Bandwidth management in a hybrid wireless network: For superstore applications

The integration of different wireless networks, such as GSM and WiFi, as a two-tier hybrid wireless network is more popular and economical. Efficient bandwidth management, call admission control strategies and mobility management are important issues in supporting multiple types of services with different bandwidth requirements in hybrid networks. In particular, bandwidth is a critical commodity because of the type of transactions supported by these hybrid networks, which may have varying bandwidth and time requirements. In this paper, we consider such a problem in a hybrid wireless network installed in a superstore environment and design a bandwidth management algorithm based on the priority level, classification of the incoming transactions. Our scheme uses a downlink transaction scheduling algorithm, which decides how to schedule the outgoing transactions based on their priority level with efficient use of available bandwidth. The transaction scheduling algorithm is used to maximize the number of transaction-executions. The proposed scheme is simulated in a superstore environment with multi Rooms. The performance results describe that the proposed scheme can considerably improve the bandwidth utilization by reducing transaction blocking and accommodating more essential transactions at the peak time of the business.

A DMFC with Methanol-Tolerant-Carbon-Supported-Pt-Pd-Alloy Cathode

Methanol-tolerant Pt-Pd alloy catalysts supported on to carbon with varying Pt:Pd atomic ratios of 1:1, 2:1 and 3:1 are prepared by a novel wet-chemical method and characterized using powder XRD, XPS, FESEM, EDAX and TEM techniques. The optimum atomic weight ratio for Pt to Pd in the carbon-supported alloy catalyst as established by linear-sweep voltammetry (LSV) and cell polarization studies is found to be 2:1. A direct methanol fuel cell (DMFC) employing carbon-supported Pt-Pd (2:1) alloy (Pt-Pd/C) catalyst as the cathode catalyst delivers a peak-power density of 115 mW/cm(2) at 70 degrees C as compared to peak-power density of 60 mW/cm(2) obtained with the DMFC employing carbon-supported Pt (Pt/C) catalyst operating under similar conditions. In the literature, DMFCs operating with Pt-TiO2 (2:1)/C and Pt-Au (2:1)/C methanol-tolerant cathodes are reported to exhibit maximum ORR activity among the group of these methanol-tolerant cathodes with varying catalysts compositions. Accordingly, the present study also provides an effective route to design methanol-tolerant-oxygen-reduction catalysts for DMFCs. (C) 2011 The Electrochemical Society. DOI: 10.1149/1.3596542] All rights reserved.

Discovery of Application Workloads from Network File Traces

An understanding of application I/O access patterns is useful in several situations. First, gaining insight into what applications are doing with their data at a semantic level helps in designing efficient storage systems. Second, it helps create benchmarks that mimic realistic application behavior closely. Third, it enables autonomic systems as the information obtained can be used to adapt the system in a closed loop.All these use cases require the ability to extract the application-level semantics of I/O operations. Methods such as modifying application code to associate I/O operations with semantic tags are intrusive. It is well known that network file system traces are an important source of information that can be obtained non-intrusively and analyzed either online or offline. These traces are a sequence of primitive file system operations and their parameters. Simple counting, statistical analysis or deterministic search techniques are inadequate for discovering application-level semantics in the general case, because of the inherent variation and noise in realistic traces.In this paper, we describe a trace analysis methodology based on Profile Hidden Markov Models. We show that the methodology has powerful discriminatory capabilities that enable it to recognize applications based on the patterns in the traces, and to mark out regions in a long trace that encapsulate sets of primitive operations that represent higher-level application actions. It is robust enough that it can work around discrepancies between training and target traces such as in length and interleaving with other operations. We demonstrate the feasibility of recognizing patterns based on a small sampling of the trace, enabling faster trace analysis. Preliminary experiments show that the method is capable of learning accurate profile models on live traces in an online setting. We present a detailed evaluation of this methodology in a UNIX environment using NFS traces of selected commonly used applications such as compilations as well as on industrial strength benchmarks such as TPC-C and Postmark, and discuss its capabilities and limitations in the context of the use cases mentioned above.

