Exponential bandwidth scaling has been a fundamental driver of the growth and popularity of the Internet. However, increases in bandwidth have been accompanied by increases in power consumption, and despite sustained system design efforts to address power demand, significant technological challenges remain that threaten to slow future bandwidth growth. In this paper we describe the power and associated heat management challenges in today's routers. We advocate a broad approach to addressing this problem that includes making powerawareness a primary objective in the design and configuration of networks, and in the design and implementation of network protocols. We support our arguments by providing a case study of power demands of two standard router platforms that enables us to create a generic model for router power consumption. We apply this model in a set of target network configurations and use mixed integer optimization techniques to investigate power consumption, performance and robustness in static network design and in dynamic routing. Our results indicate the potential for significant power savings in operational networks by including power-awareness.

The prediction of beta-sheet topology requires the consideration of long-range interactions between beta-strands that are not necessarily consecutive in sequence. Since these interactions are difficult to simulate using ab initio methods, we propose a supplementary method able to assign beta-sheet topology using only sequence information. We envision using the results of our method to reduce the three-dimensional search space of ab initio methods. Our method is based on the signature molecular descriptor, which has been used previously to predict protein-protein interactions successfully, and to develop quantitative structure-activity relationships for small organic drugs and peptide inhibitors. Here, we show how the signature descriptor can be used in a Support Vector Machine to predict whether or not two beta-strands will pack adjacently within a protein. We then show how these predictions can be used to order beta-strands within beta-sheets. Using the entire PDB database with ten-fold cross-validation, we have achieved 74.0% accuracy in packing prediction and 75.6% accuracy in the prediction of edge strands. For the case of beta-strand ordering, we are able to predict the correct ordering accurately for 51.3% of the beta-sheets. Furthermore, using a simple confidence metric, we can determine those sheets for which accurate predictions can be obtained. For the top 25% highest confidence predictions, we are able to achieve 95.7% accuracy in beta-strand ordering. [Figure: see text].

DNS names assigned to interfaces of network devices along an end-to-end path are an important source of information for both operations and research. Our study focuses on the interface DNS names that encode detailed information about the device e.g., interface type, bandwidth, manufacturer. In this paper we describe a methodology for discovering and characterizing the structure of diverse interface DNS names. We extract, organize and assess the details of the encoding used in different networks. The results of our analysis show that many different encodings are used, and that meaningful encodings are common in the core of the Internet. To enable interface DNS name decoding to be used in practice, we incorporate our information extraction library into a new version of traceroute that we call PathAudit.

No abstract

Abstract-The escalation in power consumption of networking and communications equipment is of concern to technologists and environmentalists alike. Understanding how and when networking devices consume power is complicated by their lack of instrumentation. Furthermore, standard networking devices are not typically flexible enough to support experiments with new techniques for reducing power consumption. In this paper, we describe a set of extensions for Linux-based commodity switches that enable a wide range of power-aware experiments in laboratory testbeds. The extensions are based on the requirements for high fidelity in power measurement and in modulation of key subsystems. Our implementation includes two key capabilities: a flexible power consumption model and a traffic shaper that enable emulation of a wide range of power-aware hardware and protocols. To validate our power-awareness extension and demonstrate their capabilities and utility, we built a testbed composed of simple, Linux-based switches. First, we show that the most simple configuration of our emulation extensions report power consumption consistent with what can be measured with an external power meter. Next, we conduct a series of experiments on power consumption when bandwidth is scaled directly to performance demands. Our results confirm our hypothesis that a finer grained approach yields more power savings when the transition cost is low and traffic varies.