Bruno D. Abrahao

Self-Adaptive SLA-Driven Capacity Management for Internet Services

This work considers the problem of hosting multiple third-party Internet services in a cost-effective manner so as to maximize a providers business objective. For this purpose, we present a dynamic capacity management framework based on an optimization model, which links a cost model based on SLA contracts with an analytical queuing-based performance model, in an attempt to adapt the platform to changing capacity needs in real time. In addition, we propose a two-level SLA specification for different operation modes, namely, normal and surge, which allows for per-use service accounting with respect to requirements of throughput and tail distribution response time. The cost model proposed is based on penalties, incurred by the provider due to SLA violation, and rewards, received when the service level expectations are exceeded. Finally, we evaluate approximations for predicting the performance of the hosted services under two different scheduling disciplines, namely FCFS and processor sharing. Through simulation, we assess the effectiveness of the proposed approach as well as the level of accuracy resulting from the performance model approximations

On the intrinsic locality properties of Web reference streams

There has been considerable work done in the study of Web reference streams: sequences of requests for Web objects. In particular, many studies have looked at the locality properties of such streams, because of the impact of locality on the design and performance of caching and prefetching systems. However, a general framework for understanding why reference streams exhibit given locality properties has not yet emerged. In this paper we take a first step in this direction. We propose a framework for describing how reference streams are transformed as they pass through the Internet, based on three operations: aggregation, disaggregation, and filtering. We also propose metrics to capture the temporal locality of reference streams in this framework. We argue that these metrics (marginal entropy and interreference coefficient of variation) are more natural and more useful than previously proposed metrics for temporal locality; and we show that these metrics provide insight into the nature of reference stream transformations in the Web.

Trace complexity of network inference

The network inference problem consists of reconstructing the edge set of a network given traces representing the chronology of infection times as epidemics spread through the network. This problem is a paradigmatic representative of prediction tasks in machine learning that require deducing a latent structure from observed patterns of activity in a network, which often require an unrealistically large number of resources (e.g., amount of available data, or computational time). A fundamental question is to understand which properties we can predict with a reasonable degree of accuracy with the available resources, and which we cannot. We define the trace complexity as the number of distinct traces required to achieve high fidelity in reconstructing the topology of the unobserved network or, more generally, some of its properties. We give algorithms that are competitive with, while being simpler and more efficient than, existing network inference approaches. Moreover, we prove that our algorithms are nearly optimal, by proving an information-theoretic lower bound on the number of traces that an optimal inference algorithm requires for performing this task in the general case. Given these strong lower bounds, we turn our attention to special cases, such as trees and bounded-degree graphs, and to property recovery tasks, such as reconstructing the degree distribution without inferring the network. We show that these problems require a much smaller (and more realistic) number of traces, making them potentially solvable in practice.

On the separability of structural classes of communities

Three major factors govern the intricacies of community extraction in networks: (1) the application domain includes a wide variety of networks of fundamentally different natures, (2) the literature offers a multitude of disparate community detection algorithms, and (3) there is no consensus characterizing how to discriminate communities from non-communities. In this paper, we present a comprehensive analysis of community properties through a class separability framework. Our approach enables the assessement of the structural dissimilarity among the output of multiple community detection algorithms and between the output of algorithms and communities that arise in practice. To demostrate this concept, we furnish our method with a large set of structural properties and multiple community detection algorithms. Applied to a diverse collection of large scale network datasets, the analysis reveals that (1) the different detection algorithms extract fundamentally different structures; (2) the structure of communities that arise in practice is closest to that of communities that random-walk-based algorithms extract, although still siginificantly different from that of the output of all the algorithms; and (3) a small subset of the properties are nearly as discriminative as the full set, while making explicit the ways in which the algorithms produce biases. Our framework enables an informed choice of the most suitable community detection method for a given purpose and network and allows for a comparison of existing community detection algorithms while guiding the design of new ones.

