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 Medium Large Ext Large 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 A: B: Mem CPU A: 50% B: 50% Mem CPU Value of Resource Payment

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 VMsVM1VM2VM3Total Initial Shares Demands T-shirt Allocation actually used WMMF Allocation WDRF dominant share 6/20 = 3/108/20 CPU8/(10*2) RAM100% WDRF Allocation Example: Three VMs share total 20 GHz CPU and 10 GB RAM. Unused

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 VM 1 Tenant A Tenant M RT VM n WA VM 1 VM n 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. MemCPU Mem CPU 200 Mem CPU 100 Mem CPU 300 contributions A B C D

Inter-tenant Resource Trading Tenant’s gain from other tenants should be proportional to her contribution. MemCPU Mem CPU 200 Mem CPU 100 Mem CPU 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 demand to 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.