Virtual Network Function Allocation Research Articles

Placement and Allocation of Virtual Network Functions: Multi-Dimensional Case

With the gradual transitioning to Network function virtualization (NFV), Internet service providers face the problem of where to introduce NFV in order to make the most benefit of that; here, we measure the benefit by the amount of traffic that can be served in an NFV-enabled network. This problem is non-trivial as it is composed of two challenging subproblems: 1) placement of nodes to support virtual network functions (referred to as VNF-nodes); 2) allocation of the VNF-nodes' resources to network flows. This problem has been studied for the one-dimensional setting, where all network flows require one network function, which requires a unit of resource to process a unit of flow. In this work, we consider the multi-dimensional setting, which introduces new challenges in addition to those of the one-dimensional setting (e.g., NP-hardness and non-submodularity). To address these difficulties, we propose a novel two-level relaxation method that allows us to draw a connection to the sequence submodular theory and utilize the property of sequence submodularity along with the primal-dual technique to design two approximation algorithms. We prove that the proposed algorithms have a non-trivial approximation ratio and perform trace-driven simulations to show the effectiveness of the proposed algorithms.

Virtual Network Function Allocation in Service Function Chains Using Backups With Availability Schedule

A suitable virtual network function (VNF) placement considering a node availability schedule extends service continuous serviceable time by suppressing service interruptions caused by function reallocation and node unavailabilities. However, function placement cannot avoid service interruptions caused by node unavailabilities. This paper proposes a primary and backup VNF placement model to avoid service interruptions caused by node unavailabilities by using backup functions. The considered backup functions have a period of startup time for preparation before they can be used and the number of them is limited. The proposed model is formulated as an integer linear programming problem to place the primary and backup VNFs based on the availability schedule at continuous time slots. We aim to maximize the minimum number of continuously available time slots in all service function chains (SFCs) over the deterministic availability schedule. We obverse that the proposed model considering the limited number of backup functions outperforms baseline models in terms of the minimum number of longest continuous available time slots in all SFCs. We introduce an algorithm to estimate the number of key unavailabilities at each time slot, which can find the unavailable nodes which are the bottlenecks to increase the service continuous available time at each time slot.

Online Joint Placement and Allocation of Virtual Network Functions With Heterogeneous Servers

Network function virtualization (NFV) is a promising virtualization technology that has the potential to significantly reduce the expenses and improve service agility. The NFV makes it possible for Internet service providers (ISPs) to employ various virtual network functions (VNFs) without installing new equipments. One of the most attractive approaches in the NFV technology is the so-called joint placement and allocation of virtual network functions (JPA-VNFs), which considers the balance between VNF investment with Quality of Services (QoS). We introduce a novel capability function to measure the potential of locating VNF instances for each server in the proposed OJPA-HS model. This model allows the servers in the network to be heterogeneous, at the same time combines and generalizes many classical JPA-VNF models. Despite its NP-hardness, we present a provable best-possible deterministic online algorithm based on dynamic programming (DP). To conquer the high complexity of DP, we propose two additional randomized heuristics, Las Vegas (LV) and Monte Carlo (MC) randomized algorithms, which perform even as good as DP with much smaller complexity. Besides, MC is a promising heuristic in practice as it has the advantage to deal with the big data environment. Extensive numerical experiments are constructed for the proposed algorithms in this article.

Virtual Network Function Allocation to Maximize Continuous Available Time of Service Function Chains With Availability Schedule

This paper proposes an optimization model to derive the virtual network function (VNF) allocation of time slots in sequence aiming to maximize the continuous available time of service function chains (SFCs) in a network. The proposed model suppresses service interruptions otherwise created by the unavailability of virtual machines (VMs) and the reallocation of VNFs. The proposed model computes VNF allocation in a series of time slots based on a VM availability schedule, which provides information on the availability of each VM in each time slot. We formulate the proposed model as an integer linear programming (ILP) problem with the goal of maximizing the minimum number of longest continuous available time slots in each SFC. We prove that the decision version of the VNF allocation problem (VNFA) is NP-complete. As the size of ILP problem increases, the problem is difficult to solve in a practical time. We develop a heuristic algorithm to solve the VNFA problem. Numerical results show that the proposed model improves the continuous available time of SFCs compared with existing models, which partially consider VM unavailability or VNF reallocation. We observe that the proposed model together with a consideration of routing reduces the path length of requests. The developed heuristic algorithm is faster than the ILP approach with a limited performance penalty.

Joint Energy Efficient and QoS-Aware Path Allocation and VNF Placement for Service Function Chaining

Service function chaining (SFC) allows the forwarding of traffic flows along a chain of virtual network functions (VNFs). Software defined networking (SDN) solutions can be used to support SFC to reduce both the management complexity and the operational costs. One of the most critical issues for the service and network providers is the reduction of energy consumption, which should be achieved without impacting the Quality of Service. In this paper, we propose a novel resource allocation architecture which enables energy-aware SFC for SDN-based networks, considering also constraints on delay, link utilization, server utilization. To this end, we formulate the problems of VNF placement, allocation of VNFs to flows, and flow routing as integer linear programming (ILP) optimization problems. Since the formulated problems cannot be solved (using ILP solvers) in acceptable timescales for realistic problem dimensions, we design a set of heuristic to find near-optimal solutions in timescales suitable for practical applications. We numerically evaluate the performance of the proposed algorithms over a real-world topology under various network traffic patterns. Our results confirm that the proposed heuristic algorithms provide near-optimal solutions (at most 14% optimality-gap) while their execution time makes them usable for real-life networks.