Grid Service Framework: Supporting Multi-Models Parallel Grid Programming

Web service is a grid computing technology that promises greater ease-of-use and interoperability than previous distributed computing technologies. This paper proposed Group Service Framework, a grid computing platform based on Microsoft. NET that use web service to: (1) locate and harness volunteer computing resources for different applications, and (2) support multi-models such as Master/Slave, Divide and Conquer, Phase Parallel and so forth parallel programming paradigms in Grid environment, (3) allocate data and balance load dynamically and transparently for grid computing application. The Grid Service Framework based on Microsoft. NET was used to implement several simple parallel computing applications. The results show that the proposed Group Service Framework is suitable for generic parallel numerical computing.

A policy-based billing management architecture for the next generation IP-based network

IP billing is not only a basic functionality to operate IP sen＇ices, but also it is fundamental to offer customers with a stable and QoS-enabled network environment. As IP-based network has been widely agreed to be the core network of NGN, and existing IP billing system is too simple to fulfill the emerging requirements, the next generation IP billing has become an interesting topic in recent years. Policy-based management brings flexibility and scalability to systems by describing management logic and functions through policies, and thus reduces the complexity of the management of large-scale systems. Working on existing efforts, this paper proposed an improved IETF policy framework based upon which a policy-based billing management architecture for the next generation IP-based network was presented. Then a prototype with some basic functionalities was developed. The results of the experiment validated the expected improvements specified in this paper.

A ＂cluster＂ based search scheme in peer-to-peer network

This paper presents a "cluster" based search scheme in peer-to-peer network. The idea is based on the fact that data distribution in an information society has structured feature. We designed an algorithm to cluster peers that have similar interests. When receiving a query request, a peer will preferentially forward it to another peer which belongs to the same cluster and shares more similar interests. By this way search efficiency will be remarkably improved and at the same time good resilience against peer failure (the ability to withstand peer failure) is reserved.

Calculation of Minimal Dominating Set in Wireless Sensor Network with Host Switch-on/off

This paper proposes a simple and efficient distributed algorithm for calculating minimal dominating set in wireless sensor network. This method can avoid maintaining the connectivities between backbone hosts. Considering that the hosts in mobile networks have different characteristics, this paper proposes a method of calculating minimal dominating set with weight. The nodes can be chosen to form a minimal dominating set when the network topology changes. For the host switch on/off operation, the updating algorithm was provided. The change in the status of a hostaffects only the status of hosts in the restricted vicinity. Simulation results show that the proposed method can ensure fewer dominators but with higher weight to form the minimal dominating set and the nodes can be adaptive to the changes of network topology.

Research on Optimizing Computer Network Structure based on Genetic Algorithm and Modified Convex Optimization Theory

In this paper, we report in-depth analysis and research on the optimizing computer network structure based on genetic algorithm and modified convex optimization theory. Machine learning method has been widely used in the background and one of its core problems is to solve the optimization problem. Unlike traditional batch algorithm, stochastic gradient descent algorithm in each iteration calculation, the optimization of a single sample point only losses could greatly reduce the memory overhead. The experiment illustrates the feasibility of our proposed approach.

The Edge-pancyclicity of Generalized Hypercubes

Generalized hypercubes （denoted by Q（d1,d2,... ,dn）） is an important network topology for parallel processing computer systems. Some methods of forming big cycle from small cycles and links have been developed. Basing on which, we has proved that in generalized hypercubes, every edge can be contained on a cycle of every length from 3 to IV（G）I inclusive and all kinds of length cycles have been constructed. The edgepanciclieity and node-pancilicity of generalized hypercubes can be applied in the topology design of computer networks to improve the network performance.

Network and equation-based models in epidemiology

Network and equation-based （EB） models are two prominent methods used in the study of epidemics. While EB models use a global approach to model aggregate population, net- work models focus on the behavior of individuals in the population. The two approaches have been used in several areas of research, including finance, computer science, social science and epidemiology. In this study, epidemiology is used to contrast EB models with network models. The methods are based on the assumptions and properties of compartmental models. In EB models we solve a system of ordinary differential equations and in network models we simulate the spread of epidemics on contact networks using bond percolation. We examine the impact of network structures on the spread of infection by considering various networks, including Poisson, Erd3s R6nyi, Scale-free, and Watts- Strogatz small-world networks, and discuss how control measures can make use of the network structures. In addition, we simulate EB assumptions on Watts-Strogatz net- works to determine when the results are similar to that of EB models. As a case study, we use data from the 1918 Spanish flu pandemic and that from measles outbreak to validate our results.

Exploration of unpredictable environments by networked groups

Information sharing is a critical task for group-living animals. The pattern of sharing can be modeled as a network whose structure can affect the decision-making performance of individual members as well as that of the group as a whole. A fully connected network, in which each member can directly transfer information to all other members, ensures rapid sharing of important information, such as a promising foraging location. However, it can also impose costs by amplifying the spread of inaccur- ate information （if, for example the foraging location is actually not profitable）. Thus, an optimal net- work structure should balance effective sharing of current knowledge with opportunities to discover new information. We used a computer simulation to measure how well groups characterized by dif- ferent network structures （fully connected, small world, lattice, and random） find and exploit resource peaks in a variable environment. We found that a fully connected network outperformed other struc- tures when resource quality was predictable. When resource quality showed random variation, however, the small world network was better than the fully connected one at avoiding extremely poor outcomes. These results suggest that animal groups may benefit by adjusting their informa- tion-sharing network structures depending on the noisiness of their environment.