Abstract-Current data centers usually operate under poor utilization due to resource fragmentation. The hierarchical nature of data centers places a limit on the achievable aggregate bandwidth in the backbone. Suboptimal virtual machine placement also introduces unnecessary cross network traffic. In this paper, we solve a joint tenant (i.e., server or virtual machine) placement and route selection problem by exploiting multipath routing capability and dynamic virtual machine migration. These two complementary degrees of freedom: placement and routing, are mutually-dependent, and their joint optimization turns out to substantially improve data center efficiency. We propose (i) an offline algorithm that solves a static problem given a network snapshot, and (ii) an online solution for a dynamic environment with changing traffic. Leveraging and expanding the technique of Markov approximation, we propose an efficient online algorithm that requires a very small number of virtual machine migrations. Performance evaluation that employs the synthesized data center traffic traces, on various topologies and under a spectrum of elephant and mice workloads, demonstrates a consistent and significant improvement over the benchmark achieved by common heuristics used in today's data centers.

In physical design, human designers typically place macros via trial and error, which is a Markov decision process. Reinforcement learning (RL) methods have demonstrated superhuman performance on the macro placement. In this paper, we propose an extension to this prior work [1]. We first describe the details of the policy and value network architecture. We replace the force-directed method with DREAMPlace for placing standard cells in the RL environment. We also compare our improved method with other academic placers on public benchmarks.

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The Internet is a hierarchical architecture comprising heterogeneous entities of privately owned infrastructures, where higher level Internet service providers (ISPs) supply connectivity to the local ISPs and charge the local ISPs for the transit services. One of the challenging problems facing service providers today is how to increase the profitability while maintaining good service qualities as the network scales up. In this work, we seek to understand the fundamental issues on the "interplay" (or interaction) between ISPs at different tiers. While the local ISPs (which we term peers) can communicate with each other by purchasing the connectivity from transit ISPs, there stands an opportunity for them to set up private peering relationships.Under this competitive framework, we explore the issues on (a) impact of peering relationship, (b) resource distribution, (c) revenue maximization, and (d) condition for network upgrade. Firstly, a generalized model is presented to characterize the behaviors of peers and the transit ISP, in which their economic interests are reflected. We study how a peer can distributively determine its optimal peering strategy. Furthermore, we show how a transit ISP is able to utilize the available information to infer its optimal pricing strategy, under which a revenue maximization is achieved. Two distributed algorithms are proposed to help ISPs to provide a fair and efficient bandwidth allocation to peers, avoiding a resource monopolization of the market.Last but not least, we investigate the above issues in a many-peers-region, i.e., when we scale up the network. We provide insightful evidence to show that the ISPs can still gain profits as they upgrade the network infrastructures. Extensive simulations are carried out to support our theoretical claims.

In a phase-IIa trial, we investigated the influence of 90 days continuous-delivery tenofovir (TFV) intravaginal rings (IVRs) with/without levonorgestrel (LNG) on the genital microbiota of Kenyan women. Eligible women (n = 27; 18–34 years; negative for HIV, sexually transmitted infections, and Amsel-bacterial vaginosis) were randomized 2:2:1 to use of IVRs containing TFV, TFV/LNG, or placebo. Using vaginal wall and IVR swabs at IVR insertion and removal, the genital microbial composition was determined using 16S rRNA gene sequencing. The presence of Candida spp. was determined using qPCR. The vaginal total bacterial burden appeared to decrease with TFV and TFV/LNG IVR use (log100.57 and log100.27 decrease respectively; p > 0.05). The TFV/LNG IVR was more ‘stabilizing’: 50% of the participants’ microbiota community state types remained unchanged and 50% shifted towards higher Lactobacillus abundance. Specifically, TFV/LNG IVR use was accompanied by increased abundances of Lactobacillus gasseri/hominis/johnsonii/taiwanensis (16.3-fold) and L. fermentum/reuteri/vaginalis (7.0-fold; all p < 0.01). A significant shift in the overall microbial α-diversity or β-diversity was not observed for either IVR, and IVR use did not influence Candida spp. prevalence. TFV/LNG and TFV IVRs did not adversely affect the genital microbiota and are safe to use. Our findings support further studies assessing their efficacy in preventing HIV/HSV-2 and unintended pregnancies.