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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