Design and analysis of a distributed grid resource discovery protocol

Computational grids have been emerging as a new paradigm for solving large complex problems over the recent years. The problem space and data set are divided into smaller pieces that are processed in parallel over the grid network and reassembled upon completion. Typically, resources are logged into a resource broker that is somewhat aware of all of the participants available on the grid. The resource broker scheme can be a bottleneck because of the amount of computational power and network bandwidth needed to maintain a fresh view of the grid. In this paper, we propose to place the load of managing the network resource discovery on to the network itself: inside of the routers. In the proposed protocol, the routers contain tables for resources similar to routing tables. These resource tables map IP addresses to the available computing resource values, which are provided through a scoring mechanism. Each resource provider is scored based on the attributes they provide such as the number of processors, processor frequency, amount of memory, hard drive space, and the network bandwidth. The resources are discovered on the grid by the protocol’s discovery packets, which are encapsulated within the TCP/IP packets. The discovery packet visits the routers and look up in the resource tables until a satisfactory resource is found. The protocol is validated by simulations with five different deployment environments.     

A decentralized fault tolerance model based on level of performance for grid environment

Computational grids have the potential for solving large-scale scientific problems using heterogeneous and geographically distributed resources. At this scale, computer resources and network failures are no more exceptions, but belong to the normal system behavior. Therefore, one of the most valuable characteristics of grid tools, apart from the performance they can achieve, is fault tolerance, which is a significant and complex issue in grid computing systems. In this paper, we propose a fault tolerant model for grid computing systems namely DCFT. This model is based on dynamic colored graphs without replication of computer resources. The proposed faut tolerance model consists of two stages. In the first stage, each node is described by a state vector. We color each attribute of the state vector as three colors (green, blue and red) based on its level of performance. In the second stage, we classify the nodes of a grid into three categories: the identical computer resources in term of performance, the more efficient ones and the less efficient ones. We used the colors of the nodes to develop a new strategy for fault tolerance based on the level of performance. A simulation of the proposed model using SimGrid simulator and Graphstream is conducted. Experimental results show that the proposed model performs very well in a large grid environment.     

Fast Fragmentation of Networks Using Module-Based Attacks

In the multidisciplinary field of Network Science, optimization of procedures for efficiently breaking complex networks is attracting much attention from a practical point of view. In this contribution, we present a module-based method to efficiently fragment complex networks. The procedure firstly identifies topological communities through which the network can be represented using a well established heuristic algorithm of community finding. Then only the nodes that participate of inter-community links are removed in descending order of their betweenness centrality. We illustrate the method by applying it to a variety of examples in the social, infrastructure, and biological fields. It is shown that the module-based approach always outperforms targeted attacks to vertices based on node degree or betweenness centrality rankings, with gains in efficiency strongly related to the modularity of the network. Remarkably, in the US power grid case, by deleting 3% of the nodes, the proposed method breaks the original network in fragments which are twenty times smaller in size than the fragments left by betweenness-based attack.     

Spontaneous deadlock breaking on amoeba-based neurocomputer

Any artificial concurrent computing system involves a potential risk of “deadlock” that its multiple processes sharing common computational resources are stuck in starved conditions, if simultaneous accesses of the processes to the resources were unconditionally permitted. To avoid the deadlock, it is necessary to set up some form of central control protocol capable of appropriately regulating the resource allocation. On the other hand, many decentralized biological systems also perform concurrent computing based on interactions of components sharing limited amounts of available resources. Despite the absence of a central control unit, they appear to be free from the deadlock implying their death, as long as they are alive. Should we consider that biological computing paradigms are essentially different from artificial ones? Here we employ a photosensitive amoeboid cell known as a model organism for studying cellular information processing and construct an experimental system to explore how the amoeba copes with deadlock-like situations induced by optical feedback control. The feedback control is implemented by a recurrent neural network algorithm for leading the amoeba to solve a particular constraint satisfaction problem. We show that the amoeba is capable of breaking through the deadlock-like situations because its oscillating cellular membrane spontaneously produces a wide variety of spatiotemporal patterns. The result implies that our system can be developed to a neurocomputer that works as logical circuit, associative memory device, combinatorial optimization problem solver, and chaotic computer capable of spontaneous transition among multiple solutions.     

