Resource Allocation in SDN/NFV-Enabled Core Networks

For next generation core networks, it is anticipated to integrate communication, storage and computing resources into one unified, programmable and flexible infrastructure. Software-defined networking (SDN) and network function virtualization (NFV) become two enablers. SDN decouples the network control and forwarding functions, which facilitates network management and enables network programmability. NFV allows the network functions to be virtualized and placed on high capacity servers located anywhere in the network, not only on dedicated devices in current networks. Driven by SDN and NFV platforms, the future network architecture is expected to feature centralized network management, virtualized function chaining, reduced capital and operational costs, and enhanced service quality. The combination of SDN and NFV provides a potential technical route to promote the future communication networks. It is imperative to efficiently manage, allocate and optimize the heterogeneous resources, including computing, storage, and communication resources, to the customized services to achieve better quality-of-service (QoS) provisioning. This thesis makes some in-depth researches on efficient resource allocation for SDN/NFV-enabled core networks in multiple aspects and dimensionality. Typically, the resource allocation task is implemented in three aspects. Given the traffic metrics, QoS requirements, and resource constraints of the substrate network, we first need to compose a virtual network function (VNF) chain to form a virtual network (VN) topology. Then, virtual resources allocated to each VNF or virtual link need to be optimized in order to minimize the provisioning cost while satisfying the QoS requirements. Next, we need to embed the virtual network (i.e., VNF chain) onto the substrate network, in which we need to assign the physical resources in an economical way to meet the resource demands of VNFs and links. This involves determining the locations of NFV nodes to host the VNFs and the routing from source to destination. Finally, we need to schedule the VNFs for multiple services to minimize the service completion time and maximize the network performance. In this thesis, we study resource allocation in SDN/NFV-enabled core networks from the aforementioned three aspects. First, we jointly study how to design the topology of a VN and embed the resultant VN onto a substrate network with the objective of minimizing the embedding cost while satisfying the QoS requirements. In VN topology design, optimizing the resource requirement for each virtual node and link is necessary. Without topology optimization, the resources assigned to the virtual network may be insufficient or redundant, leading to degraded service quality or increased embedding cost. The joint problem is formulated as a Mixed Integer Nonlinear Programming (MINLP), where queueing theory is utilized as the methodology to analyze the network delay and help to define the optimal set of physical resource requirements at network elements. Two algorithms are proposed to obtain the optimal/near-optimal solutions of the MINLP model. Second, we address the multi-SFC embedding problem by a game theoretical approach, considering the heterogeneity of NFV nodes, the effect of processing-resource sharing among various VNFs, and the capacity constraints of NFV nodes. In the proposed resource constrained multi-SFC embedding game (RC-MSEG), each SFC is treated as a player whose objective is to minimize the overall latency experienced by the supported service flow, while satisfying the capacity constraints of all its NFV nodes. Due to processing-resource sharing, additional delay is incurred and integrated into the overall latency for each SFC. The capacity constraints of NFV nodes are considered by adding a penalty term into the cost function of each player, and are guaranteed by a prioritized admission control mechanism. We first prove that the proposed game RC-MSEG is an exact potential game admitting at least one pure Nash Equilibrium (NE) and has the finite improvement property (FIP). Then, we design two iterative algorithms, namely, the best response (BR) algorithm with fast convergence and the spatial adaptive play (SAP) algorithm with great potential to obtain the best NE of the proposed game. Third, the VNF scheduling problem is investigated to minimize the makespan (i.e., overall completion time) of all services, while satisfying their different end-to-end (E2E) delay requirements. The problem is formulated as a mixed integer linear program (MILP) which is NP-hard with exponentially increasing computational complexity as the network size expands. To solve the MILP with high efficiency and accuracy, the original problem is reformulated as a Markov decision process (MDP) problem with variable action set. Then, a reinforcement learning (RL) algorithm is developed to learn the best scheduling policy by continuously interacting with the network environment. The proposed learning algorithm determines the variable action set at each decision-making state and accommodates different execution time of the actions. The reward function in the proposed algorithm is carefully designed to realize delay-aware VNF scheduling. To sum up, it is of great importance to integrate SDN and NFV in the same network to accelerate the evolution toward software-enabled network services. We have studied VN topology design, multi-VNF chain embedding, and delay-aware VNF scheduling to achieve efficient resource allocation in different dimensions. The proposed approaches pave the way for exploiting network slicing to improve resource utilization and facilitate QoS-guaranteed service provisioning in SDN/NFV-enabled networks

Traffic-Aware Deployment of Interdependent NFV Middleboxes in Software-Defined Networks

Middleboxes, such as firewalls, Network Address Translators (NATs), Wide Area Network (WAN) optimizers, or Deep Packet Inspector (DPIs), are widely deployed in modern networks to improve network security and performance. Traditional middleboxes are typically hardware based, which are expensive and closed systems with little extensibility. Furthermore, they are developed by different vendors and deployed as standalone devices with little scalability. As the development of networks in scale, the limitations of traditional middleboxes bring great challenges in middlebox deployments. Network Function Virtualization (NFV) technology provides a promising alternative, which enables flexible deployment of middleboxes, as virtual machines (VMs) running on standard servers. However, the flexibility also creates a challenge for efficiently placing such middleboxes, due to the availability of multiple hosting servers, capabilities of middleboxes to change traffic volumes, and dependency between middleboxes. In our first two work, we addressed the optimal placement challenge of NFV middleboxes by considering middlebox traffic changing effects and dependency relations. Since each VM has only a limited processing capacity restricted by its available resources, multiple instances of the same function are necessary in an NFV network. Thus, routing in an NFV network is also a challenge to determine not only via a path from the source to destination but also the service (middlebox) locations. Furthermore, the challenge is complicated by the traffic changing effects of NFV services and dependency relations between them. In our third work, we studied how to efficiently route a flow to receive services in an NFV network. We conducted large-scale simulations to evaluate our proposed solutions, and also implemented a Software-Defined Networking (SDN) based prototype to validate the solutions in realistic environments. Extensive simulation and experiment results have been fully demonstrated the effectiveness of our design

