A prominent parallel data processing tool MapReduce is gaining significant momentum from both industry and academia as the volume of data to analyze grows rapidly. While MapReduce is used in many areas where massive data analysis is required, there are still debates on its performance, efficiency

Abstract—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

time of the problem, which cannot be parallelized, and td(n) describes the time required for communication between the different nodes and the time for transferring data, which is usually bound to the maximal data transfer-rate of the underlying file system. The processing of ATLAS data can be highly

Abstract. Cloud computing is gaining acceptance in many IT organizations, as an elastic, flexible, and variable-cost way to deploy their service platforms using outsourced resources. Many-task computing (MTC) paradigm embraces different types of high-performance applications involving many different tasks, and requiring large number of computational resources over short period of time. In this paper, we implement private cloud by using eucalyptus middleware. It basically used to implement infrastructure as a service (IaaS).Thus it helps for the organization to create their own cloud structure which eliminates renting from the public cloud providers like Amazon Web Services. It also offers flexible infrastructure services that can be easily utilized and managed by end users according to their needs. It enables enterprises and government agencies to establish their own cloud computing environments. An important issue in cloud computing is how resources can be allocated and managed in a cost-effective manner.

Abstract: 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

Parallel data processing has become more and more reliable phenomenon due to the realization of could computing, especially using IaaS (Infrastructure as a Service) clouds. The cloud service providers such as IBM, Google, Microsoft and Oracle have made provisions for parallel data processing

Traditional views of automaticity are in need of revision. For example, automaticity otten has been treated as an all-or-none phenomenon, and traditional ~es have held that automatic processes are independent of attention. Yet recent empirical data suggest that automatic processes are continu-ous

A new set of benchmarks has been developed for the performance evaluation of highly parallel supercomputers. These benchmarks consist of ve \parallel kernel " benchmarks and three \simulated application" benchmarks. Together they mimic the computation and data movement

Abstract- Infrastructure as a Service (IaaS) clouds have emerged as a promising new platform for massively parallel data processing. By eliminating the need for large upfront capital expenses, operators of IaaS clouds offer their customers the unprecedented possibility to acquire access to a highly

Dryad is a general-purpose distributed execution engine for coarse-grain data-parallel applications. A Dryad applica-tion combines computational “vertices ” with communica-tion “channels ” to form a dataflow graph. Dryad runs the application by executing the vertices of this graph on a set