One of the features of a software-defined network (SDN) is a logically centralized control plane hosting one or more SDN controllers. As SDN controller placement can impact network performance, it is widely studied as the controller placement problem (CPP). For a cost-effective network design, network providers need to minimize the number of SDN controllers used in the network since each SDN controller incurs installation and maintenance costs. Moreover, the network providers need to deal with the failure of SDN controllers. Existing studies that consider SDN controller failures use the scheme of connecting each SDN switch to one Master controller and one or more Slave controllers. The problem with this scheme is that the computing capacity of each SDN controller cannot be used efficiently since one SDN controller handles the load of all SDN switches connected to it. The number of SDN controllers required can be reduced by distributing the load of each SDN switch among multiple SDN controllers. This paper proposes a controller placement model that allows the distribution against SDN controller failures. The proposed model determines the ratios of computing capacity demanded by each SDN switch on the SDN controllers connected to it. The proposed model also determines the number and placement of SDN controllers and the assignment of each SDN switch to SDN controllers. Controller placement is determined so that a network provider can continue to manage all SDN switches if no more than a certain number of SDN controller failures occur. We develop two load distribution methods: split and even-split. We formulate the proposed model with each method as integer linear programming problems. Numerical results show that the proposed model reduces the number of SDN controllers compared to a benchmark model; the maximum reduction ratio is 38.8% when the system latency requirement between an SDN switch and an SDN controller is 100[ms], the computing capacity of each SDN controller is 6 × 106[packets/s], and the maximum number of SDN controllers that can fail at the same time is one.

Simulating the mobility of mobile devices has always been an important issue as far as wireless networks are concerned because mobility needs to be taken into account in various situations in wireless networks. Researchers have been trying, for many years, to improve the accuracy and flexibility of mobility models. Although recent progress of designing mobility models based on social graph have enhanced the performance of mobility models and made them more convenient to use, we believe the accuracy and flexibility of mobility models could be further improved by taking a more integrated structure as the input. In this paper, we propose a new way of designing mobility models on the basis of relational graph [1] which is a graph depicting the relation among objects, e.g. relation between people and people, and also people and places. Moreover, some novel mobility features were introduced in the proposed model to provide social, spatial and temporal properties in order to produce results similar to real mobility data. It was demonstrated by simulation that these measures could generate results similar to real mobility data.

Predictive spatial-monitoring, which predicts spatial information such as road traffic, has attracted much attention in the context of smart cities. Machine learning enables predictive spatial-monitoring by using a large amount of aggregated sensor data. Since the capacity of mobile networks is strictly limited, serious transmission delays occur when loads of communication traffic are heavy. If some of the data used for predictive spatial-monitoring do not arrive on time, prediction accuracy degrades because the prediction has to be done using only the received data, which implies that data for prediction are ‘delay-sensitive’. A utility-based allocation technique has suggested modeling of temporal characteristics of such delay-sensitive data for prioritized transmission. However, no study has addressed temporal model for prioritized transmission in predictive spatial-monitoring. Therefore, this paper proposes a scheme that enables the creation of a temporal model for predictive spatial-monitoring. The scheme is roughly composed of two steps: the first involves creating training data from original time-series data and a machine learning model that can use the data, while the second step involves modeling a temporal model using feature selection in the learning model. Feature selection enables the estimation of the importance of data in terms of how much the data contribute to prediction accuracy from the machine learning model. This paper considers road-traffic prediction as a scenario and shows that the temporal models created with the proposed scheme can handle real spatial datasets. A numerical study demonstrated how our temporal model works effectively in prioritized transmission for predictive spatial-monitoring in terms of prediction accuracy.

Our prior papers proposed a traffic engineering scheme to further localize traffic in peer-assisted content delivery networks (CDNs). This scheme periodically combines the content files and allows them to obtain the combined content files while keeping the price unchanged from the single-content price in order to induce altruistic clients to download content files that are most likely to contribute to localizing network traffic. However, the selection algorithm in our prior work determined which and when content files should be combined according to the cache states of all clients, which is a kind of unrealistic assumption in terms of computational complexity. This paper proposes a new concept of virtual local server to reduce the computational complexity. We could say that the source server in our mechanism has a virtual caching network inside that reflects the cache states of all clients in the ‘actual’ caching network and combines content files based on the virtual caching network. In this paper, without determining virtual caching network according to the cache states of all clients, we approximately estimated the virtual caching network from the cache states of the virtual local server of the local domain, which is the aggregated cache state of only altruistic clients in a local domain. Furthermore, we proposed a content selection algorithm based on a virtual caching network. In this paper, we used news life-cycle model as a content model that had the severe changes in cache states, which was a striking instance of dynamic content models. Computer simulations confirmed that our proposed algorithm successfully localized network traffic.

The mobile cloud computing (MCC) paradigm is aimed at integrating mobile devices with cloud computing. In the client-server architecture of MCC, mobile devices offload tasks to the cloud to utilize the computation and storage resources of data centers. However, due to the rapid increase in the traffic demand and complexity of mobile applications, service providers have to continuously upgrade their infrastructures at great expense. At the same time, modern mobile devices have greater resources (communication, computation, and sensing), and these resources are not always fully utilized by device users. Therefore, mobile devices, from time to time, encounter other devices that could provide resources to them. Because the amount of such resources has increased with the number of mobile devices, researchers have begun to consider making use of these resources, located at the “edge” of mobile networks, to increase the scalability of future information networks. This has led to a cooperation based architecture of MCC. This paper reports the concept and design of an resource sharing mechanism that utilize resources in mobile devices through opportunistic contacts between them. Theoretical models and formal definitions of problems are presented. The efficiency of the proposed mechanism is validated through formal proofs and extensive simulation.

Streaming systems that can maintain Quality of Experience (QoE) for users have attracted much attention because they can be applied in various fields, such as emergency response training and medical surgery. Dynamic Adaptive Streaming over HTTP (DASH) is a typical protocol for streaming system. In order to improve QoE in DASH, a multi-server system has been presented by pseudo-increasing bandwidth through multiple servers. This multi-server system is designed to share streaming content efficiently in addition to having redundant server resources for each streaming content, which is excellent for fault tolerance. Assigning DASH server to users in these multi-servers environment is important to maintain QoE, thus a method of server assignment of users (user allocation method) for multi-servers is presented by using cooperative game theory. However, this conventional user allocation method does not take into account the size of the server bandwidth, thus users are concentrated on a particular server at the start of playback. Although the average required bit rate of video usually fluctuates, bit rate fluctuations are not taken into account. These phenomena may decrease QoE. In this paper, we propose a novel user allocation method using coalition structure generation in cooperative game theory to improve the QoE of all users in an immediate and stable manner in DASH environment. Our proposed method can avoid user concentration, since the bandwidth used by the overall system is taken into account. Moreover, our proposed method can be performed every time the average required bit rate changes. We demonstrate the effectiveness of our method through simulations using Network Simulator 3 (NS3).