دانلود مقاله ISI انگلیسی شماره 149713
ترجمه فارسی عنوان مقاله

معماری کامپوزیتی برای مقیاس داده بزرگ محاسبات رک

عنوان انگلیسی
Composable architecture for rack scale big data computing
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
149713 2017 14 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Future Generation Computer Systems, Volume 67, February 2017, Pages 180-193

ترجمه کلمات کلیدی
سیستم های اطلاعاتی بزرگ، معماری سیستم کامپوزیت، معماری دیتا سنتر تجزیه نشده، مرکز داده کامپوزیت محیط نرم افزار تعریف شده توسط نرم افزار، نرم افزار تعریف شبکه،
کلمات کلیدی انگلیسی
Big data platforms; Composable system architecture; Disaggregated datacenter architecture; Composable datacenter; Software defined environments; Software defined networking;
پیش نمایش مقاله
پیش نمایش مقاله  معماری کامپوزیتی برای مقیاس داده بزرگ محاسبات رک

چکیده انگلیسی

The rapid growth of cloud computing, both in terms of the spectrum and volume of cloud workloads, necessitates re-visiting the traditional rack-mountable servers based datacenter design. Next generation datacenters need to offer enhanced support for: (i) fast changing system configuration requirements due to workload constraints, (ii) timely adoption of emerging hardware technologies, and (iii) maximal sharing of systems and subsystems in order to lower costs. Disaggregated datacenters, constructed as a collection of individual resources such as CPU, memory, disks etc., and composed into workload execution units on demand, are an interesting new trend that can address the above challenges. In this paper, we demonstrate the feasibility of composable systems through building a rack scale composable system prototype using PCIe switches. Through empirical approaches, we develop an assessment of the opportunities and challenges for leveraging the composable architecture for rack scale cloud datacenters with a focus on big data and NoSQL workloads. In particular, we compare and contrast the programming models that can be used to access the composable resources, and develop the implications for the network and resource provisioning and management for rack scale architecture.