Valid session duration of traditional web applications is usually set short to decrease risks such as session hijacking and to avoid waste of the computational resources. When a client exceeds the valid duration of the idle time, the session of the client expires. After the expiration, the client must send requests and data all over again from the beginning in order to resume the session. Such resubmission irritates the users of web applications and decreases their ease of use. On the mobile networks, we encounter such situation frequently. In this paper, we propose a new session management method that resolves this problem by managing the session information and the data of web applications separately. We investigated the performance of the proposed method applied to a web application for business use. We will show the result that the proposed method realizes better usability while satisfying performance for the business web applications.
Organizations related to infrastructure, such as utilities and railway companies, manage a large number of facilities, the failure of which can have a huge impact on society. The cost of maintaining these facilities is a combination of regular maintenance costs and urgent recovery costs. Generally, the urgent costs are much higher than regular costs. Regular maintenance work should result in fewer sudden failures, and thus reduce these urgent costs. However, if the regular maintenance is too frequent, its cost becomes too high. Therefore, it is important to balance the regular and urgent costs to minimize the overall maintenance cost. We propose a maintenance schedule optimization method based on the failure probability distribution of the facilities. The total cost is mathematically modeled, with the regular maintenance schedule included via decision variables and the occurrence of failures modeled as stochastic variables. The stochastic total maintenance costs are evaluated using a Monte Carlo method, and a genetic algorithm is employed to optimize the maintenance schedule. The proposed method is evaluated using data provided by a Japanese railway company, and our results confirm that the method produces an excellent maintenance schedule. A statistical test shows there is a significant difference between the proposed and conventional methods.
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