Composable architecture for rack scale big data computing

نویسندگان

  • Chung-Sheng Li
  • Hubertus Franke
  • Colin Parris
  • Bülent Abali
  • Mukil Kesavan
  • Victor I. Chang
چکیده

The rapid growth of cloud computing, both in terms of the spectrum and volume of cloud workloads, necessitate 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 demonstrated the feasibility of composable systems through building a rack scale composable system prototype using PCIe switch. Through empirical approaches, we develop 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 developed the implications for the network and resource provisioning and management for rack scale architecture.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

An Architecture for Security and Protection of Big Data

The issue of online privacy and security is a challenging subject, as it concerns the privacy of data that are increasingly more accessible via the internet. In other words, people who intend to access the private information of other users can do so more efficiently over the internet. This study is an attempt to address the privacy issue of distributed big data in the context of cloud computin...

متن کامل

HDM: A Composable Framework for Big Data Processing

Over the past years, frameworks such as MapReduce and Spark have been introduced to ease the task of developing big data programs and applications. However, the jobs in these frameworks are roughly defined and packaged as executable jars without any functionality being exposed or described. This means that deployed jobs are not natively composable and reusable for subsequent development. Beside...

متن کامل

Cloud Computing Technology Algorithms Capabilities in Managing and Processing Big Data in Business Organizations: MapReduce, Hadoop, Parallel Programming

The objective of this study is to verify the importance of the capabilities of cloud computing services in managing and analyzing big data in business organizations because the rapid development in the use of information technology in general and network technology in particular, has led to the trend of many organizations to make their applications available for use via electronic platforms hos...

متن کامل

PETSc’s Software Strategy for the Design Space of Composable Extreme-Scale Solvers

Emerging extreme-scale architectures present new opportunities for broader scope of simulations as well as new challenges in algorithms and software to exploit unprecedented levels of parallelism. Composable, hierarchical solver algorithms and carefully designed portable software are crucial to the success of extremescale simulations, because solver phases often dominate overall simulation time...

متن کامل

Scaling Beyond One Rack and Sizing of Hadoop Platform

This paper focuses on two aspects of configuration choices of the Hadoop platform. Firstly we are looking to establish performance implications of expanding an existing Hadoop cluster beyond a single rack. In the second part of the testing we are focusing on performance differences when deploying clusters of different sizes. The study also examines constraints of the disk latency found on the t...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:
  • Future Generation Comp. Syst.

دوره 67  شماره 

صفحات  -

تاریخ انتشار 2017