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SpotCheck: Designing a Derivative IaaSCloud on the Spot Market
Prateek Sharma, Stephen Lee, Tian Guo,David Irwin, Prashant Shenoy
EuroSys 2015
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Motivation System Design Evaluation Related Work Conclusion
Infrastructure Clouds
Cloud computing is popular and offers many benefits:I Ease of deployment, scalability, pay-as-you-goI Infrastructure, Platform, Software as a ServiceI Infrastructure clouds offer computing and storage resources
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Motivation System Design Evaluation Related Work Conclusion
Cost vs. Availability TradeoffsCloud servers have different cost vs. availability tradeoffs:On-demand servers:
I Fixed price per unit timeI Non-revocable
Spot servers:I Variable prices based on market conditionsI Revocable =⇒ lower availabilityI Surplus capacity sold at lower price
m3.medium m3.large0.00
0.05
0.10
0.15
0.20
Pri
ce (
$/h
r)
On-demand Spot (average)
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Motivation System Design Evaluation Related Work Conclusion
Cost vs. Availability TradeoffsCloud servers have different cost vs. availability tradeoffs:On-demand servers:
I Fixed price per unit timeI Non-revocable
Spot servers:I Variable prices based on market conditionsI Revocable =⇒ lower availabilityI Surplus capacity sold at lower price
m3.medium m3.large0.00
0.05
0.10
0.15
0.20
Pri
ce (
$/h
r)
On-demand Spot (average)
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Motivation System Design Evaluation Related Work Conclusion
Exploiting Spot Servers
Spot servers ideal for batch jobsI Batch jobs are disruption tolerantI Checkpoint-Restart to tolerate spot revocations
Spot servers for interactive applications?I Revocable =⇒ Downtime
I Mitigate impact of revocation?I Potentially lower costs
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Motivation System Design Evaluation Related Work Conclusion
Exploiting Spot Servers
Spot servers ideal for batch jobsI Batch jobs are disruption tolerantI Checkpoint-Restart to tolerate spot revocations
Spot servers for interactive applications?I Revocable =⇒ Downtime
I Mitigate impact of revocation?I Potentially lower costs
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Motivation System Design Evaluation Related Work Conclusion
Running Interactive Applications on Spot Servers
Spot
Application
Application
On-demand
ApplicationMigrate
Naive solution:I Spot server revoked =⇒ migrate to on-demandI Revocation =⇒ risk of losing application stateI Users cannot manage complexity & risk of migration
Problem Statement:Design a system to transparently use a mix of spot & on-demandservers to run interactive applications
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Motivation System Design Evaluation Related Work Conclusion
Running Interactive Applications on Spot Servers
Spot
Application
Application
On-demand
ApplicationMigrate
Naive solution:I Spot server revoked =⇒ migrate to on-demand
I Revocation =⇒ risk of losing application stateI Users cannot manage complexity & risk of migration
Problem Statement:Design a system to transparently use a mix of spot & on-demandservers to run interactive applications
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Motivation System Design Evaluation Related Work Conclusion
Running Interactive Applications on Spot Servers
Spot
Application
Application
On-demand
ApplicationMigrate
Naive solution:I Spot server revoked =⇒ migrate to on-demand
I Revocation =⇒ risk of losing application stateI Users cannot manage complexity & risk of migration
Problem Statement:Design a system to transparently use a mix of spot & on-demandservers to run interactive applications
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Motivation System Design Evaluation Related Work Conclusion
Running Interactive Applications on Spot Servers
Spot
Application
Application
On-demand
ApplicationMigrate
Naive solution:I Spot server revoked =⇒ migrate to on-demandI Revocation =⇒ risk of losing application stateI Users cannot manage complexity & risk of migration
Problem Statement:Design a system to transparently use a mix of spot & on-demandservers to run interactive applications
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Motivation System Design Evaluation Related Work Conclusion
Talk Outline
1. Motivation2. System Design3. Evaluation4. Related Work5. Conclusion
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Motivation System Design Evaluation Related Work Conclusion
Derivative CloudI Cloud middleware derived from infrastructure cloudsI Buy servers from IaaS and resell to usersI Use servers of different types =⇒ different SLA from IaaSI Example: spot & on-demand pool to lower costs and increase
availability
Derivative Cloud
Native IaaS Cloud
Customers
Spot PoolVM VM VM
On-demand PoolVM VM VM
