In this paper, we study the optimal allocation of multimedia files in distributed network systems. In these systems, the files are shared by users connected with different servers geographically separated, and each file must be stored in at least one of servers. Users can access any files stored in any servers connected with high-speed communication networks. Copies of the files accessed frequently are to be stored in several servers that have databases. So, it is one of the most important problems how to assign the files to servers in view of costs and delays. Considering these problems in heterogeneous network environments, we present a new system model that covers wide range of multimedia network applications like VOD, CALS, and so on. In these systems, it is obvious that there is trading-off relationship between costs and delays. Our objective is to find the optimal file allocation such that the total cost is minimized subject to the total delay. We introduce a 0-1 integer programming formulation for the optimization problem, and find the optimal file allocation by solving these formulae.

This paper introduces an adaptive distributed routing algorithm for the faulty star graph. The algorithm is based on that the n-star graph has uniform node degree n-1 and is n-1-connected. By giving two routing rules based on the properties of nodes, an optimal routing function for the fault-free star graph is presented. For a given destination in the n-star graph, n-1 node-disjoint and edge-disjoint subgraphs, which are derived from n-1 adjacent edges of the destination, can be constructed by this routing function and the concept of Breadth First Search. When faults are encountered, according to that there are n-1 node-disjoint paths between two arbitrary nodes, the algorithm can route messages to the destination by finding a fault-free subgraphs based on the local failure information (the status of all its incident edges). As long as the number f of faults (node faults and/or edge faults) is less than the degree n-1 of the n-star graph, the algorithm can adaptively find a path of length at most d+4f to route messages successfully from a source to a destination, where d is the distance between source and destination.

In a previous paper, we proposed a rescue support system for victims buried in an earthquake disaster by constructing an ad-hoc network using home-server based smart homes. However, this system has the following two problems: i) it cannot ensure sufficient density of home servers to realize adequate WLAN coverage, ii) the system does not consider areas in which home servers cannot be used such as parks and factories, for example. In this paper, we propose a new method using a delay tolerant network (DTN) technique. In this method, rescuers (such as rescue teams) with mobile devices relay information between disconnected networks by walking around during rescue activities. For a performance evaluation, we performed simulation experiments using a map of Abeno-ku, Osaka. From our results, we show that the proposed method increases the information acquisition rate, and that the network can be maintained. We also quantitatively show the penetration rate of smart homes needed for our system. In addition, we show that the rescue request system is more effective than other systems, and the method with the mobile device relay is better than without this method.

Roundness is one of the most important geometric measures for circular objects in the process of mechanical assembly. It is the amount of variation in a circular size which can be permitted. To compute roundness, the authors have already proposed an exact polynomial-time algorithm whose time complexity is O(n2). In this paper, we show that this roundness algorithm can be improved more efficiently, by introducing the deletion of the unnecessary points, in practical applications. In addition, the computational experience of this revised algorithm is also presented.

Mirroring of network servers has been considered to be effective for load balancing. However, the cost of setting up new mirror servers is enormously high. In this paper, we propose a dynamic file allocation model with a simple mirroring function for handling significant changes of network traffic in the Internet. According to the load fluctuation, we can dynamically reallocate files using this model. We show that our model accomplishes satisfactory performance and reduces cost by adding a simple mirroring function to all existent servers instead of setting up mirror servers afresh.

In distributed network systems, it is one of the most important problems how to assign the files to servers in view of cost and delay. It is obvious that there is a trading-off relationship between costs and delays in these systems. In order to evaluate the optimization that the total cost is minimized subject to the total delay, we have presented the Optimal File Allocation Model as 0-1 integer programming, and have investigated the general characteristics in distributed systems. In this model, we have introduced many cost and delay parameters to evaluate the total cost and delay in the system more exactly. In constructing practical systems, it is necessary to investigate the weight and the contribution of each parameter to the total cost. It is very useful to show how to estimate cost and delay parameters on the basis of this analysis. In this paper, we analyze the sensitivity of these parameters and make clear the influence between principal parameters.

In this paper, we consider optimal mirror allocation problems for the purpose of load balancing in network servers. We focus on constructing high-reliability networks and propose the optimal mirror allocation model such that the system reliability is maximized subject to costs and delays, in view of the trade-off between the reliability and cost. This optimization model is capable of dealing with various kinds of network topologies, although for simplicity, we assume the read-only situation. We formulate this optimization problem into a 0-1 integer programming model, and we use an approximate method for numerical analysis in order to analyze more large-scale systems. Our objective is to find the optimal mirror allocation by solving this model, and to show quantitatively the general characteristics of the load balancing and the improvement of the system reliability by the distributed mirror allocation.

Content Distribution Networks (CDNs) are highly advanced architectures for networks on the Internet, providing low latency, scalability, fault tolerance, and load balancing. One of the most important issues to realize these advantages of CDNs is dynamic content allocation to deal with temporal load fluctuation, which provides mirroring of content files in order to distribute user accesses. Since user accesses for content files change over time, the content files need to be reallocated appropriately. In this paper, we propose a cost-effective content migration method called the Step-by-Step (SxS) Migration Algorithm for CDNs, which can dynamically relocate content files while reducing transmission cost. We show that our method maintains sufficient performance while reducing cost in comparison to the conventional shortest-path migration method. Furthermore, we present six life cycle models of content to consider realistic traffic patterns in our simulation experiments. Finally, we evaluate the effectiveness of our SxS Migration Algorithm for dynamic content reconfiguration across time.

Metaheuristic methods have been studied for combinational optimization problems for some time. Recently, a Consultant-Guided Search (CGS) has been proposed as a metaheuristic method for the Traveling Salesperson Problem (TSP). This approach is an algorithm in which a virtual person called a client creates a solution based on consultation with a virtual person called a consultant. In this research, we propose a parallel algorithm which uses the Ant Colony System (ACS) to create a solution with a consultant in a Consultant-Guided Search, and calculate an approximation solution for the TSP. Finally, we execute a computer experiment using the benchmark problems (TSPLIB). Our algorithm provides a solution with less than 2% error rate for problem instances using less than 2000 cities.