Advancements in web-based technology and the proliferation of sensors and mobile devices interacting with the internet have resulted in immense data management requirements. These data management activities include storage, processing, demand of high-performance read-write operations of big data. Large-scale and high-concurrency applications like SNS and search engines have appeared to be facing challenges in using the relational database to store and query dynamic user data. NoSQL and cloud computing has emerged as a paradigm that could meet these requirements. The available diversity of existing NoSQL and cloud computing solutions make it difficult to comprehend the domain and choose an appropriate solution for a specific business task. Therefore, this chapter reviews NoSQL and cloud-system-based solutions with the goal of providing a perspective in the field of data storage technology/algorithms, leveraging guidance to researchers and practitioners to select the best-fit data store, and identifying challenges and opportunities of the paradigm.

Working in cloud environment and accessing its services is fascinating task. It is one of modern technological areas to work upon it for development of nation and increase economy rate. The paper deals with improving desktop cloud computing services with the help of technique called Memory Virtualization. Since virtualization is efficient to extent scalability of typical desktop-as-a-service and make it suitable for Wide Area Network (WAN's) environments. Desktop-as-a-Service (DaaS) in cloud computing means that user can access its own desktop services as well as services of other desktops located on remote servers. Memory virtualization not only works with virtual memory but it also maps physical memory instructions to actual machine memory. The instructions stored in machine memory are checked with incoming user client requests. Similar matched queries will result in generation of data using one of recovery techniques called Shadow Paging Technique. Data is produced and stored in remote cloud environment where user can access the data to perform their tasks. The proposed systematic model for accessing desktop services is shown in following paper by using memory virtualization technique. A comparison is also made between paravirtualization technique and memory virtualization technique that gives solution in favor of memory virtualization technique. KeywordsCloud computing, Desktop-as-a-Service (DaaS), virtualization, memory virtualization, shadow paging Literature SurveyContinuous efforts in field of cloud computing by various researchers and scholars have made this area as one of legend in IT sector. It is known that for using multiple virtual machines to access data also requires management of data and its collection. Its resources must be managed in optimal way in accordance with cloud providers service laws and agreements. Few data accessing and resource management techniques are shown below:

With the advancements in science and technology, data is being generated at a staggering rate. The raw data generated is generally of high value and may conceal important information with the potential to solve several real-world problems. In order to extract this information, the raw data available must be processed and analysed efficiently. It has however been observed, that such raw data is generated at a rate faster than it can be processed by traditional methods. This has led to the emergence of the popular parallel processing programming model – MapReduce. In this study, the authors perform a comparative analysis of two popular data processing engines – Apache Flink and Hadoop MapReduce. The analysis is based on the parameters of scalability, reliability and efficiency. The results reveal that Flink unambiguously outperformance Hadoop's MapReduce. Flink's edge over MapReduce can be attributed to following features – Active Memory Management, Dataflow Pipelining and an Inline Optimizer. It can be concluded that as the complexity and magnitude of real time raw data is continuously increasing, it is essential to explore newer platforms that are adequately and efficiently capable of processing such data.

Fog computing enhances cloud computing to be closer to the processes that act on IOT devices. Fogging was introduced to overcome the cloud computing paradigm which was not able to address some services, applications, and other limitations of cloud computing such as security aspects, bandwidth, and latency. Fog computing provides the direct correlation with the internet of things. IBM and CISCO are linking their concepts of internet of things with the help of fog computing. Application services are hosted on the network edge. It improves the efficiency and reduces the amount of data that is transferred to the cloud for analysis, storage, and processing. Developers write the fog application and deploy it to the access points. Several applications like smart cities, healthcare domain, pre-processing, and caching applications have to be deployed and managed properly.