Charging Models for Data Centers

1. Charging Models for Data Centers Bhuvan Urgaonkar The Penn State University

2. Data Centers • Clusters of compute and storage servers connected by high-speed nets • Resources made available to applications • Charge the applications for these resources • Applications might have clients that they charge

3. Charging in a Data Center • Between data center & application provider • Lease out fixed # servers (over-provision) • Fixed monthly rate (e.g., yahoo Web hosting) • Performance-based charging (mostly research prototypes) • Usage-based (e.g., Sun Grid) • Between application provider & clients • Fixed monthly rate (possibly with multiple classes) • Transaction granularity (roughly same as usage-based)

4. Classification of Charging Models • Flat-rate • Usage-based • Flat-rate + Usage-based • Performance-based • Bidding-based

5. Flat-rate Charging • Local phone service, cable connection • (+) Billing can not get easier • (-) Consumer: Why should I pay even when I was on vacation? • (-) Provider: Could I have improved my revenue by charging based on usage?

6. Usage-based Charging • Electricity • Actually rate fluctuates! • Service interrupted deliberately sometimes • Long-distance phone • Sun Grid: $1/CPU-hour • There should be a way for the consumer to verify its usage • E.g., Electricity meters at our homes

7. Flat-rate + Usage-based • Cell phones • 400 day-time minutes for free • Usage-based beyond that

8. Performance-based Charging • Service providers like AT&T, Sprint guarantee average delays in the backbone • For data centers • Difficult for the data center to translate given performance into resources • Workloads vary, applications are complex • Desirable by application provider • Caveat: How do I know what response time my clients are experiencing?

9. Bidding-based Charging • eBay • Clients bid till a pre-decided time • Highest bidder gets to buy • Winning bidder can not back down • Open-bid: You see what others are doing • Closed-bid: You don’t see what others are doing

10. Bidding-based Charging • (+) Provider: This seems to maximize revenue • (-) Provider: Has to provide bidding mechanism • Scalability may be a problem • (-) Consumer: I have no guarantees; some rich guy can always shoot me down! • (-) Consumer: Outcome known only at the end of the bid • Have to wait till then to make any decisions

11. Possible Factors Governing the Choice of Charging Model • Ease of monitoring and accounting • Abundance of resource • Competition for the resource • Ease of verifying/proving resource usage • Dependencies between various resources being bought (bidding) • Different levels of desirability of the resource among the consumers

12. Interlude: Differentiated Service • When does it make sense to have multiple classes? • What decides the priority scheme/scheduling discipline?

13. Two Aspects of the Charging Problem • Charging Model • Economics Problem • Accounting and Verification Mechanism • Systems Problem

14. Two Aspects of the Charging Problem • Charging Model • Economics Problem • Accounting and Verification Mechanism • Systems Problem

15. Charging in a Data Center • Which model is suitable? • Apps are interested in performance metrics • Data center would prefer usage-based charging • What about bidding for resources? • What does the choice of model depend on? • Abundance, competition, peace of mind? • Got to be revenue maximization, right? • How to charge for the usage of multiple resources? • CPU, disk, network, …

16. Two Aspects of the Charging Problem • Charging Model • Economics Problem • Accounting and Verification Mechanism • Systems Problem

17. Two Aspects of theCharging Problem • Charging Model • Economics Problem • Accounting and Verification Mechanism • Systems Problem

18. Two Systems Requirements for Enabling Charging • Accounting • Resource provider should be able to monitor and account resource usage • Verification • Resource consumer should be able to verify its own resource usage • Ability to dispute provider’s claims

19. Accounting • Well studied by OS and networks communities • Resource containers from Rice University • Mostly an engineering exercise • Does the problem become any harder in a virtualized hosting environment?

20. Verification • Remember: App doesn’t trust the data center • Auditing: Instead of verifying resource usage at all times, the consumer does it sometimes • The provider should not be able to predict or detect an audit • Audit at random • Provider and consumer should agree to the auditing process • Involve a third party that both trust • The data center also doesn’t trust the application! • Failing an audit is a violation of SLA

21. Auditing in a Data Center:Exhaustive Profiling • The auditor uses extensive profiling to identify resource usage to performance mapping for all possible workloads • (+) The data center can not figure out it is being audited • (-) Such profiling might be prohibitively expensive

22. Auditing in a Data Center:Selective Profiling • The auditor sends well-profiled probes and observes their performance • (+) No need for extensive/exhaustive profiling • (-) Data center might identify probes • Camouflage needed • (-) Not trivial to construct probes whose performance is independent of the rest of the workload

23. Auditing in a Data Center:Self-Monitoring Applications • Assume it is possible to modify the application • Can the application monitor its own resource usage? • Can not trust the underlying OS/VMM

24. Self-Monitoring Application • Idea: We add a special auditing code (AC) to the application • … for (i=0; i < 1000000; i++); … • At a randomly chose time t1, the application sends a message to the auditor • The application jumps to AC and starts executing it • The auditor ACKs the message • The application receives the ACK at time t2 and determines t2-t1, compares it with expected time to reach the current value of i

25. Problems with Self-Monitoring Applications • The execution time of AC depends on what other apps are doing • Not a problem: data center expected to guarantee lower bounds • Unpredictable delays in the Internet • Send multiple probes and take average • The auditor could record probe reception times and try to adjust for network delays

26. Problems with Self-Monitoring Applications • How to ensure data center can not identify a msg to auditor or the execution of AC? • Msg to auditor and ACK should look like normal requests and responses • Giveaway: Data center observes that the application has become CPU-intensive suddenly • Not a problem if the app becomes CPU-intensive when serving its normal workload • Need to ensure that the CPU usage during the execution of AC is indistinguishable from that when serving normal workload • E.g., Running a while loop that lasts 30 min would be a bad idea

27. Design Issues: Self-Monitoring Applications • What is the right observation period? How many observations should be made? • What about other resources? • Network bandwidth perhaps similar to CPU • Memory and disk bandwidth much harder!

28. Summary • Charging in data centers seems like an important problem to address • We can break-down the charging problem into • Charging model: Economics problem • Accounting and verification: Systems problems • Many interesting open issues!