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While large-scale simulations have been the hallmark of the High Performance Computing (HPC) community for decades, Large Scale Data Analytics (LSDA) workloads are gaining attention within the scientific community not only as a processing component to large HPC simulations, but also as standalone scientific tools for knowledge discovery. With the path towards Exascale, new HPC runtime systems are...
This paper introduces the performance analysis of OpenStack Cloud with the commodity computers in the big data environments. It is necessary to analyze the performance of virtual system in cloud infrastructure with the commodity computers to identify effective and efficient system for processing big data. To determine performance measures, virtual systems were created in the Openstack cloud and Hadoop...
Big Data Systems are becoming increasingly complex and generally have very high operational costs. Cloud computing offers attractive solutions for managing large scale systems. However, one of the major bottlenecks in VM performance is virtualized I/O. Since Big Data applications and middleware rely heavily on high performance interconnects such as InfiniBand, the performance of virtualized InfiniBand...
Recent years have witnessed a surge of new generation applications involving big data. The de facto framework for big data processing, MapReduce, has been increasingly embraced by both academic and industrial users. Data locality seeks to co-locate computation with data, which effectively reduces remote data access and improves MapReduce’s performance in physical machine clusters. State-of-the-art...
Instead of scaling an application and data around the computer, programmers can use a software-defined server—an inverse hypervisor—in which multiple physical machines run a single virtual machine. Memory can be expanded as needed without modifying the application or limiting its data.
With the development of information technology, there are more and more meteorological services available. However, many problems also arise in the application process such as software and hardware platform redundant construction, data redundancy or large data storage. In view of these problems, this paper propose the design of provincial meteorological cloud, and describes the deployment model, architecture...
Cloud computing is a convenient model to easily access large amounts of computing resources in order to implement platforms for data intensive applications. These platforms, such as Hadoop, are designed to run on large clusters. When the amount of computing and networking resources are limited, such as in the emergent paradigm of edge computing, maximizing their utilization is of paramount importance...
Traditional distributed schedulers only consider the scheduling of jobs and treat the storage system as static and already deployed. However, individual application often needs its own configuration of storage system. Therefore, traditional distributed schedulers are not able to serve multiple tenants. To improve the resource utilization, some mechanisms are needed to consolidate multiple applications...
Cloud computing provides access to a set of resources such as virtual machines, storage and network as services. In this context, virtualization has been used as an platform for resource-intensive applications, like Hadoop, as it has brought features like server consolidation, scalability and better resources usage. OpenVZ and KVM are very popular and widely used virtualization platforms with distinct...
The Earth Observation data repositories are increasing each day by several terabytes. There is a need to provide efficient techniques to store and process this huge amount of data in order to provide end-users with valuable information and knowledge. The architectural solution proposed in this paper is based on cloud virtualization and aims to provide a flexible and adaptive method to extract and...
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