3.3 Results
4.2.4 Model initialization ‐ Yasso07
Em seguida, descrevemos alguns trabalhos futuros que consideramos importantes: • Tolerância a falhas: característica útil em sistemas distribuídos que processam dados.
No caso do emprego de múltiplos clusters em um sistema de computação paralela, o requisito de tolerância a falhas é especialmente importante porque os pontos de falha podem ser significativos. Atualmente existem trabalhos no campo de linhagem de dados (data lineage), utilizadas no Spark. No caso da HPC Shelf, esse tipo de técnica pode atuar no salvamento e recuperação de informações sobre a execução de workflows, bem como de componentes de solução. Por exemplo, no caso do Gust, isso seria especialmente aplicado
aos dados do DATACONTAINERe chaves/valores do MapReduce;
• Ambiente de interatividade com o usuário: útil para a análise interativa das informações, permitindo ao usuário realizar consultas a dados do grafo, bem como executar algoritmos de forma interativa. Para isso, tem-se considerado o ambiente de avaliação de códigos C# do Mono, ou REPL (Read-eval-print loop), no qual é possível ao usuário entrar em um terminal de comandos e inserir códigos C# linha por linha, obtendo respostas em tempo de execução. De fato, os componentes implantados já ficam acessíveis por padrão em um ambiente REPL. Porém, seu comportamento interativo ainda não foi explorado e validado, especialmente em ambiente distribuído;
• Particionamento: atualmente Gust e MapReduce possuem técnicas aleatórias para parti- cionar e balancear carga de dados e processamento. Um estudo nesse contexto poderia envolver a criação de novos algoritmos, bem como a avaliação de algoritmos e heurísticas já existentes, assim como foi feito com o Metis neste trabalho. Além disso, um particio- namento dinâmico poderia ser incluído em cada iteração MapReduce, avaliando passo a passo onde se localizam gargalos, para tomada de decisão sobre reparticionamento.
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