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<strong>Currently, cloud computing is facing different types of threats whether from inside or outside its environment.  This may cause cloud to be crashed or at least unable to provide services to the requests made by clients. In this paper, a new technique is proposed to make sure that the new node which asks to join the cloud is not composing a threat on the cloud environment. Our new technique checks the node before it will be guaranteed to join the cloud whether it runs malwares or software that could be used to launch an attack. In this way the cloud will allow only the clean node to join it, eliminating the risk of some types of threats that could be caused by infected nodes.</strong>

Background: Active Queue Management (AQM) is a TCP congestion avoidance approach that predicts congestion before sources overwhelm the buffers of routers. Random Early Detection (RED) is an AQM strategy that keeps history of queue dynamics by estimating an average queue size parameter avg and drops packets when this average exceeds preset thresholds. The parameter configuration in RED is problematic and the performance of the whole network could be reduced due to wrong setup of these parameters. Drop probability is another parameter calculated by RED to tune the drop rate with the aggressiveness of arriving packets. Objective: In this article, we propose an enhancement to the drop probability calculation to increase the performance of RED. Methods: This article studies the drop rate when the average queue size is at the midpoint between the minimum and maximum thresholds. The proposal suggests a nonlinear adjustment for the drop rate in this area. Hence, we call this strategy as the Half-Way RED (HRED). Results: Our strategy is tested using the NS2 simulator and compared with some queue management strategies including RED, TD and Gentle-RED. The calculated parameters are: throughput, link utilization and packet drop rate. Conclusion: Each performance parameter has been plotted in a separate figure; then the robustness of each strategy has been evaluated against these parameters. The results suggest that this function has enhanced the performance of RED-like strategies in controlling congestion. HRED has outperformed the strategies included in this article in terms of throughput, link utilization and packet loss rate.

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Web services are growing rapidly to provide clients, either organizations or individuals, with multiple Internet services and to offer solutions for the integration of many applications. Quality of Service (QoS) of a Web service is the key consideration of both service providers and users. Thus, measuring the QoS requires, in addition to its normative requirements, engaging the views of clients and service providers and environmental factors. Human intervention and the environment may lead to uncertainty and result in uncertain factors in assessing QoS. In such a case, traditional computing and statistical techniques cannot provide an accurate representation of inherited uncertainties, especially when uncertain variables are connected with ambiguous (fuzzy) relationships. An alternative is to use a soft computing approach. This paper proposes a Fuzzy Cognitive Map (FCM) model as a soft computing approach that can represent and simulate the uncertainty of QoS. FCM represents the uncertain variables in the domain knowledge and their connections in the form of a signed directed graph consisting of nodes representing the variables and directed arrows representing the cause-effect relationships. In addition, it allows representing imprecise data either using numeric data, i.e. in the ranges [0, 1] and [-1, 1], or linguistic data, i.e., "low, medium, high". For calculations, FCM is converted to an adjacency matrix to find the effects of variables on each other. Scenario simulations can also be implemented to help decision makers to investigate appropriate outcomes. Finally, the proposed approach is tested by an experiment to demonstrate its reasonability and admissibility of the representation and simulation of the uncertainty of the QoS domain knowledge.