Lease Servers
Request Servers
Resell Servers
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Motivation System Design Evaluation Related Work Conclusion
SpotCheckI SpotCheck: Derivative cloud using spot & on-demand serversI Provide low-cost, non-revocable servers to run unmodified
applicationsI Key idea: Run on spot. When revoked, migrate to on-demandI Requirement: Transparently migrate VMs without losing state
SpotCheck
Native IaaS Cloud
Spot PoolVM VM VM
On-demand PoolVM VM VM
Lease Servers
Migrate
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Motivation System Design Evaluation Related Work Conclusion
VM Migration
Key idea:Run on spot. When revoked, migrate to on-demand
Naive approach: Virtual Machine Live MigrationI Migration may not completeI Small termination warning (~2 minutes on EC2)I Incomplete migration =⇒ loss of state
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Motivation System Design Evaluation Related Work Conclusion
Bounded-time VM MigrationI Guaranteed bound on VM migration timeI Independent of memory size, application behaviour
Backup Server
Spot
VM
Continuouslycheckpoint memory
On-demand
VM
Lazily restore memory
I Residual dirty pages sent in bounded time
I Lazy restore: quickly restore VM
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Motivation System Design Evaluation Related Work Conclusion
Bounded-time VM MigrationI Guaranteed bound on VM migration timeI Independent of memory size, application behaviour
Backup Server
Spot
VM
Continuouslycheckpoint memory
On-demand
VM
Lazily restore memory
I Residual dirty pages sent in bounded time
I Lazy restore: quickly restore VM
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Motivation System Design Evaluation Related Work Conclusion
Bounded-time VM MigrationI Guaranteed bound on VM migration timeI Independent of memory size, application behaviour
Backup Server
Spot
VM
Continuouslycheckpoint memory
On-demand
VM
Lazily restore memory
I Residual dirty pages sent in bounded time
I Lazy restore: quickly restore VM10/ 20
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Motivation System Design Evaluation Related Work Conclusion
Nested VirtualizationProblem:
I Bounded-time VM migration needs hypervisor modificationI IaaS clouds don’t allow hypervisor modifications
Solution: Nested VirtualizationI User VMs run on a nested hypervisor (XenBlanket)I Bounded-time migration, VM lazy restore implemented inside
nested hypervisor
IaaS HypervisorCloud server
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Motivation System Design Evaluation Related Work Conclusion
Nested VirtualizationProblem:
I Bounded-time VM migration needs hypervisor modificationI IaaS clouds don’t allow hypervisor modifications
Solution: Nested VirtualizationI User VMs run on a nested hypervisor (XenBlanket)I Bounded-time migration, VM lazy restore implemented inside
nested hypervisor
IaaS HypervisorCloud server
Nested HypervisorUser VM
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Motivation System Design Evaluation Related Work Conclusion
Spreading Revocation Risk
Spot pool
Spot pool
VM 1
VM 2
VM 3
VM 1
VM 2
VM 3
On-demand
VM 1
VM 2
VM 3
Problem: Revocation storms
I Spot pool revoked =⇒ migrate all VMs to on-demand
Solution: Multiple independent poolsI Revocations from one pool dont affect other pools
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Motivation System Design Evaluation Related Work Conclusion
Spreading Revocation Risk
Spot poolSpot pool
VM 1
VM 2
VM 3
VM 1
VM 2
VM 3
On-demand
VM 1
VM 2
VM 3
Problem: Revocation stormsI Spot pool revoked =⇒ migrate all VMs to on-demand
Solution: Multiple independent poolsI Revocations from one pool dont affect other pools
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Motivation System Design Evaluation Related Work Conclusion
Spreading Revocation Risk
Spot pool
Spot pool
VM 1
VM 2
VM 3
VM 1
VM 2
VM 3
On-demand
VM 1
VM 2
VM 3
Problem: Revocation stormsI Spot pool revoked =⇒ migrate all VMs to on-demand
Solution: Multiple independent poolsI Revocations from one pool dont affect other pools
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Motivation System Design Evaluation Related Work Conclusion
Spreading Revocation Risk
Spot pool
Spot pool
VM 1
VM 2
VM 3
VM 1
VM 2
VM 3
On-demand
VM 1
VM 2
VM 3
Problem: Revocation stormsI Spot pool revoked =⇒ migrate all VMs to on-demand
Solution: Multiple independent poolsI Revocations from one pool dont affect other pools
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Motivation System Design Evaluation Related Work Conclusion
Pool Management Policies
I Distribute VMs among multiple poolsI Example: availability zones, server sizes (small, large)I Spot prices in different pools uncorrelatedI Pool distribution policies :
1. Equally2. Weighted by pool cost3. Weighted by pool availability
I Centralized controller dynamically selects pool for new VMs.
