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Multipath TCP has attracted increasing attention as a promising technology for 5G networks. To fully utilize network interfaces on multi-homed terminals and the whole network resources, MPTCP is proposed as an extension of TCP to transfer packets concurrently over multiple paths. Cross layer optimization techniques have been applied for MPTCP such as routing and path management. However, existing multipath routing algorithms and network modeling techniques are facing the challenges of subflow asymmetry due to network heterogeneity, thus cannot handle routing optimization problems comprehensively. To address these problems, in this paper, firstly, a novel Graph Neural Network (GNN) based multipath routing model is proposed to explore the complications among links, paths, subflows and the MPTCP connection on various topologies. Leveraging the GNN model, expected throughput can be predicted with given network topology and multipath routes, which can further be the guidance for optimzing the multipath routing. Then, GCLR, a GNN based cross layer optimization system for MPTCP by routing, is proposed with the help of SDN (Software Defined Networking). According to simulation results, our off-line learned GNN model can predict the expected throughput of specific MPTCP connections with very low error. Besides, it's validated that the model has high generalization ability in terms of connection arbitrary and topology arbitrary, it can maintain MSE (mean squared error) at a low level when the situations are not seen during training, which is sufficient for throughput prediction in multipath routing decisions. Finally, the online routing optimization system is realized using SDN, experimental results show that our proposed routing optimization system can achieve significant throughput enhancement compared with traditional multipath routing algorithms. INDEX TERMS Routing, multipath TCP, graph neural network, cross layer optimization, software defined networking.

Lignocellulosic biomass offers the most abundant renewable resource in replacing traditional fossil resources. However, it is still a major challenge to directly convert the lignin component into value-added materials. The availability of plentiful hydroxyl groups in lignin macromolecules and its unique three-dimensional structure make it an ideal precursor for mesoporous biosorbents. In this work, we reported an environmentally friendly and economically feasible method for the fabrication of mesoporous lignin-based biosorbent (MLBB) from lignocellulosic biomass through a SO micro-thermal-explosion process, as a byproduct of microcrystalline cellulose. BET analysis reveal the average pore-size distribution of 5.50nm, the average pore value of 0.35cm/g, and the specific surface area of 186m/g. The physicochemical properties of MLBB were studied by fourier transform infrared spectroscopy (FTIR), attenuated-total-reflection fourier transform infrared spectroscopy (ATR-FTIR), X-ray photoelectron spectroscopy (XPS), and element analysis. These results showed that there are large amounts of sulfonic functional groups existing on the surface of this biosorbent. Pb(II) was used as a model heavy-metal-ion to demonstrate the technical feasibility for heavy-metal-ion removal. Considering that lignocellulosic biomass is a naturally abundant and renewable resource and SO micro-thermal-explosion is a proven technique, this biosorbent can be easily produced at large scale and become a sustainable and reliable resource for wastewater treatment.

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