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Cloud computing is a popular computing concept that performs processing of huge volume of data using highly accessible geographically distributed resources that can be accessed by users on the basis of Pay as per Use policy. Requirements of different users may change so the amount of processing involved in such paradigm also changes. Sometimes they need huge data processing. Such highly volumetric processing results in higher computing time and cost which is not a desirable part of a good computing model. So there must be some intelligent distribution of user's work on the available resources which will result in an optimized computing environment. This paper gives a comprehensive survey on such problems and provide a detailed analysis of some best scheduling techniques from the domain of soft computing with their performance in cloud computing.

Cloud computing is a popular computing paradigm that performs processing of huge volumes of data using highly available geographically distributed resources that can be accessed by users on the basis of Pay As per Use policy. In the modern computing environment where the amount of data to be processed is increasing day by day, the costs involved in the transmission and execution of such amount of data is mounting significantly. So there is a requirement of appropriate scheduling of tasks which will help to manage the escalating costs of data intensive applications. This paper analyzes various evolutionary and swarm based task scheduling algorithms that address the above mentioned problem.

In the last few years, the size and functionality of software have experienced a massive growth. Along with this, cost estimation plays a major role in the whole cycle of software development, and hence, it is a necessary task that should be done before the development cycle begins and may run throughout the software life cycle. It helps in making accurate estimation for any project so that appropriate charges and delivery date can be obtained. It also helps in identifying the effort required for developing the application, which assures the project acceptance or denial. Since late 90's, Agile Software Development (ASD) methodologies have shown high success rates for projects due to their capability of coping with changing requirements of the customers. Commencing product development using agile methods is a challenging task due to the live and dynamic nature of ASD. So, accurate cost estimation is a must for such development models in order to fine-tune the delivery date and estimation, while keeping the quality of software as the most important priority. This paper presents a systematic survey of cost estimation in ASD, which will be useful for the agile users to understand current trends in cost estimation in ASD.

Opinion-mining generally refers to analyzing opinions on various topics available in the form of text. It is an essential operation of natural language processing since it enables efficient decision-making and planning for users and businesses. Opinion-mining can be made more comfortable and more effective by initially performing subjectivity detection, i.e., identifying the text as subjective or objective. An opinion-mining model can better identify the opinions present in the remaining subjective statements by removing objective statements. With this reasoning, we present an efficient subjectivity detection model for improved accuracy in Opinion-mining. The model uses a strategic combination of convolutional neural network (CNN) and long short-term memory (LSTM). CNN and LSTM are state-of-the-art deep learning models that can efficiently process textual data and identify inherent connections and patterns with varying abstraction levels. The proposed work combines the strengths of both these models in an ensemble model. Effectiveness of the model is enhanced with the incorporation of an attention network. In the present task, the sentences are represented as word embeddings that include sentiment information and part-of-speech. The proposed model is applied on two movie review datasets, and its performance is evaluated compared with state-of-the-art methods. Various performance indexes have validated the superiority of the proposed model in the opinion-mining task.