To accommodate heterogeneous and sophisticated network services, Network Function Virtualization (NFV) is invented as a hopeful networking technology. The most distinct feature of NFV is that it separates network functions from physical hardware. In the NFV architecture, various types of Virtual Network Functions (VNFs) are placed on specific software-based middleboxes by telecom providers. Traffic traverses through a sequence of Virtual Network Functions (VNFs) in pre-defined order, which is named as Service Function Chain (SFC). However, how to effectively place VNFs at different locations and steer SFC requests while minimizing energy consumption is still an open problem. Accordingly, we investigate on the joint optimization of VNF placement and traffic steering for energy efficiency in telecom networks. We first present the power consumption model in NFV-enabled telecom networks, and then formulate the studied problem as an Integer Linear Programming (ILP) model. Since the problem is proved as NP-hard, we design a polynomial algorithm that can achieve near-optimal performances based on the Markov approximation technique. In addition, our algorithm can be extended to an online version to serve dynamic arriving SFC requests. The online algorithm achieves a near-optimal long-term averaged performance. Extensive simulation results show that compared with the benchmark algorithms, in the offline and online scenario, our algorithm can reduce up to 14.08 and 13.72 percent power consumption in telecom networks, respectively.

Segment Routing (SR) is an emerging source routing based tunneling technique, which allows source router to steer traffic by encoding segment list in the packet header. Due to its fine-grained control of routing path, SR can be leveraged to facilitate the deployment of Service Function Chains (SFCs). Using SR, multiple segments compose a specific path delivering traffic along a set of ordered Virtual Network Function (VNF) instances. However, when introducing SR into VNF chaining, the segment list depth of SR may face the scalability problem since traffic flows must be steered to traverse a serial of ordered VNFs. To address this problem, we study on segment routing optimization for VNF chaining. Our objective is to minimize the packet overhead of SR for all SFC demands, which indicates the scalability performance of SR. We first formulate the problem as an Integer Linear Programming (ILP) model. Since the ILP model is NP-hard, we then propose a heuristic algorithm named Segment Routing for SFC Steering (SR-SFCS), which is based on the method of backtracking and dynamic programming. Extensive simulation results show that compared with the benchmark algorithms, SR-SFCS can reduce the packet overhead by 23.77% in average.

The urban transit routing problem (UTRP) for public transport systems involves finding a set of efficient transit routes to meet customer demands. The UTRP is an NP-hard, highly constrained, multi-objective problem, for which the evaluation of candidate route sets can prove both time consuming and challenging, with many potential solutions rejected on the grounds of infeasibility. In this paper we propose a simple evolutionary multi-objective optimization technique to solve the UTRP. First we present a representation of the UTRP and introduce our two key objectives, which are to minimise both passenger costs and operator costs. Following this, we describe a simple multi-objective optimization algorithm for the UTRP then present experimental results obtained using the Mandl's benchmark data and a larger transport network.