Recently, the use of big data has attracted attention as a profitable business strategy, and is expected to keep increasing in the future. In contrast to existing ways of big data analysis based on centralized computing environment such on a few huge data centers, we advocate a distributed and parallel computation environment aiming at fine-grained cloud computing, which includes so-called edge computing. In the advocated environment, it is assumed that many users, which own big data to be analyzed, dynamically participate in the network to request computer resources and leave after finishing their analyses. In such a dynamic and realistic environment, this paper improves the proximity of computer resources to big data by applying virtualized network in which nodes with each big data and the corresponding computer resources are mutually connected by proper optical paths. Optical path arrangement is periodically updated for new users, without affecting other users currently using computer resources. Moreover, a resource assignment algorithm suitable for such dynamic changes is also proposed to achieve fairness in terms of the network distance between big data and computer resources, and effective load balancing among resource suppliers. We evaluate its effectiveness by computer simulation.

In recent years, the use of big data as a profitable business strategy has attracted considerable attention, and is expected to drastically increase in the future. We advocate a distributed and parallel computation environment for big data analysis aiming at fine-grained cloud computing, which includes so-called edge computing. This approach differs from the way that data analysis was performed in the past, namely based on a centralized computing model involving a few huge data centers. In contrast, the realization of a distributed computational environment in which computer resources with relatively low capacity are utilized requires optimal matching between the many owners of big data and the suppliers of computer resources. In this paper, we apply network virtualization with optical paths to improve the proximity of the computer resources to the big data, and present a resource allocation method to achieve fairness in terms of the network distance between the big data and computer resources, and effective load balancing among suppliers. The effectiveness of our approach is evaluated by extensive computer simulation.

Multihoming is widely used by Internet service providers (ISPs) to obtain improved performance and reliability when connecting to the Internet. Recently, the use of overlay routing for network application traffic is rapidly increasing. As a source of both routing oscillation and cost increases, overlay routing is known to bring challenges to ISPs. In this paper, we study the interaction between overlay routing and a multihomed ISP's routing strategy with a Nash game model, and propose a routing strategy for the multihomed ISP to alleviate the negative impact of overlay traffic.We prove that with the proposed routing strategy, the network routing game can always converge to a stable state, and the ISP can reduce costs to a relatively low level. From numerical simulations, we show the efficiency and convergence resulting from the proposed routing strategy. We also discuss the conditions under which the multihomed ISP can realize minimum cost by the proposed strategy.

With the wide spread of mobile devices, information discovery methods over mobile ad hoc networks (MANETs) are required. Recent studies have revealed that integrating a distributed hash table (DHT) substrate and network-layer routing can increase search efficiency. Furthermore, some researchers have proposed a clustering method which groups the overlay nodes according to their physical distance. However, it has also been pointed out that the search efficiency deteriorates in highly dynamic environments. In this paper, we propose a method sharing pointers among nodes in a cluster. Through simulation experiments, we show that the proposed method improves the success ratio of object search up to 40 % compared with existing method.

There has been much research on user activity assistance applications using the location of users and objects as context. However people's activities are described in terms of time sequence aspect in addition to location aspect. Therefore, it is important for enhanced user activity support systems to consider the user's context in terms of spatio-temporal constraints. In this paper, we propose a user activity assistance system that employs a state sequence description scheme to describe the user's contexts. In this scheme, each state is described as a spatio-temporal relationship between the user and objects in the real world. Typical sets of states are stored as models of tasks performed by a user. To try out this system, we have developed an experimental house containing various embedded sensors and RFID-tagged objects. Each state is detected by a decision tree constructed by a C4.5 algorithm using the output of the sensors and the RFID tags. The user's context is obtained by matching the detected state series to a task model. Having evaluated the performance of the proposed system in this experimental house, we conclude that our system is an effective way of acquiring the user's spatio-temporal context.

We address the interaction between TCP and RLC (Radio Link Control), the link layer retransmission protocol in W-CDMA, a 3rd generation cellular wireless network technology, in evaluating TCP performance in following points; l) Supress delay-jitter in link layer to avoid excess retransmissions. 2) Clarify the trade-off between jitter-suppression and link utilization to improve TCP throughput. 3) Optimize the link layer and TCP parameters. We show how to find the TCP and link ARQ parameters that yield optimum overall performance; simulations and emulation confirm their effectiveness. The coexistence of ARQ and TCP can lead to inefficient interaction; the delay-jitter on the link layer may trigger spurious TCP retransmission. The solution is to suppress jitter on the link layer. To manage the trade-off between this suppression and link utilization, we optimized with link parameter.