business model in which the Telecommunication Service Provider needs to rent cloud resources to Infrastructure Provider (InP) at prices as low as possible. Lowest prices can be achieved if the cloud resources can be rented in advance by allocating long-term Virtual Machines (VM). This is in contrast with the short-term VMs that are rented on demand and have higher costs. For this reason we propose a proactive solution in which the cloud resource rent is planned in advance based on a peak traffic knowledge. We illustrate the problem of determining the cloud resources in Cloud Infrastructures managed by different InPs and so as to minimize the sum of cloud resource, bandwidth and deployment costs. We formulate an Integer Linear Problem (ILP) and due to its complexity we introduce an efficient heuristic approach allowing for a remarkable computational complexity reduction. We compare our solution to a reactive solution in which the cloud resources are rented on demand and dimensioned according to the current traffic. Though the proposed proactive solution needs more cloud and bandwidth resources due to its peak allocation, its total resources cost may be lower than the one achieved when a reactive solution is applied. That is a consequence of the higher cost of short-term VMs. For instance when a reactive solution is applied with traffic variation times of ten minutes, our proactive solution allows for lower total costs when the long-term VM rent is lower than the short-term VM one by 33%.
... [13] x x x x x Ayoubi et al. [14] x x x x x Guo et al. [20] x x x --Xiao et al. [17] x x x --Rahman et al. [15] x x x --Bo et al. [18] x x x --Ayoubi et al. [21] x x x --Ghaleb et al. [22] x It is worth noting that the design of this module is out of the scope of this work. There is a large body of work addressing this module and any of the existing solutions could be used (e.g., [2], [4], [6]). ...
... We assume the embedding of service chains (i.e., VNFs and virtual links) is already carried out by an existing VNF placement algorithm. In our experiments, we used the resource allocation algorithm for service chains that was proposed by Racheg et al. [4]. We considered 8 different embedding scenarios where the utilization of the infrastructure has been gradually increased as shown in Figure 3. ...
With the growing adoption of Software Defined Networking (SDN) and Network Function Virtualization (NFV), large-scale NFV infrastructure deployments are gaining momentum. Such infrastructures are home to thousands of network Service Function Chains (SFCs), each composed of a chain of virtual network functions (VNFs) that are processing incoming traffic flows. Unfortunately, in such environments, the failure of a single node may break down several VNFs and thereby breaking many service chains at the same time. In this paper, we address this particular problem and investigate possible solutions to ensure the survivability of the affected service chains by provisioning backup VNFs that can take over in case of failure. Specifically, we propose a survivability management framework to efficiently manage SFCs and the backup VNFs. We formulate the SFC survivability problem as an integer linear program that determines the minimum number of required backups to protect all the SFCs in the system and identifies their optimal placement in the infrastructure. We also propose two heuristic algorithms to cope with the large-scale instances of the problem. Through extensive simulations of different deployment scenarios, we show that these algorithms provide near-optimal solutions with minimal computation time.
