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This paper presents the latest progress on characterization of our membrane assisted phase-change heat sink (MHS) at conditions suitable for implementation in data centers (DCs). Experiments are conducted using water as the working fluid at a vapor space pressure ($P_{vapor}$) of 16 kPa, corresponding to a saturation temperature of $\sim$ 55$^{\circ}$C. This temperature is sufficiently lower than the silicon junction temperature of ~80$^{\circ}$C. As anticipated, the overall performance of MHS at sub-atmospheric pressure is lower compared to analogous tests at atmospheric pressure. In agreement with previous studies on MHS, the critical heat flux limit (CHF) increases with enhancement of the heat transfer area ratio ($A_r$) and liquid space pressure ($P_{pool}$). We report a maximum CHF of 670 W/cm$^2$ on a surface with an enhanced area ratio of 3.45, multiple times greater than the CHF reported hitherto by a comparable two-phase heat sink in literature. Heat transfer coefficients (HTC) as high as $\sim$ 1 MW/m$^2$-K are obtained. These record performance data along with unique characteristics of the MHS promise to greatly benefit next generation highly energy efficient DCs.

Usage of mobile wireless Internet has grown very fast in recent years. This radical change in availability of Internet has led to communication of big amount of data over mobile networks and consequently new challenges and opportunities for modeling of mobile Internet characteristics. While the traditional approach toward network modeling suggests finding a generic traffic model for the whole network, in this paper, we show that this approach does not capture all the dynamics of big mobile networks and does not provide enough accuracy. Our case study based on a big dataset including billions of netflow records collected from a campus-wide wireless mobile network shows that user interests acquired based on accessed domains and visited locations as well as user behavioral groups have a significant impact on traffic characteristics of big mobile networks. For this purpose, we utilize a novel graph-based approach based on KS-test as well as a novel co-clustering technique. Our study shows that interest-based modeling of big mobile networks can significantly improve the accuracy and reduce the KS distance by factor of 5 comparing to the generic approach.

In this opinion piece, we discuss recent advances in experimental methods for characterizing phase change heat transfer. We begin with a survey of techniques for high-resolution measurements of temperature and heat flux at the solid surface and in the working fluid. Next, we focus on diagnostic tools for boiling heat transfer and describe techniques for visualizing the temperature and velocity fields, as well as measurements at the single bubble level. Finally, we discuss techniques to probe the kinetics of vapor formation within a few molecular layers of the interface. We conclude with our outlook for future progress in experimental methods for phase change heat transfer.

User online behavior and interests will play a central role in future mobile networks. We introduce a systematic method for large-scale multi-dimensional analysis of online activity for thousands of mobile users across 79 buildings over a variety of web domains. We propose a modeling approach based on self-organizing maps (SOM) for discovering, organizing and visualizing different mobile users' trends from billions of WLAN records. We find surprisingly that users' trends based on domains and locations can be accurately modeled using a self-organizing map with clearly distinct characteristics. We also find many non-trivial correlations between different types of web domains and locations. Based on our analysis, we introduce a mixture model as an initial step towards realistic simulation of wireless network usage.

Design and simulation of future mobile networks will center around human interests and behavior. We propose a design paradigm for mobile networks driven by realistic models of users' on-line behavior, based on mining of billions of wireless-LAN records. We introduce a systematic method for large-scale multi-dimensional coclustering of web activity for thousands of mobile users at 79 locations. We find surprisingly that users can be consistently modeled using ten clusters with disjoint profiles. Access patterns from multiple locations show differential user behavior. This is the first study to obtain such detailed results for mobile Internet usage., Comment: 10 pages, 10 figures