Huawei (Paris): Ph.D. position in Energy optimization in IP networks

Ph.D Position: Energy optimization in IP networks  

       Global warming has become a major concern for several decades. Information and communications technology "ICT" sector accounts for between 5% and 9% of the global electricity consumption per year [1], and telecommunication networks is taking an important part. Manufacturers have an urging need to design novel mechanisms/methods to optimize energy. A broad variety of solutions have been proposed to improve energy consumption in networks [2,3,4,5]. Their ultimate goal is to reach the smallest energy consumption as possible and, in particular, a consumption that is proportional to the utilization of devices. Several studies have shown that most of network devices still demonstrate a non-proportional behavior. However, the situation is quickly evolving with the integration of energy-saving mechanisms in hardware components. On top of ongoing hardware improvements, new network management and network control solutions will have to be developed to fully benefits from them, in particular for:  

• Network design: A well-designed network architecture can reduce the number of active devices and power consumption. Identifying which equipment to replace can also help.
• Network control: Deciding the configuration of network devices allows to dynamic adapt capacity and power consumption to traffic. Computing energy-efficient routes also helps.

 These network design and control solutions depend on multiple factors: network devices and their energy-saving mechanisms, traffic fluctuations and user requirements, etc. In this context, decisions need to be carefully taken as they impact Quality of Service (QoS) and reliability (e.g., tolerance to failures). This PhD thesis aims at investigating the integration of advanced models about energy saving mechanisms into network optimization and control algorithms for the next-generation of telecommunication networks.

Concretely, this PhD thesis on Network Optimization / Operations Research will focus on:  

• Optimization under budget constraints: Telecommunication operators may have budget constraints when it comes to replacing devices in their networks and they have to prioritize upgrades for devices. One approach is to replace those which reached the end of their life cycle analysis (LCA) or are no longer supported by the manufacturer. Another approach consists in upgrading the most critical devices (those carrying the most traffic or providing critical services).
• Robust optimization: Given the stochastic nature of traffic fluctuations, a set of scenarios (such as a multi-period traffic matrix) will be considered to make long term decision "Network design & Device replacement" and short term decisions "Routing & Device frequency selection", at each period of the time horizon.
• Multi-objective optimization: Selecting a low "frequency" for devices can lead to energy savings and cost reduction for telecommunications operators. On the other hand, QoS may be impacted and degraded. Therefore, balancing the objectives of energy efficiency and QoS objectives can be a challenging multi-objective optimization problem.

Meta-Heuristic and exact methods are two different approaches that can be used to tackle the previous challenging optimization problem. Even though Meta-Heuristic methods do not guarantee optimal solutions, they are usually fast and useful with large-scale or complex problems (Stochastic problems [6], Multi-objective problems [7]). The hybridization [8] could be a powerful approach that combines the strengths of both methods to solve problems in the field of energy efficiency in IP networks. The PhD candidate will first look at exact methods and then develop hybrid ones.

Specific Requirements

Candidates should have a Master degree in Operation Research, Computer Science, or Applied Mathematics from a University or a Grande Ecole. They should have a solid background in Combinatorial Optimization. Knowledge of telecommunications will be appreciated.

English: Operational

Contacts

• LERIA, Université d'Angers : Prof. Jin-Kao Hao (jin-kao.hao@univ-angers.fr)
• Huawei FRC: Dr. Youcef Magnouche (youcef.magnouche@huawei.com), Dr. Sébastien Martin (sebastien.martin@huawei.com)

Application   To apply please send a complete CV, a cover letter, grades of University/Grande Ecole studies, and references. The position is for 3 years starting as soon as possible. Deadline: Application must be submitted as soon as possible. We will continue accepting applications until the position is filled.

Huawei The Huawei France Research Center (PRC) located in Boulogne-Billancourt, Paris area, is responsible for advanced research in the fields of Algorithm and Software design, Aesthetics, MBB & Home devices and Parallel Computing, to create and design the innovative technologies and software platforms.

References

• [1] https://www.enerdata.net/publications/executive-briefing/between-10-and-20-electricity-consumption-ict-sector-2030.html
• [2] Christensen, K., Reviriego, P., Nordman, B., Bennett, M., Mostowfi, M., Maestro, J.A.: IEEE 802.3 az: the road to energy efficient ethernet. IEEE Communications Magazine 48(11), 50?56 (2010).
• [3] R. Wang, Z. Jiang, S. Gao, W. Yang, Y. Xia and M. Zhu, "Energy-aware routing algorithms in Software-Defined Networks," Proceeding of IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks 2014, 2014, pp. 1-6.    
• [4] Okonor, Obinna, et al. "Dynamic link sleeping reconfigurations for green traffic engineering." International Journal of Communication Systems 30.9: e3224 (2017).
• [5] Zhang, Jinhong, et al. "TEAP: Traffic engineering and ALR policy based power-aware solutions for green routing and planning problems in backbone networks." Computer Communications 173: 27-44  (2021).
• [6] P Balaprakash et al. "Estimation-based local search for stochastic combinatorial optimization using delta evaluations: A case study on the probabilistic traveling salesman problem." Informs Journal on Computing 20(4): 499-666  (2008).
• [7] V Barichard, JK Hao. "Population and interval constraint propagation algorithm for multi-objective optimization." Metaheuristics International Conference-MIC 2003.    
• [8] M Vasquez, JK Hao. "A hybrid approach for the zero-one multidimensional knapsack problem." Proceedings of the 17th International Joint Conference on Artificial Intelligence IJCAI 2001, pp. 328-333.