Recently, increasingly large amounts of data are generated from a variety of sources. Existing data processing technologies are not suitable to cope with the huge amounts of generated data. Yet, many research works focus on Big Data, a buzzword referring to the processing of massive volumes of (unstructured) data. Recently proposed frameworks for Big Data applications help to store, analyze and process the data. In this paper, we discuss the challenges of Big Data and we survey existing Big Data frameworks. We also present an experimental evaluation and a comparative study of the most popular Big Data frameworks with several representative batch and iterative workloads. This survey is concluded with a presentation of best practices related to the use of studied frameworks in several application domains such as machine learning, graph processing and real-world applications.

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During the last decade, cloud computing became a natural choice to host and provide various computing resources as on‐demand services. To better satisfy user requirements, cloud services may be combined while considering the constraints of the virtualized environment, including security policies, resources availability, and interoperability. Extensive surveys have been conducted to study the major issues related to the cloud service composition problem. However, very few works have studied such issues in a multicloud setting. To fill this gap, we provide in this paper a systematic literature review on multicloud service composition. We start with a background on service composition in single clouds. Then, we present the multicloud taxonomy, and we study how service composition was tackled by researchers in multicloud environments. Finally, we identify the challenges and the requirements of multicloud service composition, as well as the future directions.

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