Cloud computing is a novel perspective for large scale distributed computing and parallel processing. It provides computing as a utility service on a pay per use basis. The performance and efficiency of cloud computing services always depends upon the performance of the user tasks submitted to the cloud system. Scheduling of the user tasks plays significant role in improving performance of the cloud services. Task scheduling is one of the main types of scheduling performed. This paper presents a detailed study of various task scheduling methods existing for the cloud environment. A brief analysis of various scheduling parameters considered in these methods is also discussed in this paper.

Web scraping is a technique to extract information from various web documents automatically. It retrieves the related contents based on the query, aggregates and transforms the data from an unstructured format into a structured representation. Text classification becomes a vital phase to summarize the data and in categorizing the webpages adequately. In this article, using effective web scraping methodologies, the data is initially extracted from websites, then transformed into a structured form. Based on the keywords from the data, the documents are classified and labeled. A recursive feature elimination technique is applied to the data to select the best candidate feature subset. The final data-set trained with standard machine learning algorithms. The proposed model performs well on classifying the documents from the extracted data with a better accuracy rate.

Transmission Control Protocol (TCP) has been the workhorse of the Internet ever since its inception. The success of the Internet, infact, can be partly attributed to the congestion control mechanisms implemented in TCP. Though the scale of the Internet and its usage increased exponentially in recent past, TCP has evolved to keep up with the changing network conditions and has proven to be scalable and robust. However, the performance of TCP in Data Center Networks has been a major concern recently because it leads to impairments such as TCP Incast, TCP Outcast, Queue build-up and Buffer pressure. With cloud computing becoming an important part of the foreseeable future, it has become extremely important to enhance the performance of TCP in Data Center Networks and overcome these impairments. In this paper, we describe the above mentioned impairments in brief and then compare the TCP variants proposed so far to ovecome these impairments in Data Center Networks. The advantages and shortcomings of every TCP variant are highlighted with respect to its efficacy and the deployment complexity. A few open issues related to TCP's performance in Data Center Networks are also discussed.

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