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Ever-increasing big data forces enterprises to migrate data to cloud storage systems. Data retrieval time from the cloud will directly affect the overall application performance. Meanwhile, sensitive data stored on cloud necessitates a robust security arrangement against cyberattacks. Therefore, it is imperative that both data retrieval time and data security should be taken into account simultaneously...
The purpose of research data placement strategy is to improve the fault tolerance of data center systems. However, commonly used strategies are not considering the transmission time to repair the failure nodes and load balancing together. In this paper, we propose a data placement strategy applied to BCube data center network topology, which has jointly taken data access efficiency, the recovery time...
The size of data centers is becoming larger to deal with the exponential data growth, and the energy consumption challenges the services providers and the environment. Various data placement strategies were developed to reduce the energy consumption of processing big data on the level of storage system, but they were typically developed for specific applications and storage medium. This paper proposes...
MapReduce has become an important distributed processing model for large-scale data-intensive applications like data mining and web indexing. Hadoop-an open-source implementation of MapReduce is widely used for short jobs requiring low response time. The current Hadoop implementation assumes that computing nodes in a cluster are homogeneous in nature. Data locality has not been taken into account...
In parallel database system, optimizing distribution of relations could improve processing efficiency of multi-join queries greatly. The cost of data communication is expensive in parallel system based on PC clusters. This paper proposes distribution of relations algorithm to select an appropriate data placement strategy for each relation, which includes selection of distribution attributes and nodes...
Traditional method of storing and querying XML documents is in single processor environment. The system performance deteriorates when the size of XML data sets increased. Parallel processing technology based on multi-processor is proposed in this paper. The key issues such as XML data model, storage model, data placement strategy, paralleling query, indexing etc. are also discussed.
Traditional method of storing and querying XML documents is in single processor environment. The system performance deteriorates when the size of XML data sets increased. Parallel processing technology based on multi-processor is proposed in this paper. The key issues such as XML data model, storage model, data placement strategy, paralleling query, indexing etc. are also discussed.
Although data duplications may be able to improve the performance of data-intensive applications on data grids, a large number of data replicas inevitably increase energy dissipation in storage resources on the data grids. In order to implement a data grid with high energy efficiency, we address in this study the issue of energy-efficient scheduling for data grids supporting real-time and data-intensive...
Despite using high-speed network interconnection systems like InfiniBand, the communication overhead for parallel applications is still high. In this paper we show, how such costs can be reduced by choosing appropriate data placement strategies. For large buffers, we propose a transparent placing in hugepages as it can dramatically decrease memory registration overhead and may increase network bandwidth...
We discuss the peer-to-peer middleware architecture for developing grid applications. We present the key issues in building services for large-scale distributed systems. Our approach of developing a replication service using a scalable, reconfigurable shared-object space realized over two-layer P2P architecture appears unique. However, we need to benchmark the platform to test the performance. Also,...
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