Charging Models for Data Centers

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Charging Models for Data Centers

• Bhuvan Urgaonkar
• The Penn State University

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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

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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)

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Classification of Charging Models

• Flat-rate
• Usage-based
• Flat-rate Usage-based
• Performance-based
• Bidding-based

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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?

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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

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Flat-rate Usage-based

• Cell phones
• 400 day-time minutes for free
• Usage-based beyond that

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Performance-based Charging

• Service providers like ATT, 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?

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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 dont see what others are doing

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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

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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

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Interlude Differentiated Service

• When does it make sense to have multiple classes?
• What decides the priority scheme/scheduling
  discipline?

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Two Aspects of the Charging Problem

• Charging Model
• Economics Problem
• Accounting and Verification Mechanism
• Systems Problem

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Two Aspects of the Charging Problem

• Charging Model
• Economics Problem
• Accounting and Verification Mechanism
• Systems Problem

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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,

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Two Aspects of the Charging Problem

• Charging Model
• Economics Problem
• Accounting and Verification Mechanism
• Systems Problem

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Two Aspects of theCharging Problem

• Charging Model
• Economics Problem
• Accounting and Verification Mechanism
• Systems Problem

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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 providers claims

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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?

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Verification

• Remember App doesnt 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 doesnt trust the
  application!
• Failing an audit is a violation of SLA

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Auditing in a Data CenterExhaustive 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

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Auditing in a Data CenterSelective 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

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Auditing in a Data CenterSelf-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

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Self-Monitoring Application

• Idea We add a special auditing code (AC) to the
  application
• for (i0 i lt 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

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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

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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

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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!

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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!