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A combination of catalytic oxidation and extraction in ionic liquid (IL) was used for the removal of benzothiophene (BT), dibenzothiophene (DBT), and 4,6-dimethyldibenzothiophene (4,6-DMDBT) from the model oil. Three peroxophosphomolybdates+ and [C 16 H 33 NC 5 H 5 ] + ) were synthesized and characterized. In the catalytic oxidation desulfurization (CODS) system containing the peroxophosphomolybdate with short alkyl chain ([(C 4 H 9 ) 4 N] 3 {PO 4 [MoO(O 2 ) 2 ] 4 }) and H 2 O 2 , the process exhibited low sulfur removal (16.8%). However, with addition of 1-butyl-3-methylimidazolium tetrafluoroborate ([Bmim]BF 4 ), the extraction and catalytic oxidative desulfurization (ECODS) system remarkably increased the removal of sulfur to 97.3% (with stoichiometric amounts of H 2 O 2 ). The process was superior to the simple extraction with IL (16.3%). The results demonstrated that the ECODS system could deeply remove DBT from the model oil, and this desulfurization system could be recycled 4 times with slight decrease in activity. We also found that the catalysts with short alkyl chains exhibited higher catalytic activity than that with long alkyl chain in the ECODS system. Moreover, the reactivity of sulfur compounds decreased in the order of DBT > 4,6-DMDBT > BT.

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Abstract-In current IaaS datacenters, tenants are suffering unfairness since the network bandwidth is shared in a besteffort manner. To achieve predictable network performance for rented virtual machines (VMs), cloud providers should guarantee minimum bandwidth for VMs or allocate the network bandwidth in a fairness fashion at VM-level. At the same time, the network should be efficiently utilized in order to maximize cloud providers' revenue. In this paper, we model the bandwidth sharing problem as a Nash bargaining game, and propose the allocation principles by defining a tunable base bandwidth for each VM. Specifically, we guarantee bandwidth for those VMs with lower network rates than their base bandwidth, while maintaining fairness among other VMs with higher network rates than their base bandwidth. Based on rigorous cooperative game-theoretic approaches, we design a distributed algorithm to achieve efficient and fair bandwidth allocation corresponding to the Nash bargaining solution (NBS). With simulations under typical scenarios, we show that our strategy can meet the two desirable requirements towards predictable performance for tenants as well as high utilization for providers. And by tuning the base bandwidth, our solution can enable cloud providers to flexibly balance the tradeoff between minimum guarantees and fair sharing of datacenter networks.