Dehydrophenylalanine zippers: strong helix-helix clamping through a network of weak interactions

A decapeptide Boc-L-Ala-(DeltaPhe)(4)-L-Ala-(DeltaPhe)(3)-Gly-OMe (Peptide I) was synthesized to study the preferred screw sense of consecutive alpha,beta-dehydrophenylalanine (DeltaPhe) residues. Crystallographic and CD studies suggest that, despite the presence of two L-Ala residues in the sequence, the decapeptide does not have a preferred screw sense. The peptide crystallizes with two conformers per asymmetric unit, one of them a slightly distorted right-handed 3(10)-helix (X) and the other a left-handed 3(10)-helix (Y) with X and Y being antiparallel to each other. An unanticipated and interesting observation is that in the solid state, the two shape-complement molecules self-assemble and interact with an extensive network of C-H...O hydrogen bonds and pi-pi interactions, directed laterally to the helix axis with amazing regularity. Here, we present an atomic resolution picture of the weak interaction mediated mutual recognition of two secondary structural elements and its possible implication in understanding the specific folding of the hydrophobic core of globular proteins and exploitation in future work on de novo design.

Glassy behavior in neural network models of associative memory

Neural network models of associative memory exhibit a large number of spurious attractors of the network dynamics which are not correlated with any memory state. These spurious attractors, analogous to "glassy" local minima of the energy or free energy of a system of particles, degrade the performance of the network by trapping trajectories starting from states that are not close to one of the memory states. Different methods for reducing the adverse effects of spurious attractors are examined with emphasis on the role of synaptic asymmetry. (C) 2002 Elsevier Science B.V. All rights reserved.

Improving web server performance by network aware data buffering and caching

In this paper we propose a new method of data handling for web servers. We call this method Network Aware Buffering and Caching (NABC for short). NABC facilitates reduction of data copies in web server's data sending path, by doing three things: (1) Layout the data in main memory in a way that protocol processing can be done without data copies (2) Keep a unified cache of data in kernel and ensure safe access to it by various processes and kernel and (3) Pass only the necessary meta data between processes so that bulk data handling time spent during IPC can be reduced. We realize NABC by implementing a set of system calls and an user library. The end product of the implementation is a set of APIs specifically designed for use by the web servers. We port an in house web server called SWEET, to NABC APIs and evaluate performance using a range of workloads both simulated and real. The results show a very impressive gain of 12% to 21% in throughput for static file serving and 1.6 to 4 times gain in throughput for lightweight dynamic content serving for a server using NABC APIs over the one using UNIX APIs.

Multipath routing to provide quality of service in mobile ad hoc networks

Because of frequent topology changes and node failures, providing quality of service routing in mobile ad hoc networks becomes a very critical issue. The quality of service can be provided by routing the data along multiple paths. Such selection of multiple paths helps to improve reliability and load balancing, reduce delay introduced due to route rediscovery in presence of path failures. There are basically two issues in such a multipath routing Firstly, the sender node needs to obtain the exact topology information. Since the nodes are continuously roaming, obtaining the exact topology information is a tough task. Here, we propose an algorithm which constructs highly accurate network topology with minimum overhead. The second issue is that the paths in the path set should offer best reliability and network throughput. This is achieved in two ways 1) by choice of a proper metric which is a function of residual power, traffic load on the node and in the surrounding medium 2) by allowing the reliable links to be shared between different paths.

Multiple scales without center manifold reductions for delay differential equations near Hopf bifurcations

We study small perturbations of three linear Delay Differential Equations (DDEs) close to Hopf bifurcation points. In analytical treatments of such equations, many authors recommend a center manifold reduction as a first step. We demonstrate that the method of multiple scales, on simply discarding the infinitely many exponentially decaying components of the complementary solutions obtained at each stage of the approximation, can bypass the explicit center manifold calculation. Analytical approximations obtained for the DDEs studied closely match numerical solutions.

Neural network based optimal routing algorithm for communication networks

This paper presents the capability of the neural networks as a computational tool for solving constrained optimization problem, arising in routing algorithms for the present day communication networks. The application of neural networks in the optimum routing problem, in case of packet switched computer networks, where the goal is to minimize the average delays in the communication have been addressed. The effectiveness of neural network is shown by the results of simulation of a neural design to solve the shortest path problem. Simulation model of neural network is shown to be utilized in an optimum routing algorithm known as flow deviation algorithm. It is also shown that the model will enable the routing algorithm to be implemented in real time and also to be adaptive to changes in link costs and network topology. (C) 2002 Elsevier Science Ltd. All rights reserved.

