Optimal Network Service Chain Provisioning

International audienceService chains consist of a set of network services, such as firewalls or application delivery controllers, which are interconnected through a network to support various applications. While it is not a new concept, there has been an extremely important new trend with the rise of Software-Defined Network (SDN) and Network Function Virtualization (NFV). The combination of SDN and NFV can make the service chain and application provisioning process much shorter and simpler. In this paper, we study the provisioning of service chains jointly with the number/location of Virtual Network Functions (VNFs). While chains are often built to support multiple applications, the question arises as how to plan the provisioning of service chains in order to avoid data passing through unnecessary network devices or servers and consuming extra bandwidth and CPU cycles. It requires choosing carefully the number and the location of the VNFs. We propose an exact mathematical model using decomposition methods whose solution is scalable in order to conduct such an investigation. We conduct extensive numerical experiments, and show we can solve exactly the routing of service chain requests in a few minutes for networks with up to 50 nodes, and traffic requests between all pairs of nodes. Detailed analysis is then made on the best compromise between minimizing the bandwidth requirement and minimizing the number of VNFs and optimizing their locations using different data sets

UNiS: A User-space Non-intrusive Workflow-aware Virtual Network Function Scheduler

Network Function Virtualization (NFV) has gained a significant research interest in both academia and industry since its inception in the late 2012. One of the key research issues in NFV is the development of systems for building Virtual Network Functions (VNFs) capable of meeting the performance requirements of enterprise and telecommunication networks. New packet processing models leveraging kernel bypass I/O and poll-mode processing have gained popularity for building high-performance VNFs because of their simple programming model and very low I/O overhead. However, a major drawback of such poll-mode processing is the inefficient use of CPU resources. Existing CPU schedulers are ill-suited for VNFs due to their inability to capture the actual processing cost of a pollmode VNF, hence, cannot rightsize the CPU allocation. This is further exacerbated by their inability to consider VNF processing order when VNFs are chained to form Service Function Chains (SFCs). The state-of-the-art solutions proposed for VNF scheduling are intrusive, i.e., requiring the VNFs to be built with scheduler specific libraries or having carefully selected scheduling checkpoints. This highly restricts the VNFs that can properly work with such schedulers. To address these issues, we developed UNiS: a User-space Non-intrusive work-flow aware VNF Scheduler. Unlike existing approaches, UNiS does not require VNF modifications and treats the poll-mode VNFs as a black box, hence, is non-intrusive. UNiS is also workflow-aware, i.e., maintains SFC processing order while scheduling the VNFs. Testbed experiments show that UNiS is able to achieve a throughput within 90% (for synthetic traffic load) and 98% (for real data centre traffic trace) of the achievable throughput using an intrusive co-operative scheduler

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

Provisioning Ultra-Low Latency Services in Softwarized Network for the Tactile Internet

The Internet has made several giant leaps over the years, from a fixed to a mobile Internet, then to the Internet of Things, and now to a Tactile Internet. The Tactile Internet is envisioned to deliver real-time control and physical tactile experiences remotely in addition to conventional audiovisual data to enable immersive human-to-machine interaction and allow skill-set delivery over networks. To realize the Tactile Internet, two key performance requirements, namely ultra-low latency and ultra-high reliability need to be achieved. However, currently deployed networks are far from meeting these stringent requirements and cannot efficiently cope with dynamic service arrivals/departures and the significant growth of traffic demands. To fulfill these requirements, a softwarized network enabled by network function virtualization (NFV) and software-defined network (SDN) technologies is introduced as a new promising concept of a future network due to its flexibility, agility, scalability and cost efficiency. Despite these benefits, provisioning Tactile Internet network services (NSs) in an NFV-based infrastructure remains a challenge, as network resources must be allocated for virtual network function (VNF) deployment and traffic routing in such a way that the stringent requirements are met, and network operator’s objectives are optimized. This problem is also well-known, as NFV resource allocation (NFV-RA) and can be further divided into three stages: (i) VNF composition, (ii) VNF embedding/placement and (iii) VNF scheduling. This thesis addresses challenges on NFV-RA for Tactile Internet NSs, especially ultra-low latency NSs. We first conduct a survey on architectural and algorithmic solutions proposed so far for the Tactile Internet. Second, we propose a joint VNF composition and embedding algorithm to efficiently determine the number of VNF instances to form a VNF forward graph (VNF-FG) and their embedding locations to serve ultra-low latency NSs, as in some cases, multiple instances of each VNF type with proper embedding may be needed to guarantee the stringent latency requirements. The proposed algorithm relies on a Tabu search method to solve the problem with a reasonable time. Third, we introduce real-time VNF embedding algorithms to efficiently support ultra-low latency NSs that require fast service provisioning. By assuming that a VNF-FG is given, our proposed algorithms aim to minimize the cost while meeting the stringent latency requirement. Finally, we focus on a joint VNF embedding and scheduling problem, assuming that ultra-low latency NSs can arrive in the network any time and have specific service deadlines. Moreover, VNF instances once deployed can be shared by multiple NSs. With these assumptions, we aim to optimally determine whether to schedule NSs on already deployed VNFs or to deploy new VNFs and schedule them on newly deployed VNFs to maximize profits while guaranteeing the stringent service deadlines. Two efficient heuristics are introduced to solve this problem with a feasible time