Clustering algorithms for dynamic adaptation of service function chains

Network function virtualization is a pillar-stone of today’s network architectures as it offers better management and elasticity and allows also a flexible maintenance of services running on shared resources over cloud environments. Network functions traditionally hosted on dedicated hardware are now provided over software based components that might run either on virtual machines or on containers. The major advantage of this transition is that it makes the deployment of new services easier while optimizing the management and administration of network architectures. It is much easier to spin up a new virtual machine/container hosting a network function or a specific application described as a service function chain, than to deploy a new hardware based equipment and checking its compatibility with the rest of the architecture. With all the advantages that this new paradigm offers comes a set of challenges related mainly to: 1) optimizing the resource consumption on the shared infrastructure 2) making the best decision of placing the virtual functions that respects at the same time clients’ requirements and also leverages the available resources on the substrate network in terms of different metrics (e.g., CPU, memory, latency, bandwidth). This aspect of Network Function Virtualization-NFV and Service Function Chains-SFC placement have been treated in so many research works that propose approaches ensuring optimal placement and chaining of VNFs in virtualized networks, but as the adoption of these technologies gets more important in real network setups, and given the strict restrictions of today’s’ applications (e.g. latency highly-sensitive applications, or availability highly-sensitive service, etc.), it is always important to consider all the parameters impacting the network management in cloud environments. In this research project, we develop new approaches for placement and chaining of virtual network functions in cloud-based environments. The first approach allows forming on demand clusters of servers deployed in a physical infrastructure. These servers are grouped according to their similar attributes (e.g., CPU-intensive server, energy-efficient server, etc). This process is a proactive measure to ensure that SFCs are hosted in servers that meet their specific metrics requirements (CPU, memory, disk, etc.). It employs a meta-heuristic called CRO (Chemical Reaction Optimization) to decide of the best VNF placement guaranteeing optimal resource consumption in terms of CPU / memory. We employ CRO also to ensure the lowest latencies during the routing between the different VNFs. In fact, the E2E delay is an important aspect to consider, as most current applications require low latencies and shortest run times. In the second approach, the clusters are formed using algorithms based on meta-heuristics, including the CRO, allowing to improve the quality of clusters formed in terms of similarity, density and modularity

Low-latency and Resource-efficient Service Function Chaining Orchestration in Network Function Virtualization

© 2014 IEEE. Recently, network function virtualization (NFV) has been proposed to solve the dilemma faced by traditional networks and to improve network performance through hardware and software decoupling. The deployment of the service function chain (SFC) is a key technology that affects the performance of virtual network function (VNF). The key issue in the deployment of SFCs is proposing effective algorithms to achieve efficient use of resources. In this article, we propose an SFC deployment optimization (SFCDO) algorithm based on a breadth-first search (BFS). The algorithm first uses a BFS-based algorithm to find the shortest path between the source node and the destination node. Then, based on the shortest path, the path with the fewest hops is preferentially chosen to implement the SFC deployment. Finally, we compare the performances with the greedy and simulated annealing (G-SA) algorithm. The experiment results show that the proposed algorithm is optimized in terms of end-to-end delay and bandwidth resource consumption. In addition, we also consider the load rate of the nodes to achieve network load balancing

Energy-Efficient Softwarized Networks: A Survey

With the dynamic demands and stringent requirements of various applications, networks need to be high-performance, scalable, and adaptive to changes. Researchers and industries view network softwarization as the best enabler for the evolution of networking to tackle current and prospective challenges. Network softwarization must provide programmability and flexibility to network infrastructures and allow agile management, along with higher control for operators. While satisfying the demands and requirements of network services, energy cannot be overlooked, considering the effects on the sustainability of the environment and business. This paper discusses energy efficiency in modern and future networks with three network softwarization technologies: SDN, NFV, and NS, introduced in an energy-oriented context. With that framework in mind, we review the literature based on network scenarios, control/MANO layers, and energy-efficiency strategies. Following that, we compare the references regarding approach, evaluation method, criterion, and metric attributes to demonstrate the state-of-the-art. Last, we analyze the classified literature, summarize lessons learned, and present ten essential concerns to open discussions about future research opportunities on energy-efficient softwarized networks.Comment: Accepted draft for publication in TNSM with minor updates and editin

Scalable and Reliable Middlebox Deployment

Middleboxes are pervasive in modern computer networks providing functionalities beyond mere packet forwarding. Load balancers, intrusion detection systems, and network address translators are typical examples of middleboxes. Despite their benefits, middleboxes come with several challenges with respect to their scalability and reliability. The goal of this thesis is to devise middlebox deployment solutions that are cost effective, scalable, and fault tolerant. The thesis includes three main contributions: First, distributed service function chaining with multiple instances of a middlebox deployed on different physical servers to optimize resource usage; Second, Constellation, a geo-distributed middlebox framework enabling a middlebox application to operate with high performance across wide area networks; Third, a fault tolerant service function chaining system