Routing information through networks is a universal phenomenon in both natural and manmade complex systems. When each node has full knowledge of the global network connectivity, finding short communication paths is merely a matter of distributed computation. However, in many real networks nodes communicate efficiently even without such global intelligence. Here we show that the peculiar structural characteristics of many complex networks support efficient communication without global knowledge. We also describe a general mechanism that explains this connection between network structure and function. This mechanism relies on the presence of a metric space hidden behind an observable network. Our findings suggest that real networks in nature have underlying metric spaces that remain undiscovered. Their discovery would have practical applications ranging from routing in the Internet and searching social networks, to studying information flows in neural, gene regulatory networks, or signaling pathways.

he variety and complexity of modern Internet traffic exceeds anything imagined by the original designers of the underlying Internet architecture. As the Internet becomes our most critical communications infrastructure, service providers attempt to retrofit functionality, including security, reliability, privacy, and multiple service qualities, into a "best effort" architecture originally intended to support a research environment. In order to prioritize, protect, or prevent certain traffic, providers need to implement technology for traffic classification: associating traffic flows with the applications -or application types -that generated them. When the focus is on detecting specific applications (e.g., Skype), the term traffic identification is sometimes used. Despite the increasing dependence on the Internet, there is essentially no scientifically reproducible body of research on global Internet traffic characteristics due to the sensitivity of and typical restrictions on sharing traffic data. Despite these constraints, security concerns and economic realities have motivated recent advances in traffic classification capabilities. Situational awareness of traffic is essential to prevention, mitigation, and response to new forms of malware, which can suddenly and rapidly threaten legitimate service on network links. Arguably as important, the high cost of deploying and operating Internet infrastructure compels providers to continually seek ways to optimize their network engineering or otherwise increase return on capital investments, including applicationbased service differentiation and content-sensitive pricing.For these reasons, the state of the art in traffic classification has experienced a major boost in the past few years, measured in the number of publications and research groups focused on the topic. Diverse interests have led to a heterogeneous, fragmented, and somewhat inconsistent landscape. A recent survey of traffic classification literature reviewed advantages and problems with different approaches, but acknowledged their general lack of accuracy and applicability [1], whereas others took a narrower focus, taxonomizing and reviewing documented machine-learning approaches for IP traffic classification [2].In this article we provide a critical but constructive analysis of the field of Internet traffic classification, focusing on major obstacles to progress and suggestions for overcoming them. We first give an overview of both the evolution of traffic classification techniques and constraints to their development. After briefly summarizing results of surveys in this field, we highlight key differences across existing approaches and techniques. We then discuss the main obstacles to progress in the current state of the art, including required trade-offs in applicability, reliability, performance, and respect for privacy. The persistently unsolved challenges in the field over the last decade suggest the need for different strategies and actions, which we recommend in the concluding section. Traffic Cl...

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