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Motivation System Design Evaluation Related Work Conclusion
Evaluation
SpotCheck prototype implemented on Amazon EC2
I Application PerformanceI Cost of SpotCheck VMsI Availability
I Benchmarks: SpecJBB, TPC-WI Experiments on EC2 m3.medium instancesI Spot prices from April to Oct 2014
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Motivation System Design Evaluation Related Work Conclusion
Impact on Application PerformanceSpec-JBB 0.0015%TPC-W 16.7%
Performance degradation due to continuous checkpointing
1 10 20 30 40 50Num. VMs per backup server
02000400060008000
1000012000
Thro
ughp
ut (b
ops)
SpecJBB Throughput
I Backup server can support up to 40 VMsI Each VM shares 1
40th the cost of a backup server
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Motivation System Design Evaluation Related Work Conclusion
Impact on Application PerformanceSpec-JBB 0.0015%TPC-W 16.7%
Performance degradation due to continuous checkpointing
1 10 20 30 40 50Num. VMs per backup server
02000400060008000
1000012000
Thro
ughp
ut (b
ops)
SpecJBB Throughput
I Backup server can support up to 40 VMsI Each VM shares 1
40th the cost of a backup server
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Motivation System Design Evaluation Related Work Conclusion
Cost of Running VMs on SpotCheck
1-Pool 2-Pools 4-Pools Equal Distributed
4-Pools Cost
4-Pools Stability
0.00
0.01
0.02
0.03
0.04
0.05
Avera
ge c
ost
per
hour
($) Live migration Bounded time migration
$0.014$0.0086
I Cost with different pool management policies is ~$0.014/hrI Cost saving of 80% compared to on-demand
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Motivation System Design Evaluation Related Work Conclusion
Availability
1-Pool 2-Pools 4-Pools Equal Distributed
4-Pools Cost
4-Pools Stability
0.00
0.05
0.10
0.15
0.20
Unavaila
bili
ty (
%)
SpotCheck with Full restore SpotCheck with Lazy restore
0.002%0.019%
0.16%
0.02%
I Availability of 99.998% relative to on-demand serversI Downtime (~20 seconds) during migrations
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Motivation System Design Evaluation Related Work Conclusion
Related Work
Spot instances: [Javadi et al., 2011, Ben-Yehuda et al., 2011]Derivative Clouds: PiCloud, HerokuNested Virtualization: XenBlanket [Williams et al., 2012],
Turtles [Ben-Yehuda et al., 2010]Bounded time Migration: Yank [Singh et al., 2013],
Remus [Cully et al., 2008]Lazy VM restoration: Post-Copy migration [Hines et al., 2009],
SnowFlock [Lagar-Cavilla et al., 2009]
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Motivation System Design Evaluation Related Work Conclusion
Conclusion
I Different cost vs. availability tradeoffs in Infrastructure cloudsI Spot servers: Cheap but volatileI Derivative Cloud : abstraction layer between user and cloudI SpotCheck : Derivative cloud with spot & on-demand serversI Prototyped on Amazon EC2I Cost savings of 80%, 5 9’s availability
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Motivation System Design Evaluation Related Work Conclusion
END
Thank You
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Spot price spikes
0 50 100 150 200Time
0.0
0.1
0.2
0.3
0.4
0.5
Pri
ce (
$/h
r)
Spot price On-demand
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Spot Prices
0.0 0.2 0.4 0.6 0.8 1.0Spot−price
Ondemand−priceratio
0.0
0.2
0.4
0.6
0.8
1.0Av
aila
bilit
y CD
F
m3.mediumm3.largem3.xlargem3.2xlarge
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Time line
0 10 20 30 40 50
Time
0.00
0.02
0.04
0.06
0.08
0.10
Pri
ce (
$/h
r)
Spot price
bid price
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Price Correlation
1 2 3 4 5 6 7 8 91
01
11
21
31
41
51
61
71
81
9
Zone IDs
123456789
10111213141516171819
Zon
e I
Ds
1.0
0.5
0.0
0.5
1.0
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What if everybody starts using this?
