The document discusses exploring cloud computing to reduce costs and improve performance for computing resources. It proposes a hybrid scheduler that would allocate additional machines from Amazon EC2's cloud services when local CPU usage exceeds a threshold, and deallocate them when usage decreases. This could help pay only for resources that are used, improve peak performance, and reduce costs compared to maintaining idle hardware. Key questions to answer include determining accurate cost models and the software to use on virtual machines.

1. Exploring the Cloud Chris Sosa, Dr. Andrew Grimshaw sosa, grimshaw @ cs.virginia.edu University of Virginia

2. Introduction - 1 Ever-increasing demand for computing resources During non-peak times, computing resources sit idle Still paying! Power, cooling, etc Total Cost of Ownership (TCO) is much more than the cost of Hardware Maintenance Administration Cooling Etc. University of Virginia

3. Introduction - 2 Observation – load on main ITC clusters exhibit bimodal distribution Can we only pay for what we use? University of Virginia

4. Enter Cloud Computing (field trip!) What is it? Infrastructure-related capabilities provided as a service Also known as utility-computing and is associated with very basic API’s Lots of industry support Amazon Infrastructure Services: EC2, S3, … Google App Engine Microsoft Azure IBM led initiatives University of Virginia

5. Cloud Computing Paradigms Top-down: Client only provides program and deployment information Microsoft Azure Google App Engine Bottom-up: Raw Infrastructure provided (virtualized hardware) Amazon Nirvanix Flexiscale GoGrid University of Virginia

6. Advantages and Disadvantages of Using the Cloud Advantages Pay for what you use – model is based on how long you use resources. You can allocate and deallocate them on-the-fly Hardware cost, set-up time, maintenance, cooling all go down to zero Can start developing immediately Disadvantages No control over physical resources. Do you trust Amazon? SLA’s may not be good enough. Is 99.95% availability good enough? Some limitations in what you can run. Must stay within the API / framework given University of Virginia

7. Why Cloud Computing Only have to pay for what we use Disadvantages do not affect most users in a batch system University of Virginia

8. Amazon Leading the Push Amazon has been most successful player so far Over 29 billion objects stored on S3 Using over 60% of their resources for Cloud services EC2 just went out of Beta in October (new) … rest of these slides will assume we use Amazon University of Virginia

9. Outline Introduction Overview of Amazon Cloud Services Proposal of Hybrid Scheduler Questions to be Answered Conclusion University of Virginia

10. Amazon S3 Simple Storage for the Internet Applications can interact with various mechanisms REST SOAP Bit Torrent 250 Mb/second network link Objects stored in buckets Buckets have own namespace Up to 100 buckets per account Unlimited objects per bucket 5 GB limit on size of objects Objects are write-once SLA guarantees 99.9% availability University of Virginia

11. S3 Pricing Storage $0.15 per GB-Month of storage used Data Transfer $0.10 per GB - all data transfer in $0.18 per GB - first 10 TB / month data transfer out $0.16 per GB - next 40 TB / month data transfer out $0.13 per GB - data transfer out / month over 50 TB FREE to EC2 Requests $0.01 per 1,000 PUT or LIST requests $0.01 per 10,000 GET and all other requests* * No charge for delete requests University of Virginia

12. Amazon EC2 Provides Virtual Compute Resources Purchase CPU’s on hourly basis Can use provided virtual machine images, or make own Virtual Machines run atop Xen Can do meta data operations with REST, SOAP, command-line tools Instances assigned IP address for SSH, remote desktop, etc SLA guarantees 99.95% availability University of Virginia

13. EC2 Pricing Instances $0.10 / hr - Small Instance - 1.7 GB of memory, 1 EC2 Compute Unit (1 virtual core - 1.7 GHz processor), 160 GB of instance storage, 32-bit platform (can buy in sets of 1, 4, 8) $0.20 / hr - High-CPU Medium Instance 1.7 GB of memory, 5 EC2 Compute Units (2 virtual cores with 2.5 EC2 Compute Units each), 350 GB of instance storage, 32-bit platform (can buy in sets of 1 or 4) Data Transfer $0.10 per GB – data in $0.18 per GB - first 10 TB out FREE to S3 University of Virginia

14. Overview Introduction Overview of Amazon Cloud Services Proposal of Hybrid Scheduler Questions to be Answered Conclusion University of Virginia

15. Main Idea Reduce the number of resources we have active and improve peak performance Modify local scheduler When CPU usage is above threshold, allocate new machines from EC2 and schedule jobs As usage decreases, deallocate resources and return to normal usage University of Virginia

16. Design University of Virginia

17. Research Setup Instead of spending funds on running experiments using EC2 and S3, we will be using Eucalyptus to emulate EC2 Eucalyptus is an open-source implementation of the EC2 interface Requires Xen be installed on host machines (need dedicated machines) Create a centralized repository for data for our tests (S3) NFS share Other possibilities? University of Virginia

18. Task Bar Decide on the software that will be installed on the virtual machines PBS licensing is complicated and expensive Several alternatives such as Genesis II, Hadoop, etc. Create AMI image and register with Eucalyptus Incorporate virtual machines from Eucalyptus into existing scheduler and create mechanism to do this on-the-fly Modify scheduler to take into account a threshold Build stubs to measure how much bandwidth, time, etc. is being used by the scheduler so that we can determine the price we would be charged by Amazon's EC2 and S3 Incorporate these costs, build economic model using actual workloads at UVa, differing thresholds, and various ways of passing jobs to the Cloud University of Virginia

19. Overview Introduction Overview of Amazon Cloud Services Proposal of Hybrid Scheduler Questions to be Answered Conclusion University of Virginia

20. Questions to be Answered What is the Cost Model associated with working with Cloud computing? What costs would be associated with common jobs being run at UVa? What software will we have installed on the Virtual Machines in the Cloud? How can we create a threshold such that we can decide on-the-fly when to start offloading resources to Cloud resources? University of Virginia

21. Overview Introduction Overview of Amazon Cloud Services Proposal of Hybrid Scheduler Questions to be Answered Conclusion University of Virginia

22. Conclusions Important to be concerned about reducing costs as well as getting bigger bang for your buck Offloading job processing to Cloud computing infrastructures can save costs while improving peak throughput University of Virginia

23. Questions? University of Virginia