Increasing use of software, rapid and unavoidable changes in the operational environment bring many problems for software engineers. One of these problems is the aging and degradation of software performance. Software rejuvenation is a proactive and preventive approach to counteract software aging. Generally, when software is initiated, amounts of memory are allocated. Then, the body of software is executed for providing a service and when the software is terminated, the allocated memory is released. In this paper, a rejuvenation model based on multilevel software rejuvenation and Markov chain presented. In this model, the system performance as a result of degraded physical memory and memory usage is divided into four equal levels by services. Hence, we offer four types of policies for software rejuvenation. In addition, the system availability is determined, and a cost function for the model is introduced. The cost function includes the time of performing rejuvenation, the number of system services at any time, and the number of rejuvenation actions. To validate the proposed model, a case study in the banking system in Iran has been studied. Due to the differences in the use of the system over time, it is better to perform the four different policies with regard to the use of the system. The numerical results show that the proposed model is convenient for the system so that the costs are reduced per day.

Abstract-Automatic image annotation refers to create text labels in accordance with images' context automatically. Although, numerous studies have been conducted in this area for the past decade, existence of multiple labels and semantic gap between these labels and visual low-level features reduced its performance accuracy. In this paper, we suggested an annotation method, based on dense weighted regional graph. In this method, clustering areas was done by forming a dense regional graph of area classification based on strong fuzzy feature vector in images with great precision, as by weighting edges in the graph, less important areas are removed over time and thus semantic gap between low-level features of image and human interpretation of high-level concepts reduces much more. To evaluate the proposed method, COREL database, with 5,000 samples have been used. The results of the images in this database, show acceptable performance of the proposed method in comparison to other methods.