I Public cloud server market is largeI More incentive to expand spot marketsI Bidding is truth revealingI Benefits both users and cloud providers
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Cost of running VMs on SpotCheck
Factors which affect cost:
p :Probability of revocationE(S) :Expected Spot price based on price tracesD :On-demand priceB :Backup server cost = 1 large serverN :# VMs sharing a backup server = 40
Expected Cost of running a VM on SpotCheck:
E(Cost) = (1− p) · E (S) + p · D +BN
Expected Cost = 0.2× On-Demand = $ 0.014 / hour
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Availability
Max. num. of concurrent revocationsN/4 N/2 3N/4 N
1-Pool 0 0 0 1.74 × 10−4
2-Pool 0 3.75 × 10−3 0 2.25 × 10−5
4-Pool 7.4 × 10−3 7.71 × 10−5 1.92 × 10−5 0
Probability of the maximum number of concurrent revocations fordifferent pools. N is the number of VMs.
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Nested Virtualization overhead
I Nested Virtualization overhead is dependent on applicationcharacteristics
I Can use containers.I Migration mechanisms in containers need to be implemented
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Storage & Networking
I Direct access to EBS and S3I Bridged or NAT networking inside nested hypervisorI VPC to isolate user VMs
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Restore overhead
0 1 5 10Num. VMs being concurrently lazily restored
010203040506070
Resp
onse
tim
e (m
s)
TPC-W response time
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Backup Slides
1. Spot pricing, CDF, avail.2. What if everyone starts using this. Market large. More
incentive for IaaS .3. Storage and networking. EBS/S3. Bridge or NAT nw
interfaces.4. Inter cloud operation. exploring practical challenges with
inter-cloud networking5. Nested overhead. Indeed significant for certain workloads. 1)
Implementations will mature. 2) Only use this because IaaSdoesnt expose migration functionality 3) Looking at usingOS-level virtualization and containers.
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References
(2014).Heroku.http://www.heroku.com.
(2014).PiCloud.http://www.multyvac.com.
Ben-Yehuda, M., Day, M., Dubitzky, Z., Factor, M., Har’El, N.,Gordon, A., Liguori, A., Wasserman, O., and Yassour, B. (2010).The Turtles Project: Design and Implementation of NestedVirtualization.In OSDI.
Ben-Yehuda, O., Ben-Yehuda, M., Schuster, A., and Tsafrir, D.(2011).Deconstructing Amazon EC2 Spot Instance Pricing.In CloudCom.
Cully, B., Lefebvre, G., Meyer, D., Feeley, M., Hutchinson, N.,and Warfield, A. (2008).Remus: High Availability via Asynchronous Virtual MachineReplication.In NSDI.
Hines, M. R., Deshpande, U., and Gopalan, K. (2009).Post-copy Live Migration of Virtual Machines.SIGOPS Operating Systems Review, 43(3).
Javadi, B., Thulasiram, R., and Buyya, R. (2011).Statistical Modeling of Spot Instance Prices in Public CloudEnvironments.In UCC.
Lagar-Cavilla, H. A., Whitney, J. A., Scannell, A. M., Patchin, P.,Rumble, S. M., De Lara, E., Brudno, M., and Satyanarayanan, M.(2009).SnowFlock: Rapid Virtual Machine Cloning for Cloud Computing.In EuroSys.
Singh, R., Irwin, D., Shenoy, P., and Ramakrishnan, K. (2013).Yank: Enabling Green Data Centers to Pull the Plug.In NSDI.
Williams, D., Jamjoom, H., and Weatherspoon, H. (2012).The Xen-Blanket: Virtualize Once, Run Everywhere.In EuroSys.
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