Leveraging BlobSeer to boost up the deployment and execution of Hadoop applications in Nimbus cloud environments on Grid 5000

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1 Leveraging BlobSeer to boost up the deployment and execution of Hadoop applications in Nimbus cloud environments on Grid 5000 Alexandra Carpen-Amarie Diana Moise Bogdan Nicolae KerData Team, INRIA

2 Outline 1 Cloud Computing VM management MapReduce applications

3 MapReduce in the Cloud Shared computing and storage resources Easily accessible Pay-per-use model Elastic Reliable MapReduce Parallel programming model for large clusters Processes large amounts of data Provides a clean abstraction for the programmer Communication between nodes Parallelization (scheduling and data distribution) Fault tolerance

4 Global view of the experiment Nimbus

5 Nimbus

6 The BlobSeer data management system BlobSeer Data striping High throughput under concurrency Versioning-based concurrency control

7 BlobSeer deployment Scripts: /home/acarpena/bsscripts Configuration settings: blobseer/env.sh Deploy the system: launchdepl/runblobseer.sh Challenges: Creating dynamic configuration file on multiple sites Gathering results

8 Nimbus

9 The Nimbus cloud environment

10 Nimbus deployment Initial scripts: developed by Pierre Riteau Modifications: Cloud spanning multiple Grid 5000 sites BlobSeer as a backend for Cumulus Automatic de-activation of existing propagation mechanisms/ Replacement with BlobSeer : /nimbus/deploy-nimbus-cloud.rb Challenges: Integrating BlobSeer-related configuration files Networking constraints in Grid 5000

11 Nimbus

12 VM cluster configuration One-click clusters in Nimbus Modifications: Wrapper scripts to automatically configure clusters Deploy a customized image : Connect to the Nimbus client Create a VM cluster: /nimbus/cloud-client-scripts/run-all.sh

13 Nimbus

14 The Hadoop MapReduce framework

15 Nimbus

16 Running MapReduce applications in the cloud Distributed Sort Sort key-value pairs Most used benchmark

17 VM management MapReduce applications VM management challenges Typical scenario: The user uploads a customized VM image to the Cloud repository. The VM image is propagated on many compute nodes. The same VM image is deployed simultaneously all nodes. Limitations of existing approaches: Image propagation delays Huge storage space needed Important network traffic

18 VM management MapReduce applications VM management challenges Typical scenario: The user uploads a customized VM image to the Cloud repository. The VM image is propagated on many compute nodes. The same VM image is deployed simultaneously all nodes. Limitations of existing approaches: Image propagation delays Huge storage space needed Important network traffic

19 VM management MapReduce applications BlobSeer-based efficient VM image management Principles: Optimize VM disk access: on-demand image mirroring Reduce contention by striping the image Evaluation: Experiments performed on Grid storage nodes up to 150 compute 10 nodes 0 Avg. time/instance to boot (s) taktuk pre-propagation qcow2 over PVFS, 256K stripe our approach, 256K chunks Number of concurrent instances

20 VM management MapReduce applications BlobSeer-based cloud data service Features Cumulus: Open source implementation of the Amazon S3 API BlobSeer: Concurrency support, Improved scalability through multiple servers Evaluation: 8 Cumulus servers 10 storage nodes, 5 metadata nodes 1GB file transferred up to 60 concurrent clients Aggregated throughput (MB/s) read write Number of clients

21 VM management MapReduce applications Improving Grid 5000 utilization Evaluation: Measure run time for Grep 12.5 GB of input stored in HDFS Run Hadoop on a no of nodes/vms ranging from 1 to 200 Experimental setup: Grid 5000: 200 physical nodes Job completion time (s) Nodes VMs Number of machines Nimbus: 200 VMs, only 60 physical nodes

22 VM management MapReduce applications Q&A

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