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Cloud computing enables efficient resource sharing among several cloud users to run their applications and fulfill the business requirements. Despite the numerous advantages of cloud computing, the heterogeneity, uncertainty, and dynamic nature of user workload complicate the allocation and scheduling of cloud resources. However, improper resource allocation and scheduling result in resource waste and delays the execution of user tasks, which may volatile the Service Level Agreement (SLA). Hence, efficient resource scheduling techniques are highly desirable to maximize resource utilization and ensure efficient execution of user tasks with maintained SLA. In this Article, a multi-objective resource scheduling method is implemented to address the resource scheduling problem in a container-based cloud environment using Ant Colony Optimization algorithm (ACO). This method aims at maintaining a balance between resource utilization and efficient execution of user tasks with minimal time and cost. To evaluate the implemented method, three scenarios were conducted on ContaienrCouldSim toolkit. The experimental analysis reported that the implemented method archives high resource utilization while minimizing the makespan and execution cost in all the scenarios. [ABSTRACT FROM AUTHOR]

This paper conducts a comprehensive review of various word and sentence semantic similarity techniques proposed in the literature. Corpus-based, Knowledge-based, and Feature-based are categorized under word semantic similarity techniques. String and set-based, Word Order-based Similarity, POSbased, Syntactic dependency-based are categorized as sentence semantic similarity techniques. Using these techniques, we propose a model for computing the overall accuracy of the twitter dataset. The proposed model has been tested on the following four measures: Atish's measure, Li's measure, Mihalcea's measure with path similarity, and Mihalcea's measure withWu and Palmer's (WuP) similarity. Finally, we evaluate the proposed method on three real-world twitter datasets. The proposed model based on Atish's measure seems to offer good results in all datasets when compared with the proposed model based on other sentence similarity measures. [ABSTRACT FROM AUTHOR]