In recent years unintentional parallel data processing has emerged to be one in every of the killer applications for Infrastructure-as-a-Service (IaaS) clouds. Major Cloud computing firms have started to integrate frameworks for parallel data processing in their product portfolio, creating it easy for purchasers to access these services and to deploy their programs. However, the processing frameworks that are currently used have been designed for static, homogeneous cluster setups and disrespect the actual nature of a cloud. Consequently, the allocated compute resources could be inadequate for giant parts of the submitted job and unnecessarily increase processing time and cost. In this paper, we tend to discuss the opportunities and challenges for efficient parallel data processing in clouds and gift our analysis project Nephele. Nephele is the first information processing framework to explicitly exploit the dynamic resource allocation offered by nowadays's IaaS clouds for both, task scheduling and execution. Explicit tasks of a processing job will be assigned to totally different types of virtual machines that are automatically instantiated and terminated throughout the task execution. Based on this new framework, we have a tendency to perform extended evaluations of MapReduce-impressed processing jobs on an IaaS cloud system and compare the results to the favored data processing framework Hadoop.
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