Service Function Chaining (SFC) dynamically links multiple Virtual Network Functions (VNFs) to provide flexible and scalable network services for network entities and users. Implementing SFCs at the network edge provides instant VNF service yet is confined by the limited edge resources. Existing strategies suggest either to deploy new VNFs for diverse service provision or to deploy more installed VNFs for reliable service provision. However, these one-sided optimizations fail to realize comprehensive improvements in the network service quality. To this end, the motivation of this paper is to consider a more comprehensive SFC deployment plan to provide more efficient network services. In this paper, we propose DeepSFC, an online SFC deployment scheme at network edge. Our DeepSFC considers the impact of resource allocations and deployment locations on the average latency of overall service requests. It realizes an elegant trade-off between the diversity and the availability of SFCs by adopting the Deep Reinforcement Learning (DRL) method. To be specific, we first determine the type and number of VNFs that need to be deployed. Thereafter, we optimize the deployment locations of these chosen VNFs in the service chain, considering the impact of dynamic bandwidth in the real network. For more general scenarios wherein users’ service requirements change or the deployed server crashes, we further relocate the VNF deployment with the joint consideration of performance degradation and migration cost. Evaluation results show that DeepSFC outperforms its competitors in various experimental settings and responds the requests with lower average latency.

The unmanned combat system-of-systems (UCSoS) in modern warfare is comprised of various interconnected entities that work together to support mission accomplishment. The soaring number of entities makes the UCSoS fragile and susceptible to triggering cascading effects when exposed to uncertain disturbances such as attacks or failures. Reconfiguring the UCSoS to restore its effectiveness in a self-coordinated and adaptive manner based on the battlefield situation and operational requirements has attracted increasing attention. In this paper, we focus on the UCSoS reconstruction with heterogeneous costs, where the collaboration nodes may have different reconstruction costs. Specifically, we adopt the heterogeneous network to capture the interdependencies among combat entities and propose a more representative metric to evaluate the UCSoS reconstruction effectiveness. Next, we model the combat network reconstruction problem with heterogeneous costs as a nonlinear optimization problem and prove its NP-hardness. Then, we propose an approach called SoS-Restorer, which is based on deep reinforcement learning (DRL), to address the UCSoS reconstruction problem. The results show that SoS-Restorer can quickly generate reconstruction strategies and improve the operational capabilities of the UCSoS by about 20∼60% compared to the baseline algorithm. Furthermore, even when the size of the UCSoS exceeds that of the training data, SoS-Restorer exhibits robust generalization capability and can efficiently produce satisfactory results in real time.

Traditional network functions are typically implemented using specialized hardware appliances, which are expensive and difficult to upgrade. Network Function Virtualization (NFV) offers an effective approach to address these challenges by implementing comparable functionalities on commercial servers through software-based virtualization. In NFV, a sequence of Virtual Network Functions (VNFs) is orchestrated to form a Service Function Chain (SFC) that provides flexible network services. However, scheduling SFCs with multiple resource constraints to achieve high reliability poses a critical challenge. Existing approaches often assume offline scheduling and overlook the dynamic nature of heterogeneous resource loads across regions. Moreover, they primarily focus on individual VNFs rather than considering the cross-region scheduling of the entire SFC, which can result in increased transmission delay. In this paper, we investigate the problem of service function chain scheduling across multiple regions (SFCS-MR) with deadline constraints, aiming to maximize the success rate of requests. We formulate this problem as an Integer Linear Programming (ILP) model and prove its NP-hardness. To address this problem effectively, we propose a two-stage algorithm that determines whether an SFC requires cross-region scheduling and selects the suitable regions for its execution. Through extensive experimental evaluations, we demonstrate that our cross-region SFC scheduling solution can achieve a maximum improvement of 32.42% in the overall request success rate compared to benchmarks.

The emergence of large-scale dynamic sets in networked and distributed applications attaches stringent requirements to approximate set representation. The existing data structures (including Bloom filter, Cuckoo filter, and their variants) preserve a tight dependency between the cells or buckets for an element and the lengths of the filters. This dependency, however, degrades the capacity elasticity, space efficiency and design flexibility of these data structures when representing dynamic sets. In this paper, we first propose the Index-Independent Cuckoo filter (I2CF), a probabilistic data structure that decouples the dependency between the length of the filter and the indices of buckets which store the information of elements. At its core, an I2CF maintains a consistent hash ring to assign buckets to the elements and generalizes the Cuckoo filter by providing optional ${k}$ candidate buckets to each element. By adding and removing buckets adaptively, I2CF supports the bucket-level capacity alteration for dynamic set representation. Moreover, in case of a sudden increase or decrease of set cardinality, we further organize multiple I2CFs as a Consistent Cuckoo filter (CCF) to provide the filter-level capacity elasticity. By adding untapped I2CFs or merging under-utilized I2CFs, CCF is capable of resizing its capacity instantly. The trace-driven experiments indicate that CCF outperforms its alternatives and realizes our design rationales for dynamic set representation simultaneously, at the cost of a little higher complexity.

Each flow usually needs to traverse a specific service function chain (SFC), which is composed of multiple network functions implemented through virtualization technology or hardware, before reaching their destinations. All network functions are deployed across commodity nodes inside a network environment. Each flow needs to change its default routing path to visit the corresponding SFC correctly. These changed routing paths will cause network load imbalance. Therefore, an intelligent routing planning method is needed to balance the traffic load while satisfying various SFC requirements of different flows. In this paper, we propose to leverage SRv6, a new routing technology, to centrally plan the routing path for each flow with any SFC request. We then present a general model of the SFC planning problem (SFCP), planning flows’ routing paths to minimize the maximum link utilization of the network, and prove that the problem is NP-hard. For this reason, we transform the SFCP problem into a graph theory optimization problem and propose SFCPlanner, an online SFC planning method based on deep reinforcement learning. Moreover, we design the node mask and incremental training mechanisms to make SFCPlanner achieve better performance. The experiment results show that our SFCPlanner can solve the SFCP problem in large-scale networks more precisely. It can reduce the maximum link utilization by 32% compared with the benchmark algorithm while ensuring each flow traverses the correct SFC.