On the internet delay space dimensionality

We investigate the dimensionality properties of the Internet delay space, i.e., the matrix of measured round-trip latencies between Internet hosts. Previous work on network coordinates has indicated that this matrix can be embedded, with reasonably low distortion, into a 4- to 9-dimensional Euclidean space. The application of Principal Component Analysis (PCA) reveals the same dimensionality values. Our work addresses the question: to what extent is the dimensionality an intrinsic property of the delay space, defined without reference to a host metric such as Euclidean space? Is the intrinsic dimensionality of the Internet delay space approximately equal to the dimension determined using embedding techniques or PCA? If not, what explains the discrepancy? What properties of the network contribute to its overall dimensionality? Using datasets obtained via the King [14] method, we study different measures of dimensionality to establish the following conclusions. First, based on its power-law behavior, the structure of the delay space can be better characterized by fractal measures. Second, the intrinsic dimension is significantly smaller than the value predicted by the previous studies; in fact by our measures it is less than 2. Third, we demonstrate a particular way in which the AS topology is reflected in the delay space; subnetworks composed of hosts which share an upstream Tier-1 autonomous system in common possess lower dimensionality than the combined delay space. Finally, we observe that fractal measures, due to their sensitivity to non-linear structures, display higher precision for measuring the influence of subtle features of the delay space geometry.

A separability framework for analyzing community structure

Four major factors govern the intricacies of community extraction in networks: (1) the literature offers a multitude of disparate community detection algorithms whose output exhibits high structural variability across the collection, (2) communities identified by algorithms may differ structurally from real communities that arise in practice, (3) there is no consensus characterizing how to discriminate communities from noncommunities, and (4) the application domain includes a wide variety of networks of fundamentally different natures. In this article, we present a class separability framework to tackle these challenges through a comprehensive analysis of community properties. Our approach enables the assessment of the structural dissimilarity among the output of multiple community detection algorithms and between the output of algorithms and communities that arise in practice. In addition, our method provides us with a way to organize the vast collection of community detection algorithms by grouping those that behave similarly. Finally, we identify the most discriminative graph-theoretical properties of community signature and the small subset of properties that account for most of the biases of the different community detection algorithms. We illustrate our approach with an experimental analysis, which reveals nuances of the structure of real and extracted communities. In our experiments, we furnish our framework with the output of 10 different community detection procedures, representative of categories of popular algorithms available in the literature, applied to a diverse collection of large-scale real network datasets whose domains span biology, online shopping, and social systems. We also analyze communities identified by annotations that accompany the data, which reflect exemplar communities in various domain. We characterize these communities using a broad spectrum of community properties to produce the different structural classes. As our experiments show that community structure is not a universal concept, our framework enables an informed choice of the most suitable community detection method for identifying communities of a specific type in a given network and allows for a comparison of existing community detection algorithms while guiding the design of new ones.

Reputation offsets trust judgments based on social biases among Airbnb users

Significance We investigate the extent to which artificial features engineered by sharing-economy platforms, such as reputation systems, can be used to override people’s tendency to base judgments of trustworthiness on social biases, such as to trust others who are similar (i.e., homophily). To this end, we engaged 8,906 users of Airbnb as volunteers in an online experiment. We demonstrate that homophily based on several demographic characteristics is a relatively weak driver of trust. In fact, having high reputation is enough to counteract homophily. Using Airbnb data, we present evidence that the effects we found experimentally are at work in the actual platform. Lastly, we found an inverse relationship between risk aversion and trust in those with positive reputations. To provide social exchange on a global level, sharing-economy companies leverage interpersonal trust between their members on a scale unimaginable even a few years ago. A challenge to this mission is the presence of social biases among a large heterogeneous and independent population of users, a factor that hinders the growth of these services. We investigate whether and to what extent a sharing-economy platform can design artificially engineered features, such as reputation systems, to override people’s natural tendency to base judgments of trustworthiness on social biases. We focus on the common tendency to trust others who are similar (i.e., homophily) as a source of bias. We test this argument through an online experiment with 8,906 users of Airbnb, a leading hospitality company in the sharing economy. The experiment is based on an interpersonal investment game, in which we vary the characteristics of recipients to study trust through the interplay between homophily and reputation. Our findings show that reputation systems can significantly increase the trust between dissimilar users and that risk aversion has an inverse relationship with trust given high reputation. We also present evidence that our experimental findings are confirmed by analyses of 1 million actual hospitality interactions among users of Airbnb.