000202110 001__ 202110
000202110 005__ 20190317000017.0
000202110 020__ $$a978-1-4503-2714-5
000202110 0247_ $$2doi$$a10.1145/2592784.2592789
000202110 037__ $$aCONF
000202110 245__ $$aScale-up Graph Processing in the Cloud: Challenges and Solutions
000202110 269__ $$a2014
000202110 260__ $$bACM New York, NY, USA ©2014$$c2014
000202110 336__ $$aConference Papers
000202110 520__ $$aProcessing large graphs is an important part of the big-data problem. Recently a number of scale-up systems such as X-Stream, Graphchi and Turbograph have been proposed for processing large graphs using secondary storage on a single machine. The design and evaluation of these systems however have focused on physical machines. We expect that a natural evolution of such systems is to the cloud where a virtual machine would run the graph processing algorithm and access the graph from secondary storage remotely connected through the network. We evaluate a state of the art graph processing system called X-Stream in EC2 to identify challenges in this space. Our primary finding is that the network bandwidth between a virtual machine and remote storage becomes the limiter for performance. We show that this bottleneck can be somewhat alleviated through the use of VM local instance storage, network provisioning and compression.
000202110 6531_ $$ax-stream
000202110 6531_ $$alarge scale graph processing
000202110 6531_ $$acloud computing
000202110 6531_ $$astorage
000202110 6531_ $$acompression
000202110 700__ $$0248041$$g233074$$aMalicevic, Jasmina
000202110 700__ $$0246803$$g226716$$aRoy, Amitabha
000202110 700__ $$0243160$$g155705$$aZwaenepoel, Willy
000202110 7112_ $$dApril 13-16,2014$$cAmsterdam, Netherlands$$aCloudDP’14: Fourth International Workshop on Cloud Data and Platforms
000202110 773__ $$tProceedings of the Fourth International Workshop on Cloud Data and Platforms
000202110 8564_ $$uhttps://infoscience.epfl.ch/record/202110/files/Scale_up%20graph%20processing%20in%20the%20cloud_updated.pdf$$zn/a$$s585421$$yn/a
000202110 909C0 $$xU10700$$0252226$$pLABOS
000202110 909CO $$ooai:infoscience.tind.io:202110$$qGLOBAL_SET$$pconf$$pIC
000202110 917Z8 $$x233074
000202110 937__ $$aEPFL-CONF-202110
000202110 973__ $$rNON-REVIEWED$$sPUBLISHED$$aEPFL
000202110 980__ $$aCONF