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