Emerging mega-trends (e.g., mobile, social, cloud, and big data) in information and communication technologies (ICT) are commanding new challenges to future Internet, for which ubiquitous accessibility, high bandwidth, and dynamic management are crucial. However, traditional approaches based on manual configuration of proprietary devices are cumbersome and error-prone, and they cannot fully utilize the capability of physical network infrastructure. Recently, software-defined networking (SDN) has been touted as one of the most promising solutions for future Internet. SDN is characterized by its two distinguished features, including decoupling the control plane from the data plane and providing programmability for network application development. As a result, SDN is positioned to provide more efficient configuration, better performance, and higher flexibility to accommodate innovative network designs. This paper surveys latest developments in this active research area of SDN. We first present a generally accepted definition for SDN with the aforementioned two characteristic features and potential benefits of SDN. We then dwell on its three-layer architecture, including an infrastructure layer, a control layer, and an application layer, and substantiate each layer with existing research efforts and its related research areas. We follow that with an overview of the de facto SDN implementation (i.e., OpenFlow). Finally, we conclude this survey paper with some suggested open research challenges.
Background Train is a common mode of public transport across the globe; however, the risk of COVID-19 transmission among individual train passengers remains unclear. Methods We quantified the transmission risk of COVID-19 on high-speed train passengers using data from 2,334 index patients and 72,093 close contacts who had co-travel times of 0–8 hours from 19 December 2019 through 6 March 2020 in China. We analysed the spatial and temporal distribution of COVID-19 transmission among train passengers to elucidate the associations between infection, spatial distance, and co-travel time. Results The attack rate in train passengers on seats within a distance of 3 rows and 5 columns of the index patient varied from 0 to 10.3% (95% confidence interval [CI] 5.3% – 19.0%), with a mean of 0.32% (95%CI 0.29% – 0.37%). Passengers in seats on the same row as the index patient had an average attack rate of 1.5% (95%CI 1.3% – 1.8%), higher than that in other rows (0.14%, 95%CI 0.11% – 0.17%), with a relative risk (RR) of 11.2 (95%CI 8.6 –14.6). Travellers adjacent to the index patient had the highest attack rate (3.5%, 95%CI 2.9% – 4.3%) of COVID-19 infections (RR 18.0, 95%CI 13.9 – 23.4) among all seats. The attack rate decreased with increasing distance, but it increased with increasing co-travel time. The attack rate increased on average by 0.15% (p = 0.005) per hour of co-travel; for passengers at adjacent seats, this increase was 1.3% (p = 0.008), the highest among all seats considered. Conclusions COVID-19 has a high transmission risk among train passengers, but this risk shows significant differences with co-travel time and seat location. During disease outbreaks, when travelling on public transportation in confined spaces such as trains, measures should be taken to reduce the risk of transmission, including increasing seat distance, reducing passenger density, and use of personal hygiene protection.
Multihoming is often used by large enterprises and stub ISPs to connect to the Internet. In this paper, we design a series of novel smart routing algorithms to optimize cost and performance for multihomed users. We evaluate our algorithms through both analysis and extensive simulations based on realistic charging models, traffic demands, performance data, and network topologies. Our results suggest that these algorithms are very effective in minimizing cost and at the same time improving performance. We further examine the equilibrium performance of smart routing in a global setting and show that a smart routing user can improve its performance without adversely affecting other users.
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