Service Function Chain Placement in Distributed Scenarios: a Systematic Review

The network function virtualization (NFV) paradigm is an emerging technology that provides network flexibility by allowing the allocation of network functions over commodity hardware, like legacy servers in an IT infrastructure. In comparison with traditional network functions, implemented by dedicated hardware, the use of NFV reduces the operating and capital expenses and improves service deployment. In some scenarios, a complete network service can be composed of several functions, following a specific order, known as a service function chain (SFC). SFC placement is a complex task, already proved to be NP-hard. Moreover, in highly distributed scenarios, the network performance can also be impacted by other factors, such as traffic oscillations and high delays. Therefore, a given SFC placement strategy must be carefully developed to meet the network operator service constraints. In this paper, we present a systematic review of SFC placement advances in distributed scenarios. Differently from the current literature, we examine works over the last 10 years which addressed this problem while focusing on distributed scenarios. We then discuss the main scenarios where SFC placement has been deployed, as well as the several techniques used to create the placement strategies. We also present the main goals considered to create SFC placement strategies and highlight the metrics used to evaluate them.

Impact of Information on Topology-Induced Traffic Oscillations

Previous studies have shown traffic oscillations can be induced by special network topology. In the simplest case, a network of two intersections connected by two parallel roads would produce oscillatory traffic, when the split of drivers between the two roads falls into certain range. To understand how traffic information may affect such oscillations, a subset of drivers is allowed to be “reactive” in this study; that is, their route choice varies according to information about prevailing traffic conditions on the roads. We show that, depending on the ratio of reactive drivers, the system displays six new decaying, periodic oscillatory, or stable patterns. All solutions are obtained analytically in closed form and validated by macroscopic traffic simulation. Of all the solutions discovered, only one both is stable and fully utilizes the road space between the two intersections, and hence it is more desirable than the other solutions. The findings reveal the link between information provision and topology-induced oscillations, which may help practitioners design strategies that contribute to mitigating the adverse impact of such oscillations.

Systematic Evaluation of a Multi-Lane Green-Driving Algorithm in a Mixed Connected Environment

Vehicles traveling under oscillated traffic have low energy efficiency and high air pollutant emissions. Green driving with the help of connected vehicles (CVs) attracts a lot of research effort to improve vehicle energy efficiency. However, it is very challenging to perform green driving on multi-lane freeways under a mixed connected environment. In the researchers’ previous work, one innovative green-driving algorithm was proposed to solve the multi-lane problem with only one CV. In this study, a systematic analysis of the algorithm is conducted to understand its benefits and limitations on smoothing traffic oscillations. The effect of the steady states and the number of lanes is also analyzed. In addition, the algorithm is extended to a more general scenario with multiple CVs. The extended system coordinates multiple CVs to form multiple moving bottlenecks to mitigate traffic oscillation more efficiently as well as providing more realistic instructions to CVs. The evaluation of the extended system concludes with the most effective strategies to control CVs to smooth oscillations.

Deep Reinforcement Learning-Based Vehicle Driving Strategy to Reduce Crash Risks in Traffic Oscillations

The primary objective of this study is to propose a deep reinforcement learning-based driving strategy for individual vehicles to mitigate oscillations and optimize traffic safety in stop-and-go waves. A deep deterministic policy gradient (DDPG)-based driving strategy, which requires information that is directly obtained by in-vehicle sensors, is proposed for system performance optimization. Two typical scenarios were simulated based on simulation software (SUMO): (i) the leading vehicle slowed down according to real trajectory data to produce one oscillation; (ii) the leading vehicle conducted several abrupt decelerations with various degrees of disturbance to produce multiple oscillations. The DDPG agents interacted with the SUMO platform to determine the optimal acceleration of vehicles that can reduce crash risks in various stop-and-go waves. The results showed that the proposed DDPG-based driving strategy successfully reduced the crash risk by 68.9%–78.4%. Scenarios with different penetration rates of DDPG agents and in various flow rates were compared to test the effect of the proposed strategy. The DDPG-based driving strategy reduced crash risk more with the increase of penetration rate and this strategy performed better when applied in the scenario with a high traffic flow rate. The proposed strategy is compared with the adaptive cruise control and jam-absorbing driving strategies. Results showed the proposed strategy outperformed other oscillation mitigating strategies in reducing crash risks.

Defining Highway Node Acceptance Capacity (HNAC): Theoretical Analysis and Data Simulation

A new concept of Highway Node Acceptance Capacity (HNAC) is proposed in this paper inspired by a field data observation. To understand HNAC in microscopic view, boundary condition of successful merging is found using car-following behaviours and lane-changing rules, which could also explain traffic oscillations. In macroscopic view, linear positive relationship between HNAC and background traffic volume is obtained based on moving bottleneck. To determine the explicit form of the relationship, data simulation considering car-following behaviours and traffic flow theory is used. In the results, the synchronization phenomenon of oscillation in on-ramp (with respect to main road) and intersected road is found. The explicit equation of HNAC is determined based on standard deviation and correlation coefficient analysis, and also proved to be accurate with model validation, which is helpful in studies related to propagation mechanism of traffic emergencies on highway network.