5 research outputs found

    Data Replication and Its Alignment with Fault Management in the Cloud Environment

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    Nowadays, the exponential data growth becomes one of the major challenges all over the world. It may cause a series of negative impacts such as network overloading, high system complexity, and inadequate data security, etc. Cloud computing is developed to construct a novel paradigm to alleviate massive data processing challenges with its on-demand services and distributed architecture. Data replication has been proposed to strategically distribute the data access load to multiple cloud data centres by creating multiple data copies at multiple cloud data centres. A replica-applied cloud environment not only achieves a decrease in response time, an increase in data availability, and more balanced resource load but also protects the cloud environment against the upcoming faults. The reactive fault tolerance strategy is also required to handle the faults when the faults already occurred. As a result, the data replication strategies should be aligned with the reactive fault tolerance strategies to achieve a complete management chain in the cloud environment. In this thesis, a data replication and fault management framework is proposed to establish a decentralised overarching management to the cloud environment. Three data replication strategies are firstly proposed based on this framework. A replica creation strategy is proposed to reduce the total cost by jointly considering the data dependency and the access frequency in the replica creation decision making process. Besides, a cloud map oriented and cost efficiency driven replica creation strategy is proposed to achieve the optimal cost reduction per replica in the cloud environment. The local data relationship and the remote data relationship are further analysed by creating two novel data dependency types, Within-DataCentre Data Dependency and Between-DataCentre Data Dependency, according to the data location. Furthermore, a network performance based replica selection strategy is proposed to avoid potential network overloading problems and to increase the number of concurrent-running instances at the same time

    Graph-cut based coscheduling strategy towards efficient execution of scientific workflows in collaborative cloud environments

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    Recently, cloud computing has emerged as a promising computing infrastructure for performing scientific workflows by providing on-demand resources. Meanwhile, it is convenient for scientific collaboration since different cloud environments used by the researchers are connected through Internet. However, the significant latency arising from frequent access to large datasets and the corresponding data movements across geo-distributed data centers has been an obstacle to hinder the efficient execution of data-intensive scientific workflows. In this paper, we propose a novel graph-cut based data and task co scheduling strategy for minimizing the data transfer across geo-distributed data centers. Specifically, a dependency graph is firstly constructed from workflow provenance and cut into sub graphs according to the datasets which must appear in fixed data centers by a multiway cut algorithm. Then, the sub graphs might be recursively cut into smaller ones by a minimum cut algorithm referring to data correlation rules until all of them can well fit the capacity constraints of the data centers where the fixed location datasets reside. In this way, the datasets and tasks are distributed into target data centers while the total amount of data transfer between them is minimized. Additionally, a runtime scheduling algorithm is exploited to dynamically adjust the data placement during execution to prevent the data centers from overloading. Simulation results demonstrate that the total volume of data transfer across different data centers can be significantly reduced and the cost of performing scientific workflows on the clouds will be accordingly saved. 漏 2011 IEEE

    Graph-cut based coscheduling strategy towards efficient execution of scientific workflows in collaborative cloud environments

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    Recently, cloud computing has emerged as a promising computing infrastructure for performing scientific workflows by providing on-demand resources. Meanwhile, it is convenient for scientific collaboration since different cloud environments used by the researchers are connected through Internet. However, the significant latency arising from frequent access to large datasets and the corresponding data movements across geo-distributed data centers has been an obstacle to hinder the efficient execution of data-intensive scientific workflows. In this paper, we propose a novel graph-cut based data and task co scheduling strategy for minimizing the data transfer across geo-distributed data centers. Specifically, a dependency graph is firstly constructed from workflow provenance and cut into sub graphs according to the datasets which must appear in fixed data centers by a multiway cut algorithm. Then, the sub graphs might be recursively cut into smaller ones by a minimum cut algorithm referring to data correlation rules until all of them can well fit the capacity constraints of the data centers where the fixed location datasets reside. In this way, the datasets and tasks are distributed into target data centers while the total amount of data transfer between them is minimized. Additionally, a runtime scheduling algorithm is exploited to dynamically adjust the data placement during execution to prevent the data centers from overloading. Simulation results demonstrate that the total volume of data transfer across different data centers can be significantly reduced and the cost of performing scientific workflows on the clouds will be accordingly saved

    Anales del XIII Congreso Argentino de Ciencias de la Computaci贸n (CACIC)

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    Contenido: Arquitecturas de computadoras Sistemas embebidos Arquitecturas orientadas a servicios (SOA) Redes de comunicaciones Redes heterog茅neas Redes de Avanzada Redes inal谩mbricas Redes m贸viles Redes activas Administraci贸n y monitoreo de redes y servicios Calidad de Servicio (QoS, SLAs) Seguridad inform谩tica y autenticaci贸n, privacidad Infraestructura para firma digital y certificados digitales An谩lisis y detecci贸n de vulnerabilidades Sistemas operativos Sistemas P2P Middleware Infraestructura para grid Servicios de integraci贸n (Web Services o .Net)Red de Universidades con Carreras en Inform谩tica (RedUNCI

    Anales del XIII Congreso Argentino de Ciencias de la Computaci贸n (CACIC)

    Get PDF
    Contenido: Arquitecturas de computadoras Sistemas embebidos Arquitecturas orientadas a servicios (SOA) Redes de comunicaciones Redes heterog茅neas Redes de Avanzada Redes inal谩mbricas Redes m贸viles Redes activas Administraci贸n y monitoreo de redes y servicios Calidad de Servicio (QoS, SLAs) Seguridad inform谩tica y autenticaci贸n, privacidad Infraestructura para firma digital y certificados digitales An谩lisis y detecci贸n de vulnerabilidades Sistemas operativos Sistemas P2P Middleware Infraestructura para grid Servicios de integraci贸n (Web Services o .Net)Red de Universidades con Carreras en Inform谩tica (RedUNCI
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