The Internet has led to the creation of a digital society, where (almost) everything is connected and is accessible from anywhere. However, despite their widespread adoption, traditional IP networks are complex and very hard to manage. It is both difficult to configure the network according to predefined policies, and to reconfigure it to respond to faults, load, and changes. To make matters even more difficult, current networks are also vertically integrated: the control and data planes are bundled together. Software-defined networking (SDN) is an emerging paradigm that promises to change this state of affairs, by breaking vertical integration, separating the network's control logic from the underlying routers and switches, promoting (logical) centralization of network control, and introducing the ability to program the network. The separation of concerns, introduced between the definition of network policies, their implementation in switching hardware, and the forwarding of traffic, is key to the desired flexibility: by breaking the network control problem into tractable pieces, SDN makes it easier to create and introduce new abstractions in networking, simplifying network management and facilitating network evolution. In this paper, we present a comprehensive survey on SDN. We start by introducing the motivation for SDN, explain its main concepts and how it differs from traditional networking, its roots, and the standardization activities regarding this novel paradigm. Next, we present the key building blocks of an SDN infrastructure using a bottom-up, layered approach. We provide an in-depth analysis of the hardware infrastructure, southbound and northbound application programming interfaces (APIs), network virtualization layers, network operating systems (SDN controllers), network programming languages, and network applications. We also look at cross-layer problems such as debugging and troubleshooting. In an effort to anticipate the future evolution of this new paradigm, we discuss the main ongoing research efforts and challenges of SDN. In particular, we address the design of switches and control platforms—with a focus on aspects such as resiliency, scalability, performance, security, and dependability—as well as new opportunities for carrier transport networks and cloud providers. Last but not least, we analyze the position of SDN as a key enabler of a software-defined environment.

https://publications.uni.lu/bitstream/10993/20596/1/survey_on_SDN.pdf

Software-Defined Networking: A Comprehensive Survey

Software-Defined Networking (SDN) is an emerging paradigm that promises to change this state of affairs, by breaking vertical integration, separating the network's control logic from the underlying routers and switches, promoting (logical) centralization of network control, and introducing the ability to program the network. The separation of concerns introduced between the definition of network policies, their implementation in switching hardware, and the forwarding of traffic, is key to the desired flexibility: by breaking the network control problem into tractable pieces, SDN makes it easier to create and introduce new abstractions in networking, simplifying network management and facilitating network evolution. In this paper we present a comprehensive survey on SDN. We start by introducing the motivation for SDN, explain its main concepts and how it differs from traditional networking, its roots, and the standardization activities regarding this novel paradigm. Next, we present the key building blocks of an SDN infrastructure using a bottom-up, layered approach. We provide an in-depth analysis of the hardware infrastructure, southbound and northbound APIs, network virtualization layers, network operating systems (SDN controllers), network programming languages, and network applications. We also look at cross-layer problems such as debugging and troubleshooting. In an effort to anticipate the future evolution of this new paradigm, we discuss the main ongoing research efforts and challenges of SDN. In particular, we address the design of switches and control platforms -- with a focus on aspects such as resiliency, scalability, performance, security and dependability -- as well as new opportunities for carrier transport networks and cloud providers. Last but not least, we analyze the position of SDN as a key enabler of a software-defined environment.

House of Quality

‘Sharing economy’ platforms such as Airbnb have recently flourished in the tourism industry. The prominent appearance of sellers' photos on these platforms motivated our study. We suggest that the presence of these photos can have a significant impact on guests' decision making. Specifically, we contend that guests infer the host's trustworthiness from these photos, and that their choice is affected by this inference. In an empirical analysis of Airbnb's data and a controlled experiment, we found that the more trustworthy the host is perceived to be from her photo, the higher the price of the listing and the probability of its being chosen. We also find that a host's reputation, communicated by her online review scores, has no effect on listing price or likelihood of consumer booking. We further demonstrate that if review scores are varied experimentally, they affect guests' decisions, but the role of the host's photo remains significant.

/pdf/trust-and-reputation-in-the-sharing-economy-the-role-of-2qzieayx28.pdf

Trust and Reputation in the Sharing Economy: the Role of Personal Photos in Airbnb

Trust and reputation in the sharing economy: The role of personal photos in Airbnb

Peer-to-peer markets, collectively known as the sharing economy, have emerged as alternative suppliers of goods and services traditionally provided by long-established industries. A central question regards the impact of these sharing economy platforms on incumbent firms. We study the case of Airbnb, specifically analyzing Airbnb’s entry into the short-term accommodation market in Texas and its impact on the incumbent hotel industry. We first explore Airbnb’s impact on hotel revenue, by using a difference- in-differences empirical strategy that exploits the significant spatiotemporal variation in the patterns of Airbnb adoption across city-level markets. We estimate that in Austin, where Airbnb supply is highest, the causal impact on hotel revenue is in the 8-10% range; moreover, the impact is non-uniformly distributed, with lower-priced hotels and those hotels not catering to business travelers being the most affected. We find that this impact materializes through less aggressive hotel room pricing, an impact that benefits all consumers, not just participants in the sharing economy. The impact on hotel prices is especially pronounced during periods of peak demand, such as SXSW. We find that by enabling supply to scale – a differentiating feature of peer-to-peer platforms – Airbnb has significantly crimped hotels’ ability to raise prices during periods of peak demand. Our work provides empirical evidence that the sharing economy is making inroads by successfully competing with, differentiating from, and acquiring market share from incumbent firms.

