Dynamically managing service level agreements (SLAs) is a non-trivial issue for both active media technology users and providers. Cloud computing is considered as one of the significant components of the rapidly emerging active media in the current era. As the technology grows rapidly, cloud computing requirements and techniques also change over time, while the current cloud SLA management methods are rigorous in terms of SLA contents updates with the technology innovation and user requirement change. In other words, public SLAs are used as templates, and when issuing a new SLA, it is necessary to map its requirements to all public SLAs. In addition, updating any consumer's SLA requires a cumbersome process of remapping their changed requirements to all public SLAs. This paper proposes a cloud computing SLA management mechanism based on the real options analysis concept to manage cloud SLAs in a dynamic manner. At the beginning, or when issuing a new SLA, the proposed framework receives a new user's requirements and maps these to all public SLAs, while executes only the most appropriate SLA based on options analysis and records/marks the other related solutions (SLAs) for future analysis to address any further change due to internal/external factor. This also provides a mechanism to effectively counter uncertainty parameters triggering dynamic change in cloud SLA management. The framework was deployed using Web SLA and Java followed by an experimental study to evaluate the efficacy and scalability of the proposed framework. The results show that the proposed framework provides efficient and scalable cloud SLA management mechanism.
Information sharing via social networking systems (SNS) is a common practice among academics, as well as others, that brings substantial benefits. At the same time, privacy concerns are widespread among SNS users, which may tend to inhibit their maximising the benefit from using the systems. This paper investigates the proposition that SNS user attitudes and behaviour are affected by privacy concerns, and that the effects are subject to significant cultural factors. A broad assessment of the literature provides the context for the study. Working in the context of Saudi Arabia, we apply a mixed-methods approach beginning with in-depth interviews, exposing in detail a range of views and concerns about privacy and SNS use, also allowing us to identify three key factors that bear on SNS usage and users’ concerns. Analysis of these factors in the light of the “theory of reasoned action” derives a structural model predicting several hypotheses relating the factors and users’ attitudes and behaviour. We assess the model through development of a questionnaire, administered to a large pool of academic participants, that allows us to examine how the model responds in general, and via multigroup partial least squares analyses, differentially to gender and to culturally distinct (Arab vs. non-Arab) constituents of the participant group. Results show good support for the hypotheses and clear gender and culture effects. Picking up issues from the interviews, discussion focuses on users’ views about SNS providers’ privacy policies and their inadequacy regarding culturally specific ethical concerns. We argue that these views may reflect different regulatory environments in combination with other cultural factors.
Selective imaging is a new concept in computer forensics. It is used for collecting only the data that is relevant to the crime and helps in improves the scalability of the investigation process. However, the current selective imaging approaches directly image the identified data without considering their offsets on the targeted user storage. This paper investigates the impact of the relevant data offsets on the efficiency of the selective imaging process. A practical selective imaging model is presented which includes a digital evidence ordering algorithm (DEOA) for ordering the selected relevant data items. The proposed selective imaging model has been implemented and evaluated in different types of storage devices. The evaluation result shows that even if our proposed algorithm has a small efficiency negative impact before the imaging process starts; it has a large positive effect on the efficiency of the selective imaging process itself.
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