With the development of Internet of Things (IoT), massive amounts of data will be brought. By offloading caching from the cloud to the edge, edge caching technology represents a promising solution in the era of IoT to meet the delay requirements of IoT applications. An efficient cache decision and replacement strategy on edge caching devices is a key factor in ensuring the cache hit ratio. The existing cache replacement policies do not comprehensively consider the characteristics of cache files and are likely to result in cache pollution problems. In order to cache data reasonably, to improve the cache hit ratio, and to reduce the user request delay, we propose a concept of file cache value and a file cache value‐aware cache replacement (FCVACR) algorithm of the edge cache system in this article. File cache value is associated with three aspects of cache file: the file size, the file popularity, and the time of requests. The proposed FCVACR algorithm adopts the file cache value method, improves the utilization of edge caching space, and reduces the content transmission delay. Experimental results show that the proposed FCVACR algorithm has a higher cache hit ratio and lower user request delay than the classical cache replacement algorithms.
It is crucial to detect high-severity defects, such as memory leaks that can result in system crashes or severe resource depletion, in order to reduce software development costs and ensure software quality and reliability. The primary cause of high-severity defects is usually resource scheduling errors, and in the program source code, these defects have contextual features that require defect context to confirm their existence. In the context of utilizing machine learning methods for defect automatic confirmation, the single-feature label method cannot achieve high-precision defect confirmation results for high-severity defects. Therefore, a multi-feature fusion defect automatic confirmation method is proposed. The label generation method solves the dimensionality disaster problem caused by multi-feature fusion by fusing features with strong correlations, improving the classifier’s performance. This method extracts node features and basic path features from the program dependency graph and designs high-severity contextual defect confirmation labels combined with contextual features. Finally, an optimized Support Vector Machine is used to train the automatic detection model for high-severity defects. This study uses open-source programs to manually implant defects for high-severity defect confirmation verification. The experimental results show that compared with existing methods, this model significantly improves the efficiency of confirming high-severity defects.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2025 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.