1. Data-Intensive Cloud Control for GENI GEC 8 demo Orca control framework July 20, 2010 Michael Zink, Prashant Shenoy, Jim Kurose, David Irwin and Emmanuel Cecchet cecchet@cs.umass.edu

2. 2 DiCloud Project Overview  Support researchers conducting data-intensive experiments Sensors  network  storage volumes  processing  Extend Orca with Data-centric Slices: storage as a first-class resource with Amazon Simple Storage Service (S3) and Elastic Block Storage (EBS) Cloud Computing: connect Amazon Elastic Compute Cloud (EC2) resources to GENI

3. 3 Demo – Step 1 (Allocate resources) ViSE Sensor Network S3 (storage) EC2 servers Researcher creates a slice with: weather radar storage space on S3 compute servers on EC2 Orca DiCloud

4. 4 Demo – Step 2 (Storing data) ViSE Sensor Network S3 (storage) Weather radar data is stored in S3 DiCloud S3 Proxy

5. 5 Demo – Step 3 (Processing data) S3 (storage) EC2 servers Get radar data from S3 Process data on EC2 server Put generated image in S3 DiCloud AWS accounting 0010101 0110101 1100110 0101000 1110000

6. 6 Demo – Step 4 (Visualizing data) S3 (storage) DiCloud S3 Proxy Get generated image in S3

7. 7 Technical details  Implemented on top of Amazon Web Service API  DiCloud monitors EC2 Server hourly usage Network usage (in and out traffic using CloudWatch statistics) S3 storage space and put/get operations using a dedicated proxy EBS disk usage (storage space and number of IOs using CloudWatch statistics)  All accounting operations are logged in a database  Resources are automatically revoked when budget has expired

8. 8 Challenges  Network connectivity with AWS  Monitoring CloudWatch does not differentiate between free and paying network traffic or disk IO S3 Proxy in the cloud would save network traffic and cost  What security model for storage How to share data with others? Storage leases can last years  What budget to allocate to make this useful?