Map-Reduce are a programming model that enables easy development of scalable parallel applications to process vast amounts of data on large clusters of commodity machines.  Based on this new framework, we perform extended evaluations of Map Reduce-inspired processing jobs on an IaaS cloud system and compare the results to the popular data processing framework Hadoop. Through a simple interface with two functions, map and reduce, this model facilitates parallel implementation of many real-world tasks such as data processing for search engines and machine learning. In recent years ad hoc parallel data processing has emerged to be one of the killer applications for Infrastructure-as-a- Service (IaaS) clouds. Major Cloud computing companies have started to integrate frameworks for parallel data processing in their product portfolio, making it easy for customers to access these services and to deploy their programs. Nephele is the first data processing framework to explicitly exploit the dynamic resource allocation offered by today’s IaaS clouds for both, task scheduling and execution. Particular tasks of a processing job can be assigned to different types of virtual machines which are automatically instantiated.

In order to simplify the development of    distributed applications on top of such architectures, many of these companies have also built customized data processing frameworks. They can be classified by terms like high throughput computing (HTC) or many-task computing (MTC), depending on the amount of data and the number of tasks involved in the computation. Although these systems differ in design, their programming models share similar objectives, namely hiding the hassle of parallel programming, fault tolerance, and execution optimizations from the developer. Developers can typically continue to write sequential programs. The processing framework then takes care of distributing the program among the available nodes and executes each instance of the program on the appropriate fragment of data. Instead, Cloud computing has emerged as a promising approach to rent a large IT infrastructure on a short-term pay-per-usage basis. Operators of so-called Infrastructure-as-a-Service allocated. In this paper we want to discuss the particular challenges and opportunities for efficient parallel data processing in clouds and present Nephele, a new processing framework explicitly designed  cloud environments. Most notably, Nephele is the first data processing framework to include the possibility of dynamically allocating/deallocating different compute resources from a cloud in its scheduling and during job execution



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