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Applications with different characteristics in the cloud may have different resources preferences. However, traditional resource allocation and scheduling strategies rarely take into account the characteristics of applications. Considering that an I/O-intensive application is a typical type of application and that frequent I/O accesses, especially small files randomly accessing the disk, may lead to an inefficient use of resources and reduce the quality of service (QoS) of applications, a weight allocation strategy is proposed based on the available resources that a physical server can provide as well as the characteristics of the applications. Using the weight obtained, a resource allocation and scheduling strategy is presented based on the specific application characteristics in the data center. Extensive experiments show that the strategy is correct and can guarantee a high concurrency of I/O per second (IOPS) in a cloud data center with high QoS. Additionally, the strategy can efficiently improve the utilization of the disk and resources of the data center without affecting the service quality of applications.
The relevance of identity data leaks on the Internet is more present than ever. Almost every week we read about leakage of databases with more than a million users in the news. Smaller but not less dangerous leaks happen even multiple times a day. The public availability of such leaked data is a major threat to the victims, but also creates the opportunity to learn not only about security of service providers but also the behavior of users when choosing passwords. Our goal is to analyze this data and generate knowledge that can be used to increase security awareness and security, respectively. This paper presents a novel approach to the processing and analysis of a vast majority of bigger and smaller leaks. We evolved from a semi-manual to a fully automated process that requires a minimum of human interaction. Our contribution is the concept and a prototype implementation of a leak processing workflow that includes the extraction of digital identities from structured and unstructured leak-files, the identification of hash routines and a quality control to ensure leak authenticity. By making use of parallel and distributed programming, we are able to make leaks almost immediately available for analysis and notification after they have been published. Based on the data collected, this paper reveals how easy it is for criminals to collect lots of passwords, which are plain text or only weakly hashed. We publish those results and hope to increase not only security awareness of Internet users but also security on a technical level on the service provider side.