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The optimization of traditional FTL(Flash Translation Layer) algorithm is mainly aimed at the average response time of flash memory read and write operation on a flash chip, because of the Out-of-place update, traditional FTL algorithm writes data in a new free page each time, and a new block is assigned when a block is full. Therefore, when the flash memory is almost full, a written request will lead to a garbage collection, in which there will be many write copies, which will lead to a significant decrease in response time. In this paper, we optimize the traditional FTL algorithm and propose an FTL algorithm that guarantees real-time performance by shortening the worst response time of the request as a measure of the guaranteed flash Qos (Quality of Service) under the condition that the address mapping is provided the upper layer. The algorithm adopts the idea of space-for-time and spreads the written copy of flash memory to each write request so that the worst response time of the write request is significantly reduced. The algorithm also wears balancing considerations to the algorithm. After experimental verification, the proposed algorithm has advantages over existing FTL algorithms in terms of real-time performance and has a performance improvement of more than 11% in terms of average response time and wear balance. By the way, we add the wear-level module into an algorithm. According to improving the algorithm, the worst response time has shorten 47.6%. [ABSTRACT FROM AUTHOR]

After the service provider temporarily selects the required edge nodes based on social and storage capabilities, application execution causes the edge nodes to cache part of the application data. Therefore, offloading part of the application computing tasks to the selected edge nodes can effectively improve application execution performance. However, in cases where the resources of user's IoT devices are insufficient, tasks can be further offloaded to traditional edge servers or even to the cloud to maximize application execution efficiency. In this paper, the entire uninstall utility is modeled as a weighted sum of task completion time and energy consumption. Under the premise of considering users' preferences for completion time and energy consumption, a game-based uninstallation algorithm is proposed. The algorithm performs uninstallation by optimizing the uninstallation decision. Based on user preferences, the total system overhead is relatively small. The subsequent simulation experiments show that the algorithm can reduce system overhead on the basis of satisfying user preferences and has relatively good adaptability. [ABSTRACT FROM AUTHOR]