دانلود مقاله انگلیسی رایگان:بستر مقیاس گذاری منابع در زمان واقعی برای بارهای کاری داده های بزرگ در محیطهای بدون سرور - 2020
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دانلود مقاله انگلیسی داده های بزرگ رایگان
  • Real-time resource scaling platform for Big Data workloads on serverless environments Real-time resource scaling platform for Big Data workloads on serverless environments
    Real-time resource scaling platform for Big Data workloads on serverless environments

    سال انتشار:

    2020


    عنوان انگلیسی مقاله:

    Real-time resource scaling platform for Big Data workloads on serverless environments


    ترجمه فارسی عنوان مقاله:

    بستر مقیاس گذاری منابع در زمان واقعی برای بارهای کاری داده های بزرگ در محیطهای بدون سرور


    منبع:

    Sciencedirect - Elsevier - Future Generation Computer Systems, 105 (2020) 361-379: doi:10:1016/j:future:2019:11:037


    نویسنده:

    Jonatan Enes ∗, Roberto R. Expósito, Juan Touriño


    چکیده انگلیسی:

    The serverless execution paradigm is becoming an increasingly popular option when workloads are to be deployed in an abstracted way, more specifically, without specifying any infrastructure requirements. Currently, such workloads are typically comprised of small programs or even a series of single functions used as event triggers or to process a data stream. Other applications that may also fit on a serverless scenario are stateless services that may need to seamlessly scale in terms of resources, such as a web server. Although several commercial serverless services are available (e.g., Amazon Lambda), their use cases are mostly limited to the execution of functions or scripts that can be adapted to predefined templates or specifications. However, current research efforts point out that it is interesting for the serverless paradigm to evolve from single functions and support more flexible infrastructure units such as operating-system-level virtualization in the form of containers. In this paper we present a novel platform to automatically scale container resources in real time, while they are running, and without any need for reboots. This platform is evaluated using Big Data workloads, both batch and streaming, as representative examples of applications that could be initially regarded as unsuitable for the serverless paradigm considering the currently available services. The results show how our serverless platform can improve the CPU utilization by up to 77% with an execution time overhead of only 6%, while remaining scalable when using a 32-container cluster.
    Keywords: Serverless computing | Big Data | Resource scaling | Operating-system-level virtualization | Container cluster


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 19
    حجم فایل: 4228 کیلوبایت

    قیمت: رایگان


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