There is a growing trend of using cloud environments for ever growing storage and data processing needs. However, adopting a cloud computing paradigm may have positive as well as negative effects on the data security of service consumers. This paper primarily aims to highlight the major security issues existing in current cloud computing environments. We carry out a survey to investigate the security mechanisms that are enforced by major cloud service providers. We also propose a risk analysis approach that can be used by a prospective cloud service for analyzing the data security risks before putting his confidential data into a cloud computing environment.

MapReduce is a popular programming model for distributed data processing. Extensive research has been conducted on the reliability of MapReduce, ranging from adaptive and on-demand fault-tolerance to new fault-tolerance models. However, realistic benchmarks are still missing to analyze and compare the effectiveness of these proposals. To date, most MapReduce fault-tolerance solutions have been evaluated using microbenchmarks in an ad-hoc and overly simplified setting, which may not be representative of real-world applications. This paper presents MRBS, a comprehensive benchmark suite for evaluating the dependability of MapReduce systems. MRBS includes five benchmarks covering several application domains and a wide range of execution scenarios such as data-intensive vs. compute-intensive applications, or batch applications vs. online interactive applications. MRBS allows to inject various types of faults at different rates. It also considers different application workloads and dataloads, and produces extensive reliability, availability and performance statistics. We illustrate the use of MRBS with Hadoop clusters running on Amazon EC2, and on a private cloud.

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