Empowered by network function virtualization (NFV), mobile edge computing aims to provide low latency and ultra reliable network services to mobile end users, achieved as a service function chain (SFC) consisting of a series of ordered virtual network functions (VNFs). Due to user mobility, live SFC migration is imperative to avoid Quality of Service (QoS) degradation. Recent advances mainly make separate decisions on VNF node remapping and migration path routing in a heuristic manner, or implement both through reinforcement learning within a single agent of ill-defined policy and action space. In this paper, given next access node, we first formulate the live SFC migration problem as an integer linear programming (ILP) model to achieve optimal solutions. Then, we present HRL-QC, a hierarchical reinforcement learning framework that jointly optimizes VNF destination node remapping, migration path and post-migration service path selections for QoS-aware and cost-efficient live SFC migration. A GCN-Transformer block is introduced to capture long-range VNF-to-physical node dependencies, while a two-level actor-critic design couples the decision-makings through inter-level reward passing. Extensive evaluations show that HRL-QC outperforms the state-of-the-art in energy consumption, migration time, end-to-end service delay, and migration success rate, while remaining within a small margin of the optimal ILP solution.

Network function virtualization (NFV) empowered by virtualization technology can achieve flexible virtual network function (VNF) placement. To improve resource utilization and energy efficiency, different VNFs tend to be co-located on common servers, which inevitably intrigues VNF performance degradation induced by hardware resource competition. The problem of energy-efficient and interference-aware service function chain (SFC) provisioning is considered in this paper and envisioned to yield minimum activated servers and maximum average throughput. It is formulated as a mixed integer linear programming (MILP) model to achieve optimal solutions. Then, a gale-shapley based offline approximation algorithm is designed through bipartite matching, to yield an SFC allocation decision in one go with proved competitive ratio. In online scenario, Transformer and its efficient model Fastformer, combined with Graph Attention Network (GAT) respectively, are introduced into deep reinforcement learning (DRL) structure for the first time to quickly and accurately abstract features of substrate network and SFC. A DRL-based Fastformer-assisted energy efficient and interference aware SFC provisioning (DRL-EI) algorithm is proposed with an elaborately designed reward function to balance energy consumption and VNF interference. Simulations indicate the gap between DRL-EI and MILP is marginal. DRL-EI outperforms state-of-art work in terms of energy consumption, VNF normalized throughput and acceptance rate.

In recent times, it has been witnessed that an increasing number of Internet connected devices impose an huge challenge on Internet of Thing (IoT) networks, which provides diverse and complex network services through edge computing empowered IoT terminals. A network service can be formally represented by Virtualized Network Function Forwarding Graph (VNF-FG) with the advent of Network Function Virtualization (NFV) technology. Previous researches mainly focus on VNF-FG embedding (VNF-FGE) and take VNF-FG as the input. In this paper, we investigate the design of VNF-FG, which is required to be a Directed Acyclic Graph (DAG) and achieved by the IoT terminal, in two scenarios. In static scenario, for a set of traffic flows arriving at the IoT terminal and requesting different network services, an ILP model Ps including loop prevention constraints is well formulated. An approximation algorithm AFGC with competitive ratio O(1+(|R|-1)α), α (0, 1) is then designed, which thoroughly search all key instances to run comprehensive loop break. In dynamic scenario for a flow request on the fly reaching an IoT terminal, an ILP model Pd and another approximation algorithm DFGU with a competitive ratio O(1 + |SCr||Fr| ) are developed, giving priority to reuse of existing topology to generate an augmented VNF-FG of minimum size. Simulation results indicate AFGC and DFGU outperform state-of-art work, and the gap between each of the two algorithms and their respective ILP models is marginal.

To comply with security and performance policies, multicast communication requires inline service that chains an ordered sequence of virtualized network functions (VNFs) with an emerging paradigm of network function virtualization (NFV). As many-to-many multicast pattern is widely used especially in current MEC, multimedia and big data industries, in this paper we focus on multi-source NFV-enabled multicasting in software-defined networks (SDN). By jointly investigating VNF placement and multicast tree routing strategy, it is envisioned to construct service function tree with minimum network resource expenditure while yielding lower delay. We explore this problem in static and online scenarios individually. In static situation with a bunch of multicast requests to be served, a column generation based computing model is first formulated, based on which an approximation algorithm MDNM that leverages logic of Viterbi and puts emphasis on resource optimization and segmental delay satisfaction is developed. In online situation with a single incoming request, another ILP model to enhance VNF instance reuse is established. Then, SVM algorithm giving importance to source determination and VNF instance consolidation is designed to efficiently construct a service function tree with minimum resource usage and delay. Simulation results show that the gap between each of the two proposed algorithms and their respective ILP models is marginal, and both MDNM and SVM outperform the state-of-art work.

To minimize operating expenses (OPEX) and enhance flexibility for network service provisioning, network function virtualization (NFV) is used to chain an ordered sequence of virtualized network functions (VNFs), also known as service function chain (SFC), which can be placed on commodity servers. As a highly complementary to NFV, software-defined networking (SDN) can offer an agile way of VNF orchestration with the capability of fine-granularity network control over flows. However, one-step migration to SDN is impossible in ISP networks. Thus, in this paper we resort to hybrid SDN to implement SFC provisioning. By jointly optimizing SDN deployment that largely determines capital expenditures (CAPEX) and VNF placement that decides OPEX, we manage to make a tradeoff between CAPEX and OPEX, and to find out an appropriate SDN deployment rate for network operator. We first formulate the problem as an integer linear programming (ILP) model$\boldsymbol {P}$, then, reformulate it with column generation technique and decomposition theory to develop a distributed approximation algorithm CGPD which obtains a tight upper bound of optimal solution to$\boldsymbol {P}$. In order to better decrease CAPEX, a dynamic programming-based heuristic EOES giving importance to effective resource sharing is further designed based on a hidden Markov model. The simulation results indicate that, the difference between$\boldsymbol {P}$and CGPD is marginal. CGPD outperforms EOES in terms of total cost, average delay and bandwidth utilization, and EOES on the other hand demands 20% SDN deployment rate which is half of that of CGPD.