Static load-balanced routing for slimmed fat-trees

Slimmed fat-trees have recently been proposed and deployed to reduce costs in High Performance Computing (HPC) clusters. While existing static routing schemes such as destination-mod-k (D-mod-k) routing are load-balanced and effective for full bisection bandwidth fat-trees, they incur significant load imbalance in many slimmed fat-trees. In this work, we propose a static load balanced routing scheme, called Round-Robin Routing (RRR$RRR$), for 2$2$- and 3$3$-level extended generalized fat-trees (XGFTs), which represent many fat-tree variations including slimmed fat-trees. RRR$RRR$ achieves near perfect load-balancing for any such XGFT in that links at the same level of a tree carry traffic from almost the same number of source–destination pairs. Our evaluation results indicate that on many slimmed fat-trees, RRR$RRR$ is significantly better than D-mod-k for dense traffic patterns due to its better load-balancing property, but performs worse for sparse patterns. We develop a combined routing scheme that enjoys the strengths of both RRR$RRR$ and D-mod-k by using RRR$RRR$ in conjunction with D-mod-k. The combined routing is a robust load-balanced routing scheme for slimmed fat-trees: it performs similar to D-mod-k for sparse traffic patterns and to RRR$RRR$ for dense patterns.     

Green framework for future heterogeneous wireless networks

Energy-efficient communication has sparked tremendous interest in recent years as one of the main design goals of future wireless Heterogeneous Networks (HetNets). This has resulted in paradigm shift of current operation from data oriented to energy-efficient oriented networks. In this paper, we propose a framework for green communications in wireless HetNets. This framework is cognitive in holistic sense and aims at improving energy efficiency of the whole system, not just one isolated part of the network. In particular, we propose a cyclic approach, named as energy-cognitive cycle, which extends the classic cognitive cycle and enables dynamic selection of different available strategies for reducing the energy consumption in the network while satisfying the quality of service constraints.     

A metaheuristic approach for optical network optimization problems

Two of the most complex optimization problems encountered in the design of third generation optical networks are the dynamic routing and wavelength assignment (DRWA) problem under the assumptions of ideal and non-ideal physical layers. Both these problems are NP-complete in nature. These are challenging due to the presence of multiple local optima in the search space. Even heuristics-based algorithms fail to solve these problems efficiently as the search space is non-convex. This paper reports the performance of a metaheuristic, that is, an evolutionary programming algorithm in solving different optical network optimization problems. The primary motivation behind adopting this approach is to reduce the algorithm execution time. It is demonstrated that the same basic approach can be used to solve different optimization problems by designing problem-specific fitness functions. Also, it is shown how the algorithm performance can be improved by integrating suitable soft constraints with the original constraints. Exhaustive simulation studies are carried out assuming the presence of different levels of linear impairments such as switch and demultiplexer crosstalk and non-linear impairments like four wave mixing to illustrate the superiority of the proposed algorithms.     

Strategic Reasoning: Building Cognitive Models from Logical Formulas

This paper presents an attempt to bridge the gap between logical and cognitive treatments of strategic reasoning in games. There have been extensive formal debates about the merits of the principle of backward induction among game theorists and logicians. Experimental economists and psychologists have shown that human subjects, perhaps due to their bounded resources, do not always follow the backward induction strategy, leading to unexpected outcomes. Recently, based on an eye-tracking study, it has turned out that even human subjects who produce the outwardly correct ‘backward induction answer’ use a different internal reasoning strategy to achieve it. The paper presents a formal language to represent different strategies on a finer-grained level than was possible before. The language and its semantics help to precisely distinguish different cognitive reasoning strategies, that can then be tested on the basis of computational cognitive models and experiments with human subjects. The syntactic framework of the formal system provides a generic way of constructing computational cognitive models of the participants of the Marble Drop game.     

Design and simulation of a thermo transfer type MEMS based micro flow sensor for arterial blood flow measurement

Thermo transfer type MEMS (Micro Electro Mechanical System) based micro flow sensing device have promising potential to solve the limitation of implantable arterial blood flow rate monitoring. The present paper emphasizes on modeling and simulation of MEMS based micro flow sensing device, which will be capable of implantable arterial blood flow rate measurement. It describes the basic design and model architecture of thermal type micro flow sensor. A pair of thin film micro heaters is designed through MEMS micro machining process and simulated using CoventorWare; a finite element based numerical code. A rectangular cross section micro channel has been modeled where in micro heater and thermal sensors are embedded using the same CoventorWare tools. Some promising and interesting results of thermal dissipation depending upon very small amount of flow rate through the micro channel are investigated. It is observed that measuring the variation of temperature difference between downstream and upstream, the variation of fluid flow rate in the micro channel can be measured. The numerical simulation results also shows that the temperature distribution profile of the heated surface depends upon microfluidic flow rate i.e. convective heat transfer is directly proportional to the microfluidic flow rate on the surface of the insulating membrane. The simplified analytical model of the thermo transfer type flow sensor is presented and verified by simulation results, which are very promising for application in arterial blood flow rate measuring in implantable micro devices for continuous monitoring of cardiac output.     

Prediction of quality characteristics of laser drilled holes using artificial intelligence techniques

Micro-drilling using lasers finds widespread industrial applications in aerospace, automobile, and bio-medical sectors for obtaining holes of precise geometric quality with crack-free surfaces. In order to achieve holes of desired quality on hard-to-machine materials in an economical manner, computational intelligence approaches are being used for accurate prediction of performance measures in drilling process. In the present study, pulsed millisecond Nd:YAG laser is used for micro drilling of titanium alloy and stainless steel under identical machining conditions by varying the process parameters such as current, pulse width, pulse frequency, and gas pressure at different levels. Artificial intelligence techniques such as adaptive neuro-fuzzy inference system (ANFIS) and multi gene genetic programming (MGGP) are used to predict the performance measures, e.g. circularity at entry and exit, heat affected zone, spatter area and taper. Seventy percent of the experimental data constitutes the training set whereas remaining thirty percent data is used as testing set. The results indicate that root mean square error (RMSE) for testing data set lies in the range of 8.17–24.17% and 4.04–18.34% for ANFIS model MGGP model, respectively, when drilling is carried out on titanium alloy work piece. Similarly, RMSE for testing data set lies in the range of 13.08–20.45% and 6.35–10.74% for ANFIS and MGGP model, respectively, for stainless steel work piece. Comparative analysis of both ANFIS and MGGP models suggests that MGGP predicts the performance measures in a superior manner in laser drilling operation and can be potentially applied for accurate prediction of machining output.

     

Lubricated wear of PTFE and graphited PTFE

Tribological behaviour of PTFE and graphited PTFE have been evaluated under lubricated conditions and compared with dry wear. The stress selected is in the range of 0.44 to 1.64 MPa, and the data generated are relevant to conformal contacts. Mineral oil lubrication can reduce wear of PTFE by one to two orders of magnitude. With graphited PTFE the lubricant effect is lower in comparison to that observed with PTFE. Specific wear rates are plotted on a histogram.