{[ promptMessage ]}

Bookmark it

{[ promptMessage ]}

2009.dfs.hotpower - On the Energy(In)efciency of Hadoop...

Info icon This preview shows pages 1–2. Sign up to view the full content.

View Full Document Right Arrow Icon
On the Energy (In)efficiency of Hadoop Clusters Jacob Leverich, Christos Kozyrakis Computer Systems Laboratory Stanford University {leverich, kozyraki}@stanford.edu ABSTRACT Distributed processing frameworks, such as Yahoo!’s Hadoop and Google’s MapReduce, have been successful at harnessing expansive datacenter resources for large-scale data analysis. However, their ef- fect on datacenter energy efficiency has not been scrutinized. More- over, the filesystem component of these frameworks effectively pre- cludes scale-down of clusters deploying these frameworks (i.e. op- erating at reduced capacity). This paper presents our early work on modifying Hadoop to allow scale-down of operational clusters. We find that running Hadoop clusters in fractional configurations can save between 9% and 50% of energy consumption, and that there is a trade- off between performance energy consumption. We also outline further research into the energy-efficiency of these frameworks. 1. INTRODUCTION Energy consumption and cooling are now large com- ponents of the operational cost of datacenters and pose significant limitations in terms of scalability and reli- ability [3]. A growing segment of datacenter work- loads is managed with MapReduce-style frameworks, whether by privately managed instances of Yahoo!’s Hadoop [2], by Amazon’s Elastic MapReduce [12], or ubiquitously at Google by their archetypal implemen- tation [5]. Therefore, it is important to understand the energy efficiency of this emerging workload. The energy efficiency of a cluster can be improved in two ways: by matching the number of active nodes to the current needs of the workload, placing the remain- ing nodes in low-power standby modes; by engineering the compute and storage features of each node to match its workload and avoid energy waste on oversized com- ponents. Unfortunately, MapReduce frameworks have many characteristics that complicate both options. First, MapReduce frameworks implement a dis- tributed data-store comprised of the disks in each node, which enables affordable storage for multi-petabyte datasets with good performance and reliability. Asso- ciating each node with such a large amount of state ren- ders state-of-the-art techniques that manage the number of active nodes, such as VMWare’s VMotion [13], im- practical. Even idle nodes remain powered on to ensure HotPower ’09, Copyright 2009 ACM 0 0.2 0.4 0.6 0.8 1 20 40 60 80 100 120 System Inactivity Distribution Fraction of Runtime Inactivity Duration (s) Multi-job Mix (32GB Scans and Sorts) (a) Distribution of the lengths of system inactivity periods across a cluster during a multi-job batch workload, comprised of several scans and sorts of 32GB of data. A value of .38 at x = 40 seconds means that 38% of the time, a node was idle for 40 seconds or longer.
Image of page 1

Info icon This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
Image of page 2
This is the end of the preview. Sign up to access the rest of the document.

{[ snackBarMessage ]}

What students are saying

  • Left Quote Icon

    As a current student on this bumpy collegiate pathway, I stumbled upon Course Hero, where I can find study resources for nearly all my courses, get online help from tutors 24/7, and even share my old projects, papers, and lecture notes with other students.

    Student Picture

    Kiran Temple University Fox School of Business ‘17, Course Hero Intern

  • Left Quote Icon

    I cannot even describe how much Course Hero helped me this summer. It’s truly become something I can always rely on and help me. In the end, I was not only able to survive summer classes, but I was able to thrive thanks to Course Hero.

    Student Picture

    Dana University of Pennsylvania ‘17, Course Hero Intern

  • Left Quote Icon

    The ability to access any university’s resources through Course Hero proved invaluable in my case. I was behind on Tulane coursework and actually used UCLA’s materials to help me move forward and get everything together on time.

    Student Picture

    Jill Tulane University ‘16, Course Hero Intern