Role of different lymphoid tissues in the initiation and maintenance of DNA-raised antibody responses to the influenza virus H1 glycoprotein.

Antibody responses in mice immunized by a single gene gun inoculation of plasmid expressing the influenza virus H1 hemagglutinin and in mice immunized by a sublethal H1 influenza virus infection have been compared. Both immunizations raised long-lived serum responses that were associated with the localization of antibody-secreting cells (ASC) to the bone marrow. However, the kinetics of these responses were 4 to 8 weeks slower in the DNA-immunized than in the infection-primed mice. Following a gene gun booster, the presence of ASC in the inguinal lymph nodes, but not in other lymph nodes, revealed gene gun responses being initiated in the nodes that drain the skin target site. Both pre- and postchallenge, the DNA-immunized mice had 5- to 10-times-lower levels of antibody and ASC than the infection-primed mice.     

IP traceback with sparsely-tagged fragment marking scheme under massively multiple attack paths

IP traceback is known to be one of the most effective measures to deter Internet attacks. Various techniques for IP traceback have been suggested. Among them, we focus on Probabilistic Packet Marking scheme (PPM) with tagging. We believe PPM is more advantageous than others because it does not generate additional network traffic and requires minimal protocol change. However, three parameters need to be optimized to make PPM practical under massively multiple attack paths: the number of packets to collect, the number of fragment combinations to recover the IP addresses, and the false positive error rate. Tagging is an effective way to reduce the number of combinations but it increases the false positive error rates when the number of routers in the attack paths grows. Other PPM-related techniques suggested in the past have similar problems. They improve one or two parameters at the expense of others, or they require additional data structures such as an upstream router map. In this paper, we propose a method that optimizes the three parameters at the same time and recovers original IPs quickly and correctly even in the presence of massive multiple attack paths. Our method does not need either a combinatorial process to recover IPs or additional information such as an upstream router map. Our result shows that our method recovers 95% of the original IPs correctly with no fragment combinations and with zero false positives. It needs to collect only 8N packets per router where N is the number of routers involved in the attack paths.     

Automatic reconstruction of molecular and genetic networks from discrete time series data

We apply a mathematical algorithm which processes discrete time series data to generate a complete list of Petri net structures containing the minimal number of nodes required to reproduce the data set. The completeness of the list as guaranteed by a mathematical proof allows to define a minimal set of experiments required to discriminate between alternative network structures. This in principle allows to prove all possible minimal network structures by disproving all alternative candidate structures. The dynamic behaviour of the networks in terms of a switching rule for the transitions of the Petri net is part of the result. In addition to network reconstruction, the algorithm can be used to determine how many yet undetected components at least must be involved in a certain process. The algorithm also reveals all alternative structural modifications of a network that are required to generate a predefined behaviour.     

Dynamic Source Routing Strategy for Two-Level Flows on Scale-Free Networks

Packets transmitting in real communication networks such as the Internet can be classified as time-sensitive or time-insensitive. To better support the real-time and time-insensitive applications, we propose a two-level flow traffic model in which packets are labeled as level-1 or level-2, and those with level-1 have higher priority to be transmitted. In order to enhance the traffic capacity of the two-level flow traffic model, we expand the global dynamic routing strategy and propose a new dynamic source routing which supports no routing-flaps, high traffic capacity, and diverse traffic flows. As shown in this paper, the proposed dynamic source routing can significantly enhance the traffic capacity and quality of time-sensitive applications compared with the global shortest path routing strategy.     

