With the explosive growth of mobile devices, the need for precise positioning in indoor environments is becoming more prominent. Yet these state-of-the-art solutions require either multiple devices or multiple antennas that not available on all devices. The positioning of devices with simpler structures should be investigated. In this paper, we propose a method to treat channel state information (CSI) data as a random process, construct an autocorrelation matrix and apply the MUSIC algorithm to achieve the purpose of single antenna estimation of time of flight (TOF). We study the distribution of symbol time offset (STO) errors by means of statistical tests and provide a theoretical basis for real experiments. Finally, we use Intel5300 commercial wireless network card to conduct experiments in typical indoor environments of NLOS and LOS, and the median errors of 3.3m and 1.2m are achieved respectively.

Somatic variants act as critical players during cancer occurrence and development. Thus, an accurate and robust method to identify them is the foundation of cutting-edge cancer genome research. However, due to low accessibility and high individual-/sample-specificity of the somatic variants in tumor samples, the detection is, to date, still crammed with challenges, particularly when lacking paired normal samples as control. To solve this burning issue, we developed a tumor-only somatic and germline variant identification method (TSomVar) using the random forest algorithm established on sample-specific variant datasets derived from genotype imputation, reads-mapping level annotation and functional annotation. We trained TSomVar by using genomic variant datasets of three major cancer types: colorectal cancer, hepatocellular carcinoma and skin cutaneous melanoma. Compared with existing tumor-only somatic variant identification tools, TSomVar shows excellent performances in somatic variant detection with higher accuracy and better capability of recalling for test datasets from colorectal cancer and skin cutaneous melanoma. In addition, TSomVar is equipped with the competence of accurately identifying germline variants in tumor samples. Taken together, TSomVar will undoubtedly facilitate and revolutionize somatic variant explorations in cancer research.

The explosive expansion of data center sizes aggravates the power consumption and carbon footprint, which has restricted the sustainable growth of cloud services and seriously troubled data center operators. In recent years, plenty of advanced data center network architectures have been proposed. They usually employ richly-connected topologies and multi-path routing to provide high network capacity. Unfortunately, they also undergo inefficient network energy usage during the traffic valley time. To address the problem, many energy-aware flow scheduling algorithms are proposed recently, primarily considering how to aggregate traffic by flexibly choosing the routing paths, with flows fairly sharing the link bandwidths. In this paper, we leverage software defined network (SDN) technique and explore a new solution to energy-aware flow scheduling, i.e., scheduling flows in the time dimension and using exclusive routing (EXR) for each flow, i.e., a flow always exclusively utilizes the links of its routing path. The key insight is that exclusive occupation of link resources usually results in higher link utilization in high-radix data center networks, since each flow does not need to compete for the link bandwidths with others. When scheduling the flows, EXR leaves flexibility to operators to define the priorities of flows, e.g., based on flow size, flow deadline, etc. Extensive simulations and testbed experiments both show that EXR can effectively save network energy compared with the regular fair-sharing routing (FSR), and significantly reduce the average flow completion time if assigning higher scheduling priorities to smaller flows.

Cloud computing not only requires high-capacity data center networks to accelerate bandwidth-hungry computations, but also causes considerable power expenses to cloud providers. In recent years many advanced data center network architectures have been proposed to increase the network throughput, such as Fat-Tree [1] and BCube [2], but little attention has been paid to the power efficiency of these network architectures. This paper makes the first comprehensive comparison study for typical data center networks with regard to their Network Power Effectiveness(NPE), which indicates the end-to-end bps per watt in data transmission and reflects the tradeoff between power consumption and network throughput. We take switches, server NICs and server CPU cores into account when evaluating the network power consumption. We measure NPE under both regular routing and power-aware routing, and investigate the impacts of topology size, traffic load, throughput threshold in power-aware routing, network power parameter as well as traffic pattern. The results show that in most cases Flattened Butterfly possesses the highest NPE among the architectures under study, and server-centric architectures usually have higher NPEs than Fat-Tree and VL2 architectures. In addition, the sleep-on-idle technique and power-aware routing can significantly improve the NPEs for all the data center architectures, especially when the traffic load is low. We believe that the results are useful for cloud providers, when they design/upgrade data center networks or employ network power management.

The explosive expansion of data center sizes aggravates the power consumption and carbon footprint, which has restricted the sustainable growth of cloud services and seriously troubled data center operators. In recent years, plenty of advanced data center network architectures have been proposed. They usually employ richly-connected topologies and multi-path routing to provide high network capacity. Unfortunately, they also undergo inefficient network energy usage during the traffic valley time. To address the problem, many energy-aware flow scheduling algorithms are proposed recently, primarily considering how to aggregate traffic by flexibly choosing the routing paths, with flows fairly sharing the link bandwidths. In this paper, we leverage software defined network (SDN) technique and explore a new solution to energy-aware flow scheduling, i.e., scheduling flows in the time dimension and using exclusive routing (EXR) for each flow, i.e., a flow always exclusively utilizes the links of its routing path. The key insight is that exclusive occupation of link resources usually results in higher link utilization in high-radix data center networks, since each flow does not need to compete for the link bandwidths with others. When scheduling the flows, EXR leaves flexibility to operators to define the priorities of flows, e.g., based on flow size, flow deadline, etc. Extensive simulations and testbed experiments both show that EXR can effectively save network energy compared with the regular fair-sharing routing (FSR), and significantly reduce the average flow completion time if assigning higher scheduling priorities to smaller flows.

Data centers are becoming a big consumer of the world's electricity because of the explosive growth of their scale, which has become a serious concern. In recent years, many advanced data center network architectures have been proposed, which employ richly-connected topologies and multi-path routing to achieve high bisection bandwidth. Unfortunately, they undergo inefficient network energy usage. To address this issue, many network-wide energy conservation techniques have been proposed in the community. In the paper, we explore a new dimension in saving data center network energy, i.e., letting flows exclusively occupy link resources when transmission and using a flow preemption algorithm to schedule them. The key insight behind this design choice is that exclusively occupying link resources usually results in higher link utilization because there is no collision among the traffic flows. By combining both flow preemption and energy-aware routing, we can save much more network power than with traditional ways.