Cloud computing is an attractive processing model, it allows clients to use the internet and central remote servers to manipulate data, applications and access their personal files at any computer without installation of extra software. This technology allows more efficient computing by centralizing storage, memory, processing and bandwidth. Optimizing resources in the cloud is a main benefit, minimizing cost and satisfying client requests are the goal. In this paper, many resource allocation strategies and their challenges are presented. It is believed that this paper would help both cloud users and researchers to be aware with many applied resource allocation strategies.
Abstract-Aspect mining is a reverse engineering process that aims at mining legacy systems to discover crosscutting concerns to be refactored into aspects. This process improves system reusability and maintainability. But, locating crosscutting concerns in legacy systems manually is very difficult and causes many errors. So, there is a need for automated techniques that can discover crosscutting concerns in source code. Aspect mining approaches are automated techniques that vary according to the type of crosscutting concerns symptoms they search for. Code duplication is one of such symptoms which risks software maintenance and evolution. So, many code clone detection techniques have been proposed to find this duplicated code in legacy systems. In this paper, we present a clone detection technique to extract exact clones from object-oriented source code using Differential File Comparison Algorithm (DIFF) to improve system reusability and maintainability which is a major objective of aspect mining.
Nowadays, more and more human activity recognition (HAR) tasks are being solved with deep learning techniques because it's high recognition rate. The architectural design of deep learning is a challenge because it has multiple parameters which effect on the result. In this work, we propose a novel method to enhance deep learning architecture by using genetic algorithm and adding new statistical features. Genetic algorithm is utilized as an enhancing method to get the optimal value parameters of deep learning. Also new statistical features are appended to the features that are extracted automatically from CNN technique. Because the spread of the internet and its significance in our life, we developed Internet of Things (IoT) system. Therefore, we evaluated the performance of the proposed method in its system and found satisfactory results. Moreover, the proposed method was trained on two benchmark datasets (WISDM and UCI) and tested on the dataset, which was collected from IoT system. The results showed that the proposed model improved the accuracy up to 93.8% and 86.1% for user-dependent and independent.
Due to the energy limitation in Wireless Sensor Networks (WSNs), most researches related to data collection in WSNs focus on how to collect the maximum amount of data from the network with minimizing the energy consumption as much as possible. Many types of research that are related to data collection are proposed to overcome this issue by using mobility with path constrained as Maximum Amount Shortest Path routing Protocol (MASP) and zone-based algorithms. Recently, Zone-based Energy-Aware Data Collection Protocol (ZEAL) and Enhanced ZEAL have been presented to reduce energy consumption and provide an acceptable data delivery rate. However, the time spent on data collection operations should be taken into account, especially concerning real-time systems, as time is the most critical factor for these systems' performance. In this paper, a routing protocol is proposed to improve the time needed for the data collection process considering less energy consumption. The presented protocol uses a novel path with a communication time-slot assignment algorithm to reduce the count of cycles that are needed for the data collection process with reduction of 50% of the number of cycles needed for other protocols. Therefore, the time and energy needed for data collection are reduced by approximately 25%and 6% respectively, which prolongs the network lifetime. The proposed protocol is called Energy-Time Aware Data Collection Protocol (ETCL).
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