AEGLE's Cloud Infrastructure for Resource Monitoring and Containerized Accelerated Analytics

Konstantina Koliogeorgi, Dimosthenis Masouros, Georgios Zervakis, S. Xydis, Tobias Becker, G. Gaydadjiev, D. Soudris
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引用次数: 6

Abstract

This paper presents the cloud infrastructure of the AEGLE project, that targets to integrate cloud technologies together with heterogeneous reconfigurable computing in large scale healthcare systems for Big Bio-Data analytics. AEGLEs engineering concept brings together the hot big-data engines with emerging acceleration technologies, putting the basis for personalized and integrated health-care services, while also promoting related research activities. We introduce the design of AEGLE’s accelerated infrastructure along with the corresponding software and hardware acceleration stacks to support various big data analytics workloads showing that through effective resource containerization AEGLE’s cloud infrastructure is able to support high heterogeneity regarding to storage types, execution engines, utilized tools and execution platforms. Special care is given to the integration of high performance accelerators within the overall software stack of AEGLE’s infrastructure, which enable efficient execution of analytics, up to 140× according to our preliminary evaluations, over pure software executions.
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AEGLE的用于资源监控和容器化加速分析的云基础设施
本文介绍了AEGLE项目的云基础设施,其目标是将云技术与大型医疗保健系统中的异构可重构计算集成在一起,用于大生物数据分析。AEGLEs工程概念将热门的大数据引擎与新兴的加速技术结合在一起,为个性化和综合医疗保健服务奠定基础,同时也促进了相关的研究活动。我们介绍了AEGLE的加速基础设施设计以及相应的软件和硬件加速堆栈,以支持各种大数据分析工作负载,表明通过有效的资源容器化,AEGLE的云基础设施能够支持存储类型、执行引擎、使用的工具和执行平台的高度异构。特别注意的是,在AEGLE基础架构的整个软件堆栈中集成了高性能加速器,根据我们的初步评估,它可以有效地执行分析,比纯软件执行高140倍。
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