Molecular dynamics in a grid computing environment: experiences using DL_POLY_3 within the eMinerals escience project

We use the example of a study of the compressibility anomaly in amorphous silica to illustrate how molecular-scale simulations can be performed using grid computing. The potential for running many simulations within a single study requires the use of new data management methods, which are discussed in this paper. The example of silica highlights the advantages of the use of grid computing for studying subtle effects.     

Comparative evaluation of CD8+CTL responses following gene gun immunization targeting the skin with intracutaneous injection of antigen-transduced dendritic cells

The skin is an attractive target for antigen-specific vaccination. Particle bombardment of the epidermis with plasmid DNA using the gene gun results in antigen expression in keratinocytes of the epidermis leading to antigen presentation in the draining lymph nodes by migratory dendritic cells (DC). In order to better understand the role of the skin in stimulating antigen-specific CD8+cytotoxic T cells (CTL), we compared gene gun immunization with intracutaneous injections of antigen-transduced DC. A single intracutaneous injection of antigen-transduced DC was able to induce in vivo expansion of CD8+CTL specific for the model antigen chicken ovalbumin while four simultaneous shots with the gene gun were not effective. Antigen-transduced DC were much more efficient than particle bombardment of the epidermis in stimulating adoptively transferred TCR-transgenic CD8+CTL in the draining lymph nodes. Employing the novel technique of in vivo bioluminescence imaging, we demonstrated efficient gene transfer to the skin following gene gun bombardment and confirmed that a similar amount of antigen reached the lymph node when compared with injection of antigen-transduced DC. Our results suggest that direct transfection of the skin does not optimally reach and activate appropriate antigen-presenting DC. We believe that this reflects the immunological function of the epidermis which must balance immunity and tolerance to foreign antigens. Further investigations will have to address the role of Langerhans cells for the activation of cellular immunity in the skin.     

Erythrophagocytosis in the caprine hemal node

Caprine hemal nodes were studied by transmission electron microscopy after glutaraldehyde fixation and epoxy resin embedding. Hemal node macrophages were observed to be engaged in erythrophagocytosis. In the early stages of endocytosis, intact erythrocytes were contained in some of the heterophagic vacuoles of macrophages. Later, granular, electron-dense material appeared on erythrocytes, presumably as a result of lysosomal degradation of their matrices. Subsequently, the matrix fragmented and probably formed 'myelin-like figures' and residual bodies that dominated the macrophage cytoplasm. In addition, images of sinusoidal endothelium, reticular cells, lymphocytes and, rarely, eosinophils were observed that depicted structures resembling various stages of lysosomal digestion of erythrocyte matrix noted in macrophages. Our study provides evidence to support the fact that effete erythrocytes are filtered, besides known organs, also in caprine hemal nodes. The morphology and location of hemal nodes suggest that the organ can be an efficient blood filter.     

Ultrastructural morphogenesis of dimorphic arcuate infection (gun) cells of Haptoglossa erumpens an obligate parasite of Bunonema nematodes

Haptoglossa is a genus of biflagellate organisms that has been placed in the oomycetes and is characterised by producing unique infective gun cells, which usually infect by physically rupturing the nematode cuticle. Haptoglossa erumpens is a parasite of Bunonema nematodes that produces arcuate infection cells and aplanospores that are discharged following the swelling and rupture of the thallus wall and distended host cuticle. Recent isolations of H. erumpens have revealed that the germinating aplanospores develop into two similar-sized but morphologically distinct infection cells. The uni-nucleate, convexly arcuate, gun cells were observed to fire in response to host nematodes, producing a cylindrical sporidium inside the host body. These gun cells had an apical missile chamber containing a needle with a unique arrangement of investing cones. Unlike previously described gun cells, the tube tail did not wind around the nucleus but continued into the basal vacuole where it terminated. The second type of infection cell was a concavely arcuate, bi-nucleate, cell that had an unusually large and elongate annulus component in the missile chamber. These modified bi-nucleate gun cells were never observed to fire in response to contact with Bunomena nematodes. The patterns of morphological and structural variations in these infection structures in this genus are reviewed in the light of these findings.     