https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID2872361_code1846454.pdf?abstractid=2366898&mirid=1

The Rise of the Sharing Economy: Estimating the Impact of Airbnb on the Hotel Industry

Peer-to-peer markets, collectively known as the sharing economy, have emerged as alternative suppliers of goods and services traditionally provided by long-established industries. The authors explore the economic impact of the sharing economy on incumbent firms by studying the case of Airbnb, a prominent platform for short-term accommodations. They analyze Airbnb’s entry into the state of Texas and quantify its impact on the Texas hotel industry over the subsequent decade. In Austin, where Airbnb supply is highest, the causal impact on hotel revenue is in the 8%–10% range; moreover, the impact is nonuniform, with lower-priced hotels and hotels that do not cater to business travelers being the most affected. The impact manifests itself primarily through less aggressive hotel room pricing, benefiting all consumers, not just participants in the sharing economy. The price response is especially pronounced during periods of peak demand, such as during the South by Southwest festival, and is due to a di...

/pdf/the-rise-of-the-sharing-economy-estimating-the-impact-of-rssa2tpbnf.pdf

Judging by the millions of reviews left by guests on the Airbnb platform, this "trusted community marketplace" is fulfilling its mission of matching travelers seeking accommodation with hosts who have room to spare remarkably well. Based on our analysis of ratings we collected for over 600,000 properties listed on Airbnb worldwide, we find that nearly 95% of Airbnb properties boast an average user-generated rating of either 4.5 or 5 stars (the maximum); virtually none have less than a 3.5 star rating. We contrast this with the ratings of approximately half a million hotels worldwide that we collected on TripAdvisor, where there is a much lower average rating of 3.8 stars, and more variance across reviews. Considering properties by accommodation type and by location, we find considerable variability in ratings, and observe that vacation rental properties on TripAdvisor have ratings most similar to ratings of Airbnb properties. Last, we consider several thousand properties that are listed on both platforms. For these cross-listed properties, we find that even though the average ratings on Airbnb and TripAdvisor are similar, proportionally more properties receive the highest ratings (4.5 stars and above) on Airbnb than on TripAdvisor. Moreover, there is only weak correlation in the ratings of individual cross-listed properties across the two platforms. Our work is a first step towards understanding and interpreting nuances of user-generated ratings in the context of the sharing economy.

/pdf/a-first-look-at-online-reputation-on-airbnb-where-every-stay-2gcawx1qgu.pdf

A First Look at Online Reputation on Airbnb, Where Every Stay is Above Average

Sharing economy platforms have become extremely popular in the last few years, and they have changed the way in which we commute, travel, and borrow among many other activities. Despite their popularity among consumers, such companies are poorly regulated. For example, Airbnb, one of the most successful examples of sharing economy platform, is often criticized by regulators and policy makers. While, in theory, municipalities should regulate the emergence of Airbnb through evidence-based policy making, in practice, they engage in a false dichotomy: some municipalities allow the business without imposing any regulation, while others ban it altogether. That is because there is no evidence upon which to draft policies. Here we propose to gather evidence from the Web. After crawling Airbnb data for the entire city of London, we find out where and when Airbnb listings are offered and, by matching such listing information with census and hotel data, we determine the socio-economic conditions of the areas that actually benefit from the hospitality platform. The reality is more nuanced than one would expect, and it has changed over the years. Airbnb demand and offering have changed over time, and traditional regulations have not been able to respond to those changes. That is why, finally, we rely on our data analysis to envision regulations that are responsive to real-time demands, contributing to the emerging idea of ``algorithmic regulation''.

/pdf/who-benefits-from-the-sharing-economy-of-airbnb-17b9cxm62e.pdf

Who Benefits from the "Sharing" Economy of Airbnb?

We present an approach to differentially private computation in which one does not scale up the magnitude of noise for challenging queries, but rather scales down the contributions of challenging records. While scaling down all records uniformly is equivalent to scaling up the noise magnitude, we show that scaling records non-uniformly can result in substantially higher accuracy by bypassing the worst-case requirements of differential privacy for the noise magnitudes.This paper details the data analysis platform wPINQ, which generalizes the Privacy Integrated Query (PINQ) to weighted datasets. Using a few simple operators (including a non-uniformly scaling Join operator) wPINQ can reproduce (and improve) several recent results on graph analysis and introduce new generalizations (e.g., counting triangles with given degrees). We also show how to integrate probabilistic inference techniques to synthesize datasets respecting more complicated (and less easily interpreted) measurements.

/pdf/calibrating-data-to-sensitivity-in-private-data-analysis-a-3ys6q10dc5.pdf

Davide Proserpio

Papers

The Rise of the Sharing Economy: Estimating the Impact of Airbnb on the Hotel Industry

The Rise of the Sharing Economy: Estimating the Impact of Airbnb on the Hotel Industry

A First Look at Online Reputation on Airbnb, Where Every Stay is Above Average

Who Benefits from the "Sharing" Economy of Airbnb?

Calibrating data to sensitivity in private data analysis: a platform for differentially-private analysis of weighted datasets