Enterprises have paid attention to consortium blockchains, in which multiple authorized organizations participate to form a consortium, in contrast to public blockchains, which consist of unspecified participants. Since a system using a blockchain is based on participants providing/using resources such as nodes to each other, a mechanism to motivate participants to provide resources is important. Typically, a public blockchain realizes an incentive mechanism for providing nodes by unspecified participants through mining rewards and transaction fees associated with a consensus algorithm such as PoW on the platform layer. On the other hand, a consortium blockchain usually does not have an incentive mechanism in the platform itself. In order to maintain and operate a consortium, it is essential that participating organizations provide resources or pay the costs of providing resources. However, because participating organizations use different percentages of the resources, a uniform fee could cause unfairness among the organizations. Therefore, it is necessary to manage billing to remove the gap between the costs borne and the actual usage or/and the unfairness among participants in the consortium. This research aims to realize a billing management method for consortium blockchain-based systems. We propose a smart contract-based service billing management mechanism named "BillingOpsSC" to ensure cross-organizational transparency for billing calculation and management. Furthermore, to ensure fairness among participants, we design a billing calculation model based on metering of the "contribution" by each organization such as providing resources, and the system "usage" by each organization, in the consortium. We implement a prototype of the proposed method including the billing management mechanism and calculation model for Hyperledger Fabric, which is one of the major consortium blockchains, and evaluate the method using the prototype. The results show that our proposed method is effective in ensuring transparency and fairness in billing management among multiple organizations.

Integrated monitoring system, enabled with semi-structured datastore, is a promising solution for monitoring SaaS systems. However, according to increasing scale of SaaS systems and their long-term of service operations, the monitoring system has faced the problem in response times of log analysis and storage consumption. Our empirical observation is that the problem is primarily derived from the unselective log processing of semi-structure datastore, whereas there should be heterogeneities in log data that we can take advantage of for efficient log management. Based on this observation, we first attest this insight by investigating the usage patterns of log data in a quantitative manner with an actual dataset of log access histories obtained from a SaaS system serving to enterprise users, and we show that there are heterogeneities in required retention period of logs, response time, and amount of data, depending on log data category and its analysis scenario. Armed with the evidence found from the investigation, we design a methodology of context-aware log management, key features of which are to speculatively pre-cache the log analysis and to proactively archive ones depending on log data category and analysis scenario. Evaluation with a prototype implementation shows that the proposed method reduces the response time and the storage consumption.

Software-defined Networking (SDN) is a new network architecture that decouples the control plane from the data plane. Scalability of the control plane with respect to network size and update frequency is an important problem that has been addressed by previous studies from a variety of viewpoints. However, the solutions found in these studies may be only locally optimized solutions. To find a globally optimized solution, a broader viewpoint is required: one in which various SDN architectures can be evaluated and compared. In this paper, we propose an abstract model of SDN architectures, which enables multiple SDN architectures to be compared under a unified evaluation condition, and discuss the modeling of SDN architecture and its variations to find the optimal design from a global viewpoint. We first propose a generic model of SDN architectures and derive variations in terms of composition unit (single or multiple), processing principle (sequential or parallel), or location (intra- or inter-node). We then show that existing SDN architectures can be represented as one of the variations of our abstract model with fitted parameters. Finally we discuss how variation of components affects performance and show, using message-driven simulations, that our model enables comprehensive performance comparisons of different SDN designs represented as parameterized models.

In this paper, we propose an power reduction method in a house which considers comfort. The proposed method assumes sensor networks which consist of smart taps, temperature and humidity sensors, light sensors, sleeping detection sensors, and human detection sensors. Furthermore, we made a modeling of the sensor networks and developed a simulation system to verify an effectiveness of the proposed method. From the evaluation by simulation, we verified effectiveness of the proposed method.

In today's data centers, many application services share the same physical/virtual devices and affect each other. Therefore, application service managers need to spend a lot of time monitoring the application services by investigating a wide range of historical data about the shared devices. In this paper, integrated monitoring software for a wide range of historical data, which shortens the transition time (i.e., the time to switch from one historical data to another) by collecting and processing the historical data, is proposed and evaluated. Five basic historical data formats for automatically creating both well-organized historical data and relation data for the historical data are also proposed. The formats help application service managers by eliminating the need for the additional software development, which was otherwise required for each application service. Surveys on application service managers in a SaaS provider for over 50 000 companies show that about 98.6% of monitoring tasks can be covered by the five basic data formats and that the integrated monitoring software reduces the transition times for switching between historical data by about 54.7% compared to those with conventional monitoring software. These results suggest that the proposed integrated monitoring software is effective not only for reducing the time required for monitoring application services, but also for enhancing the overall service availability of SaaS providers' systems.

To support understanding a specific GUI functionality in a program, we propose a visualization technique that presents the correspondence between the screens before and after the operation, as well as the traces of the source code executed by the operation. The presented traces include the executed lines and the method calls, which are fundamental information for understanding the functionality. They are represented as highlights that are superimposed on the entire source code.