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Internet of recent decades considered cloud computing as the most effective and distributed platform. It is a comfortable and quick way to access shared resources over the Internet anytime. The major problem cloud customers face while choosing the resources for a particular application is QoS. In the cloud computing environment, various resources need to be effectively allocated on VMs by reducing makespan and synchronously increasing resource utilization. For that, a novel multi-objective hybrid capuchin search with genetic algorithm (MHCSGA) based hierarchical resource allocation is established in this work. MHCSGA optimizes the multi-objective functions like resource utilization, response time, makespan, execution time and throughput. Initially, partitioning around the K-medoids clustering method is utilized to allocate the resources optimally. During clustering, the tasks are divided into two cluster groups then, the optimization is performed to attain an optimal resource allocation process.The experimental setup is executed using the JAVA tool. For the simulation process, the proposed work uses the GWA-T-12 Bitbrains dataset. The makespan achieved by proposed algorithm for 50, 100, 150, and 200 tasks are found to be 10. 45, 17.6, 25.67, and 31.34, respectively. The comparison analysis proves that the developed model attains improved performance than the state-of-the-art works.

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In a cloud environment, resources should be acquired quickly and automatically released at runtime. Traditional trajectory data partitions, indexing and query processing techniques are extended, so that they can take advantage of the cloud of large clusters of highly parallel processing capabilities. There are ways with trajectory data in the cloud database query processing. The advanced sensing techniques available today caused the existence of many types of trajectory datasets whose study can form a strong basis for decision making regarding a data context. Trajectory data generally includes trajectory classification, trajectory clustering, and trajectory associations and so on. All these tools need trajectory similarity measures to get comparisons in trajectory data. Some of the popular existing methods of trajectory similarity include Euclidean distance, semantic distance and there near variants. Each variant of these techniques exhibits notable differences in measurement of similarity and computational difficulty. This paper targeted to deal with this situation with the goal of lower computational efforts in trajectory similarity computations. A two-phase trajectory similarity measure is defined. The first phase does point level analysis of trajectory points representing all trajectories and the following phase use the analysis to find the similarities. Using a hybrid similarity framework, new means for trajectory clustering and trajectory query processing are developed. From trajectory analysis followed by trajectory clustering, evaluation of cluster quality and processing of trajectory query are undertaken in this paper in different ways from the existing methods. All the proposed developments are tested with trajectory datasets with multiple attributes and the results are more favour to proposed framework than the existing ones.

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