SummaryProviding a pool of various resources and services to customers on the Internet in exchanging money has made cloud computing as one of the most popular technologies. Management of the provided resources and services at the lowest cost and maximum profit is a crucial issue for cloud providers. Thus, cloud providers proceed to auto‐scale the computing resources according to the users' requests in order to minimize the operational costs. Therefore, the required time and costs to scale‐up and down computing resources are considered as one of the major limits of scaling which has made this issue an important challenge in cloud computing. In this paper, a new approach is proposed based on MAPE‐K loop to auto‐scale the resources for multilayered cloud applications. K‐nearest neighbor (K‐NN) algorithm is used to analyze and label virtual machines and statistical methods are used to make scaling decision. In addition, a resource allocation algorithm is proposed to allocate requests on the resources. Results of the simulation revealed that the proposed approach results in operational costs reduction, as well as improving the resource utilization, response time, and profit.

Accurate prediction of a user's movement path has various advantages for many applications, such as optimising a nurse's trajectory in a hospital and assisting elderly or disabled people and making them feel secure and protected in the places where they live. Recently, researchers have suggested techniques based on machine learning and deep learning in this field. However, these approaches have drawbacks such as their low accuracy in classifying the extracted features into associated movement paths, high sensitivity to noisy data, and ignoring time dependencies within raw data. In this work, a threephase stacked method named CNN-LSTM-FSC is proposed, which uses the Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Fuzzy Soft-max Classifier (FSC) to overcome the mentioned constraints. In the first phase, the CNN structure extracts time dependencies within raw data using the stacked convolutional and pooling layer. In the second phase, the long-term time dependency of the user's movement path is learnt using the LSTM layers, and the user path is determined using a new innovated fuzzy soft-max classifier. Finally, in the post-processing phase, by performing a majority voting technique on the k-adjacent sample predictions of the classifier, the authors have tried to reduce the effects of noise in identifying the user's movement path. Experiments were conducted on the MovementAAL_RSS dataset. The proposed method has successfully reached 93.86%, 93.71%, and 93.26% accuracy rate on the Move-mentAAL_RSS datasets, with 0%, 5%, and 10% Gaussian noise, respectively, and demonstrates superior results in comparison to the previous literature research. K E Y W O R D Sconvolutional neural network, fuzzy soft-max classifier, long short-term memory, user's movement path | INTRODUCTIONDifferent user movement prediction methods attempt to derive patterns within input data that predict future events [1][2][3]. These patterns can be used effectively to optimise resources and provide intelligent services. Today, motion prediction methods are widely used in a variety of fields, including wireless cellular networks, Location-based Services (LBS) (which include navigation services, social networking services, monitoring and tracking objects in warehouses) [4,5], Ambient Assisted Living (AAL) [6,13] and various applications (including utilities-based applications, health care, safety systems, and alert applications). Two key components in movement prediction systems include hardware environment with its indoor positioning technique and its prediction or classification technique. Hardware environments with indoor positioning can be made in different ways, such as Wireless Sensor Networks (WSN) [5] or Ultra-Wideband (UWB) [7]. A WSN-based system consists of small sensors armed with environmental power sources, sensing devices, radio, and processor units. User's movement is predicted by utilising Received Signal Strength (RSS) information [8] between some WSN and a user of wearable sensors. Then data-drivenThis is an ope...

SUMMARYIn this paper, we use fuzzy Petri nets (FPNs) to propose a secure routing protocol in mobile ad hoc network. The proposed method is based on secure ad hoc on-demand distance vector (SAODV), which is named FPN-SAODV. In FPN-SAODV routing protocol, for each packet delivery or firing each transition, a type of bidirectional node-to-node fuzzy security verification is conducted that can be carried out with five security threshold levels. This inference uses four fuzzy variables that have been selected to well represent the malicious behaviors of some public attacks in mobile ad hoc network. Furthermore, a through route security verification has been used for selecting the most secure route among each candidate path through source node to destination. Both of these verifications utilize FPN inherent features for their operation. For evaluation purpose, we used the metrics such as packet delivery ratio, end-to-end delay, average security level of the nodes, and percentage of true/false detector nodes. These metrics have been used for investigating the inner operation of FPN-SAODV as determining the proper level of security threshold level in node-tonode security verification module. Also, these are used for comparison of FPN-SAODV performance versus the original AODV.

Cloud computing environment allows presenting different services on the Internet in exchange for cost payment. Cloud providers can minimize their operational costs by auto-scaling of the computational resources based on demand received from users. However, the time and cost required to increase and decrease the number of active computational resources are among the biggest limitations of scalability. Thus, auto-scaling is considered as one of the most important challenges in the field of cloud computing.The present study aimed to present a new solution to automatic scalability of resources for multilayered cloud applications under the Monitor-Analysis-Plan-Execute-Knowledge loop. In addition, the Google penalty payment model was used to model the penalty costs in the problem and to accurately evaluate the earned profit. A hybrid resource load prediction algorithm was proposed to evaluate the future of resources in each cloud layer. Further, we used statistical solution to determine the statuses of VMs in addition to presenting a risk-aware algorithm to allocate the user requests to active resources. The experimental results by Cloudsim indicated the improvement of the proposed approach in terms of operational costs, the number of used resources, and the amount of profit. K E Y W O R D Scloud computing, multilayered application, operational cost, scalability

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