An artificial hysteresis binary neuron: a model suppressing the oscillatory behaviors of neural dynamics

A hysteresis binary McCulloch-Pitts neuron model is proposed in order to suppress the complicated oscillatory behaviors of neural dynamics. The artificial hysteresis binary neural network is used for scheduling time-multiplex crossbar switches in order to demonstrate the effects of hysteresis. Time-multiplex crossbar switching systems must control traffic on demand such that packet blocking probability and packet waiting time are minimized. The system using n×n processing elements solves an n×n crossbar-control problem with O(1) time, while the best existing parallel algorithm requires O(n) time. The hysteresis binary neural network maximizes the throughput of packets through a crossbar switch. The solution quality of our system does not degrade with the problem size.     

Computing tertiary structures of proteins

Using only data on sequence, a method of computing a low-resolution tertiary structure of a protein is described. The steps are: (a) Estimate the distances of individual residues from the centroid of the molecule, using data on hydrophobicity and additional geometrical constraints. (b) Using these distances, construct a two-valued matrix whose elements, the distances between residues, are greater or less thanR, the radius of the molecule. (c) Optimize to obtain a three-dimensional structure. This procedure requires modest computing facilities and is applicable to proteins with 164 residues and presumably more. It produces structures withr (correlation between inter-residue distances in the computed and native structures) between 0.5 and 0.7. Furthermore, correct inference of two or three long-range contacts suffices to yield structures withr values of 0.8–0.9. Because segments forming parallel or antiparallel folding structures intersect the radius vector at similar angles, from centroidal point distances it is possible to infer some of these long-range contacts by an elaboration of the procedure used to construct the input matrix. A criterion is also described which can be used to determine the quality of a proposed input matrix even when the native structure is not known.     

BinTree seeking: a novel approach to mine both bi-sparse and cohesive modules in protein interaction networks

Modern science of networks has brought significant advances to our understanding of complex systems biology. As a representative model of systems biology, Protein Interaction Networks (PINs) are characterized by a remarkable modular structures, reflecting functional associations between their components. Many methods were proposed to capture cohesive modules so that there is a higher density of edges within modules than those across them. Recent studies reveal that cohesively interacting modules of proteins is not a universal organizing principle in PINs, which has opened up new avenues for revisiting functional modules in PINs. In this paper, functional clusters in PINs are found to be able to form unorthodox structures defined as bi-sparse module. In contrast to the traditional cohesive module, the nodes in the bi-sparse module are sparsely connected internally and densely connected with other bi-sparse or cohesive modules. We present a novel protocol called the BinTree Seeking (BTS) for mining both bi-sparse and cohesive modules in PINs based on Edge Density of Module (EDM) and matrix theory. BTS detects modules by depicting links and nodes rather than nodes alone and its derivation procedure is totally performed on adjacency matrix of networks. The number of modules in a PIN can be automatically determined in the proposed BTS approach. BTS is tested on three real PINs and the results demonstrate that functional modules in PINs are not dominantly cohesive but can be sparse. BTS software and the supporting information are available at: www.csbio.sjtu.edu.cn/bioinf/BTS/.     

An empirical study of web browsers’ resistance to traffic analysis and website fingerprinting attacks

Anonymity protocols are employed to establish encrypted tunnels to protect the privacy of Internet users from traffic analysis attacks. However, the attackers strive to infer some traffic patterns’ characteristics (e.g. packet directions, packet sizes, inter-packet timing, etc.) in order to expose the identities of Internet users and their activities. A recent and popular traffic analysis attack is called website fingerprinting which reveals the identity of websites visited by target users. Existing work in the literature studied the website fingerprinting attack using a single web browser, namely Firefox. In this paper we propose a unified traffic analysis attack model composed of a sequence of phases that demonstrate the efficiency of website fingerprinting attack using popular web browsers under Tor (The Onion Router). In addition, we reveal the main factors that affect the accuracy of website fingerprinting attack over Tor anonymous system and using different browsers. To the best of our knowledge, no previous study uncovered such factors by deploying real-world traffic analysis attack utilizing the top five web browsers. The outcomes of the research are very relevant to Internet users (individuals/companies/governments) since they allow to assess to which extent their privacy is preserved in presence of traffic analysis attacks, in particular, website fingerprinting over different browsers. A recommendation for future research direction regarding the investigation of website fingerprinting over different scenarios is also provided.     

