Task scheduling is one of the essential techniques in the cloud computing environment. It is required for allocating tasks to the proper resources and optimizing the overall system performance. Particle swarm optimization (PSO) algorithm is one of the most popular scheduling algorithms, which is used to maximize resource utilization. However, the performance of the PSO scheduling algorithm decreases when the number of tasks is significant. In this paper, the improved PSO (IPSO) algorithm is proposed to provide the optimal allocation for a large number of tasks. This is achieved by splitting the submitted tasks into batches in a dynamic way. The resources utilization state is considered in each creation of batches. After getting a sub-optimal solution for each batch, the algorithm appends all the sub-optimal solutions for batches into a final allocation map. Finally, IPSO tries to balance the loads over the final allocation map. The proposed algorithm is compared with different scheduling algorithms, namely, honey bee, ant colony, and round-robin algorithms. The results of experiments show the efficiency of the proposed algorithm in terms of makespan, standard deviation of load, and degree of imbalance.INDEX TERMS Cloud computing, task scheduling, load balancing, particle swarm optimization.

with the increasing popularity of Internet of Things (IoT) applications in many different areas, the round-trip delay of data processing in the cloud affect the user's quality perception. But fortunately, fog computing aims to service users at the network edge similar to cloud services, which helps in supporting IoT in processing the data near to the end-user, especially for time-sensitive applications. It makes the resource allocation of application placement requests in a fog environment more necessary to satisfy the Quality of Experience (QoE) Influence Factors (IFs). In this paper, an IoT application placement algorithm based on the Multi-Dimensional QoE (MD-QoE) model is proposed in a fog computing environment. The algorithm is composed of two main phases. The first phase is to prioritize different IoT application placement requests depending on three main domains of IFs which are: Environment runtime context, application usage, and user expectations considering the Quality of Service (QoS) violation as a feedback. The second phase is to map and place the request to the appropriate fog node instance, depending on its proximity, computing capabilities, and expected response time. The proposed algorithm is evaluated by simulating a fog environment using iFogSim. Experimental results indicate that the proposed algorithm significantly improves the QoE in respect of application placement time, application delay, network usage, and power consumption. Therefore, the proposed algorithm can improve the overall system performance with a slight increasing in power consumption in fog control nodes.

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In this paper, a new technique for plane detection from 3D point clouds is proposed. The algorithm depends on two concepts to balance between high-accuracy and fast performance. The first is the use of a new fast octree-based balanced density down-sampling technique to reduce the number of points. The second is the fact that the number of planes in any dataset is much less than the number of the points. Random points are examined to find the 3D planes. To increase the accuracy, the system utilizes an adaptive plane extraction technique to overcome data noise. Initially, the point cloud is subdivided using octree into small cubes with a limited number of points. Then the cubes are down-sampled based on the local density of each cube. The points are explored randomly for finding a planar surface by applying principal component analysis (PCA) on the points' spherical neighborhood obtained by the down-sampled octree structure. The adaptive plane extraction is used to adjust the plane orientation to find the best position that includes the maximum number of points. Experimental results demonstrate that the proposed algorithm is capable of processing large amounts of data efficiently to produce accurate results that are robust to noise.

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