No abstract

Purpose Today, with the rapid growth of cloud computing (CC), there exist several users that require to execute their tasks by the available resources to obtain the best performance, reduce response time and use resources. However, despite the significance of the scheduling issue in CC, as far as the authors know, there is not any systematic and inclusive paper about studying and analyzing the recent methods. This paper aims to review the current mechanisms and techniques, which can be addressed in this area. Design/methodology/approach The central purpose of this paper refers to offering a complete study of the state-of-the-art planning algorithms in the cloud and also instructions for future research. Besides, this paper offers a methodological analysis of the scheduling mechanisms in the cloud environment. Findings The central role of this paper is to present a summary of the present issues related to scheduling in the cloud environment, providing a structure of some popular techniques in cloud scheduling scope and defining key areas for the development of cloud scheduling techniques in the future research. Research limitations/implications In this paper, scheduling mechanisms are classified into two main categories include deterministic and non-deterministic algorithms; however, it can also be classified into different categories. In addition, the selection of all related papers could not be ensured. It is possible that some appropriate and related papers were removed in the search process. Practical implications According to the results of this paper, the requirement for more suitable algorithms exists to allocate tasks for resources in cloud environments. In addition, some principal rules in cloud scheduling should be re-evaluated to achieve maximum productivity and minimize wasted expense and effort. In this direction, to stay away from overloading and under loading of components and resources, the proposed method should execute workloads in an adaptable and scalable way. As the number of users increased in cloud environments, the number of tasks in the cloud that needed to be scheduled proportionally increased. Thus, an efficient mechanism is needed for scheduling tasks in these environments. Originality/value The general information gathered in this study makes the researchers acquainted with the state-of-the-art scheduling area of the cloud. Entirely, the answers to the research questions summarized the main objective of scheduling, current challenges, mechanisms and methods in the cloud systems. The authors hope that the results of this paper lead researchers to present more efficient scheduling techniques in cloud systems.

Emotion detection has been extensively studied in recent years. Current baseline methods often use token-based features which cannot properly capture more complex linguistic phenomena and emotional composition in fine grained emotion detection. A novel supervised learning approach-segment-based fine-grained emotion detection model for Chinese text has been proposed in this paper. Different from most existing methods, the proposed model applies the hierarchical structure of sentence (e.g., dependency relationship) and exploits segment-based features. Furthermore, the emotional composition in short text is addressed by using the log linear model. We perform emotion detection on our dataset: news contents, fairly tales, and blog dataset, and compare our proposed method to representative existing approaches. The experimental results demonstrate the effectiveness of the proposed segment-based model.