Distributed Clone Detection in Static Wireless Sensor Networks: Random Walk with Network Division

Wireless Sensor Networks (WSNs) are vulnerable to clone attacks or node replication attacks as they are deployed in hostile and unattended environments where they are deprived of physical protection, lacking physical tamper-resistance of sensor nodes. As a result, an adversary can easily capture and compromise sensor nodes and after replicating them, he inserts arbitrary number of clones/replicas into the network. If these clones are not efficiently detected, an adversary can be further capable to mount a wide variety of internal attacks which can emasculate the various protocols and sensor applications. Several solutions have been proposed in the literature to address the crucial problem of clone detection, which are not satisfactory as they suffer from some serious drawbacks. In this paper we propose a novel distributed solution called Random Walk with Network Division (RWND) for the detection of node replication attack in static WSNs which is based on claimer-reporter-witness framework and combines a simple random walk with network division. RWND detects clone(s) by following a claimer-reporter-witness framework and a random walk is employed within each area for the selection of witness nodes. Splitting the network into levels and areas makes clone detection more efficient and the high security of witness nodes is ensured with moderate communication and memory overheads. Our simulation results show that RWND outperforms the existing witness node based strategies with moderate communication and memory overheads.     

Performance Evaluation of Deflection Routing in Optical IP Packet-Switched Networks

In previous papers [5,6], an optical switch architecture was proposed to handle variable-length packets such as IP datagrams, based on an AWG device to route packets and equipped with a fiber delay-line stage as optical input buffer. Unfortunately, extensive simulations of optical networks built with switches of this type showed that considerable buffering capability would be required in order to achieve acceptable performance. In this work, therefore, we studied the effectiveness of packet deflection as a mean for solving packet contentions on outputs of optical switches. Optical transport networks were simulated, evaluating the performance of packet deflection routing, based on a traffic model adherent to real IP traffic measurements. Full-mesh and wheel network topologies have been considered, comparing results to assess deflection effectiveness. Our simulation results show that deflection routing leads to satisfying performance even using buffers with limited size. Furthermore, the average delivery delay does not suffer heavy penalty from packet deflection, even under heavy traffic conditions.     

Multi-Scale Analysis of the European Airspace Using Network Community Detection

We show that the European airspace can be represented as a multi-scale traffic network whose nodes are airports, sectors, or navigation points and links are defined and weighted according to the traffic of flights between the nodes. By using a unique database of the air traffic in the European airspace, we investigate the architecture of these networks with a special emphasis on their community structure. We propose that unsupervised network community detection algorithms can be used to monitor the current use of the airspace and improve it by guiding the design of new ones. Specifically, we compare the performance of several community detection algorithms, both with fixed and variable resolution, and also by using a null model which takes into account the spatial distance between nodes, and we discuss their ability to find communities that could be used to define new control units of the airspace.     

Microscopic-macroscopic interface in biological information processing

Recent experimental work suggests that chemical messengers associated with the neuron membrane serve as a link between macroscopic and microscopic information processes in the brain. Arguments based on the physical limits of computing, on computational parallelism, and on evolution theory suggest that microphysical computing processes enormously enhance the brain's computing power. A number of models are briefly sketched which illustrate how molecular switching processes could be recruited for useful biological functions. The flow of information between microscopic and macroscopic forms is suggestive of processes which occur in a measuring apparatus, and the implications of this analogy are considered.