A fuzzy control for network overload alleviation

This paper presents a prototype of a fuzzy system for alleviation of network overloads in the day-to-day operation of power systems. The control used for overload alleviation is real power generation rescheduling. Generation Shift Sensitivity Factors (GSSF) are computed accurately, using a more realistic operational load flow model. Overloading of lines and sensitivity of controlling variables are translated into fuzzy set notations to formulate the relation between overloading of line and controlling ability of generation scheduling. A fuzzy rule based system is formed to select the controllers, their movement direction and step size. Overall sensitivity of line loading to each of the generation is also considered in selecting the controller. Results obtained for network overload alleviation of two modified Indian power networks of 24 bus and 82 bus with line outage contingencies are presented for illustration purposes.

Probing the Allosteric Mechanism in Pyrrolysyl-tRNA Synthetase Using Energy-Weighted Network Formalism

Pyrrolysyl-tRNA synthetase (PyIRS) is an atypical enzyme responsible for charging tRNA(Pyl) with pyrrolysine, despite lacking precise tRNA anticodon recognition. This dimeric protein exhibits allosteric regulation of function, like any other tRNA synthetases. In this study we examine the paths of allosteric communication at the atomic level, through energy-weighted networks of Desulfitobacterium hafniense PyIRS (DhPyIRS) and its complexes with tRNA(Pyl) and activated pyrrolysine. We performed molecular dynamics simulations of the structures of these complexes to obtain an ensemble conformation-population perspective. Weighted graph parameters relevant to identifying key players and ties in the context of social networks such as edge/node betweenness, closeness index, and the concept of funneling are explored in identifying key residues and interactions leading to shortest paths of communication in the structure networks of DhPylRS. Further, the changes in the status of important residues and connections and the costs of communication due to ligand induced perturbations are evaluated. The optimal, suboptimal, and preexisting paths are also investigated. Many of these parameters have exhibited an enhanced asymmetry between the two subunits of the dimeric protein, especially in the pretransfer complex, leading us to conclude that encoding of function goes beyond the sequence/structure of proteins. The local and global perturbations mediated by appropriate ligands and their influence on the equilibrium ensemble of conformations also have a significant role to play in the functioning of proteins. Taking a comprehensive view of these observations, we propose that the origin of many functional aspects (allostery rand half-sites reactivity in the case of DhPyIRS) lies in subtle rearrangements of interactions and dynamics at a global level.

Localisation of discrete change in a transformer winding: a network-function-loci approach

An entirely different approach for localisation of winding deformation based on terminal measurements is presented. Within the context of this study, winding deformation means, a discrete and specific change externally imposed at a particular position on the winding. The proposed method is based on pre-computing and plotting the complex network-function loci e.g. driving-point impedance (DPI)] at a selected frequency, for a meaningful range of values for each element (increasing and decreasing) of the ladder network which represents the winding. This loci diagram is called the nomogram. After introducing a discrete change, amplitude and phase of DPI are measured. By plotting this single measurement on the nomogram, it is possible to estimate the location and identify the extent of change. In contrast to the existing approach, the proposed method is fast, non-iterative and yields reasonably good localisation. Experimental results for actual transformer windings (interleaved and continuous disc) are presented.

Transaction-based QoS management in a Hybrid Wireless Superstore Environment

Hybrid wireless networks are extensively used in the superstores, market places, malls, etc. and provide high QoS (Quality of Service) to the end-users has become a challenging task. In this paper, we propose a policy-based transaction-aware QoS management architecture in a hybrid wireless superstore environment. The proposed scheme operates at the transaction level, for the downlink QoS management. We derive a policy for the estimation of QoS parameters, like, delay, jitter, bandwidth, availability, packet loss for every transaction before scheduling on the downlink. We also propose a QoS monitor which monitors the specified QoS and automatically adjusts the QoS according to the requirement. The proposed scheme has been simulated in hybrid wireless superstore environment and tested for various superstore transactions. The results shows that the policy-based transaction QoS management is enhance the performance and utilize network resources efficiently at the peak time of the superstore business.

Interference Alignment as a Tool in Network Coding as Applied to Distributed Storage

In this paper, we outline an approach to the task of designing network codes in a non-multicast setting. Our approach makes use of the concept of interference alignment. As an example, we consider the distributed storage problem where the data is stored across the network in n nodes and where a data collector can recover the data by connecting to any k of the n nodes and where furthermore, upon failure of a node, a new node can replicate the data stored in the failed node while minimizing the repair bandwidth.