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Reciprocal Resource Fairness: Towards Cooperative Multiple-Resource Fair Sharing in IaaS Clouds
School of Computer Engineering
Nanyang Technological University, Singapore
Haikun Liu and Bingsheng He
Current IaaS Cloud Model (T-shirt)
VM instance
CPU (EC2 comp. unit)
Memory (GB)
Storage (GB)
Price ($/hour)
Small 1 1.7 160 0.06
Medium 2 3.75 410 0.12
Large 4 7.5 850 0.24
Ext Large 8 15 1690 0.48
• Popular Cloud providers sell VM instances with fixed capacity (T-shirt).
• Charge users based on resources used over time (Pay-as-you-use).
• Horizontal resource scaling (Scale-out).
Disadvantages of T-shirt Model
• Tenants’ resource demands are
heterogeneous (NSDI’11). – Tenants have different resource demands. – A tenant’s demand is changing over time.
• Resource utilization is a critical problem in such pay-as-you-use environments. – Cloud providers waste resource.
higher operating cost and less revenue.– Cloud tenants waste their money.
Static resource allocation (T-shirt Model) causes resource underutilization or bad application performance.
To Share or Not To Share?
• Resource Utilization = $• Resource Sharing can improve resource efficiency.
– Allow underloaded tenants to release resources to other tenants. – Allow overloaded tenants to temporarily use more resources
(from others).
• Virtualization technologies already provide enough technical supports for resource sharing.– CPU, I/O multiplexing (time-sharing)– Memory Overcommit (ballooning, hotplugging)
A New Resource Alloc. Model
• Time-sharing Model– Compatible with current cloud
interface (static billing)– Allow dynamic resource scaling for
VMs in a fine-grained manner
• Challenges: fairness – Free-riding– Lying– Economic fairness
Tenants
Resource pool
If the fairness problem is not solved, tenants should not have incentive to share resource.
Economical Fairness: Resource-as-you-pay • The total value of resources the tenant received should
be proportional to her payment. • This is a Service-Level Agreement (SLA).
$ 50
$ 50$ 50
A:
B:
MemCPU
A: 50% B: 50%B: 50%
MemCPU
Value of ResourcePayment
Existing Fair Policies
• State-of-the-art: – Weighted Max-Min Fairness (WMMF): always select the user
with the minimum demand/share ratio every time.– Dominant Resource Fairness (DRF): always maximize the
smallest dominant share of users in a system (NSDI’11).
• Disadvantages of resource allocation for multiple resource types:– Free-riding– Lying– Economical fairness
Problems of T-shirt, WMMF, DRF
VMs VM1 VM2 VM3 Total
Initial Shares <500, 500> <500, 500> <1000, 1000> <2000, 2000>
Demands <6 GHz, 3 GB> <8 GHz, 1 GB> <8 GHz, 8 GB> <22 GHz, 12GB>
T-shirt Allocation
<5 GHz, 2.5GB> <5 GHz, 2.5GB> <10 GHz, 5 GB> actually used <18 GHz, 8.5GB>
WMMF Allocation
<6 GHz, 3 GB> <6 GHz, 1 GB> <8 GHz, 6 GB> <20 GHz, 10 GB>
WDRF dominant share
6/20 = 3/10 8/20 CPU 8/(10*2) RAM 100%
WDRF Allocation
<6 GHz, 3 GB>
<7 GHz, 1 GB> <7 GHz, 6 GB> <20 GHz, 10 GB>
• Example: Three VMs share total 20 GHz CPU and 10 GB RAM.
Unused <0 GHz, 0 GB> <0GHz, 1.5GB> <2 GHz, 0 GB>
• Challenges: can we find a fair sharing policy that satisfies the following properties? – Sharing Incentive– Gain-as-you-contribute Fairness – Strategy-proofness
• Solution: Reciprocal Resource Fairness– The basic idea is to allow flexible resource allocation
to VMs while keeping the total resource value unchanged.
Our Work: RRF
Reciprocal Resource Fairness (RRF)• Hierarchical and complementary mechanisms:
– Inter-tenant Resource Trading (IRT)– Intra-tenant Weight Adjustment (IWA)
PM
VM1
TenantA
TenantM
RT
VMnWA VM1
VMn
WA
…
Resource Alloc. Model
• Normalize different types of resources based on their market price.– A tenant’s asset is the aggregate shares of all resource types.
• Resource allocation model:– Payment Shares Resources– A VM’s resource share reflects its priority relative to other VMs.– Resource allocation is determined by shares, tenant’s payment.
Inter-tenant Resource Trading
• Tenant’s gain from other tenants should be proportional to her contribution.
MemCPUMem
CPU200
MemCPU
100
MemCPU
300
contributions
A B C D
Inter-tenant Resource Trading• Tenant’s gain from other tenants should be proportional
to her contribution.
MemCPUMem
CPU
200
MemCPU
100
MemCPU
200
100
Contribution of Memory : A:B= 200:100
Gain of CPU: A:B= 200: 100
A B C D
Discussion
• Comparison of WMMF, DRF, RRF
Proof sketches are in the paper.
Evaluation• Testbed:
– implemented RRF on Xen 4 and deploy the prototype in a cluster with 10 nodes.
• Benchmark: – TPC-C, RUBBoS, Kernel-build, Hadoop
• Workloads:– Stable, cyclical on-off, heavy
and light – Application are running in one or
more VMs. • Methodology:
– T-shirt, WMMF, DRF
The ratio of total resource demandto total initial share
Economic Fairness
• RRF can guarantee 95% economic fairness for multi-resource sharing among multi-tenants.
Application Performance
• RRF delivers 45% app performance improvement to tenants compared to T-shirt model.
VM density vs. App Performance
• RRF improves VM density than T-shirt model by 2.2X at the
expense of around 15% performance penalty.
Performance Overhead
• RRF causes reasonable CPU load on the host machine;• RRF causes negligible performance overhead on guest VMs.
Conclusion
• A new resource sharing model for IaaS clouds.
• Reciprocal resource fairness with two complementary mechanisms: inter-tenant resource trading and intra-tenant weight adjustment.
• RRF can guarantee economical fairness, and Improve resource efficiency and application performance.
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