Falloc: Fair Network Bandwidth Allocation in IaaS Datacenters via a Bargaining Game Approach

Transcription

1 Falloc: Fair Network Bandwidth Allocation in IaaS Datacenters via a Bargaining Game Approach Fangming Liu 1,2 In collaboration with Jian Guo 1,2, Haowen Tang 1,2, Yingnan Lian 1,2, Hai Jin 2 and John C.S. Lui 3 1 "Cloud Datacenter & Green Computing" research group 2 Huazhong University of Science and Technology, Wuhan, China 3 The Chinese University of Hong Kong October 8, IEEE ICNP, Gottingen, Germany

2 Outline Motivation Fairness is important in datacenter networks Problem Idea How to achieve flexible fairness on bandwidth sharing Cooperation among VMs for flexible bandwidth allocation via a Bargaining Game Solution Distributed cooperative algorithm Evaluations Efficiency, fairness and utilization 2

3 Motivation Why fairness is important in IaaS datacenter networks (Intra-DCNs)?

4 IaaS Clouds Hosting Increasingly More Apps Datacenters for IaaS cloud services 36% growth $20 billion 4

5 Today s IaaS cloud IaaS DCN: Challenges & Opportunities Shared & Multiplexed across many tenants Pay-per-usage charging model via different types of virtual machines (VMs) Only true for: CPU, memory, storage However Intra-DC network resources shared in best effort manner based on traditional protocols, e.g., TCP Bandwidth is not fairly shared based on payment Unpredictable/varying performance, e.g., job finish times Lack of performance isolation/performance guarantee for VMs NO charge on quantified intra-dcn bandwidth Remind that Providers do charge you for CPU, Memory, Storage Virtualization became mature except for Networking. 5

6 Issue I: Intra-DC Network is not fairly shared Global view Different tenants sharing the same underlying intra-dcn A is more aggressive (UDP, more TCP links) B is more important (commercial transaction) Tenants A Tenants B Total throughput A will get more bandwidth Payment 6

7 Issue I: VM-level Fairness in Intra-DCN In details VMs are sharing congested links Relying on TCP s congestion control flow-level fairness Applications are running in VMs The network allocation depends on: 1) VMs running on the same machine, 2) cross-traffic on each link used by the VM Fairness among users Transport layer fairness VM-level fairness VMs Congested links The congested link is shared based on the number of TCP-flows 7

8 Issue II: Bandwidth Guarantee An existing approach Allocate VMs in the topos VMs Reserve bandwidth for virtual clusters Server Switch Bandwidth guarantee 8

9 Issue III: Utilization An example of cloud service in DC: VM0: demand of 10Mbps 500Mbps 1Gbps Rooter VM1: demand of 1Gbps Virtual Switch 500Mbps Low utilization The networking demands of cloud applications are time-varying Low network utilization if statically reserved 9

10 A Large Design Space for 3-way Tradeoffs Cloud Providers & Tenants are mutually interested!!! Utilization Provider Minimum guarantee Fair bandwidth share User Predicable performance 10

11 Problem How to achieve flexible fairness on bandwidth sharing for balancing such tradeoffs

12 Fairness requirements What do cloud users want? Paying for a fixed bandwidth A priority stands for the ratio of shared bandwidth What do cloud providers want? High utilization Meeting SLA Problem formulation: how to express these goals? 12

13 Requirements 1 Guarantee base bandwidth Base bandwidth: B Bandwidth demand: D t1 t2 A base bandwidth User: pay for a base bandwidth How to guarantee D<B: allocate enough bandwidth to satisfy the demand D>B: limit the upper bound to maintain fairness among VMs 13

14 Requirement 2 Assign a weight for each VM D - B Base bandwidth: B Bandwidth demand: D Weight Important (expensive) jobs have larger weight How to Share the bandwidth beyond the base bandwidth in proportion to the weight 14

15 Problem How to achieve these two goals, as well as maintaining high utilization? 15

16 Idea & Solution Cooperation among VMs: Guarantee base bandwidth and network proportionality for VMs via a Bargaining Game Approach

17 Ideas Traditional way The bandwidth allocation depends on users applications Selfish: Flow-level fairness/ Unpredictable performance Why not Cloud providers manage the bandwidth allocation Cooperation among VMs Social welfare: fairness for tenants/ performance in SLA/ high utilization How to cooperate in bandwidth allocation for Requirements 1 and 2? Let s make clear the problem. 17

18 Model formulation Resources abstraction Non-blocking core (full bisection bandwidth) VMs located in servers BW 1 BW 2 BW m VM VM Non-blocking Switch VM VM... Server VM VM 18

19 Model formulation We know VM placement matrix: W: [w i,j ] M N VM Demand matrix: D: [d i,j ] N N Server bandwidth: C m Weight and base bandwidth of VMs: VM i < B i, K i > We solve The bandwidth allocation from VM to VM: [r i,j ] N N We apply Rate control on each server 19

20 Problem Characterization Asymmetric Nash Bargaining Solution max (r i,j L i,j ) K i,j Maximize the product of utility gain L i,j r i,j U i,j, i, j ℵ v i m r i I C m, m M Constraints for bound and server capacity v i m r i E C m, m M Why Nash bargaining solution in game? Initial utility <-> minimum guarantee Fairness nation in game <-> fair bandwidth sharing among VMs Pareto optimum <-> utilization 20

21 DCN: An Ideal Network Environment to be viewed as a Harmonious Society Poor VMs: base bandwidth > bandwidth demand (Bi>Di) Rich competitor: base bandwidth bandwidth demand (Bi Di) Poor VMs Wealth flow Rich VMs End Server Harmonious society Server Switch Fairness: 1) Minimum guarantee for the poor 2) Maintain proportionality among the rich Utilization: Social welfare 21

22 Solution Solution Lagrangian relaxation dual problem/ Subgradient method Solution to the dual problem λ m = max (0, λ m ξ(c m r p )) Distributed λ m can be solved by iteration on each server Solution to the primal problem: bandwidth allocation r i,j = L i,j + λ m K i,j +λ l Cooperative r i,j of a link can be solved with λ on two end servers 22

23 Solution Distributed cooperative algorithm Distributed: dual variable λ m, λ l Cooperative: bandwidth allocation r i,j r i,j = L i,j + λ m K i,j +λ l λ m = max (0, λ m ξ(c m r p )) λ l = max (0, λ m ξ(c m r p )) 23

24 Algorithm: Falloc (Fair allocation) How does the algorithm work λ m = max (0, λ m ξ(c m r p )) r i,j = L i,j + λ m K i,j +λ l Remaining bandwidth (C m r p > 0) λ m decrease r i,j increase Reaming bandwidth are allocated Exceeds capacity (C m r p < 0) λ m incrase r i,j decrease Exceeded bandwidth are withdrawn Fully utilized (C m r p = 0) λ m stable r i,j stable 24

25 Evaluations Flexible fairness, utilization and efficiency

26 VMM I/O scheduler Implementation via SDN Implemented with OpenFlow run our proposed bandwidth allocation algorithm in a centralized controller Enforce the allocation result by forwarding packets through specified queues in the switches Mininet Evaluation a SDN platform running real network protocols and workloads the developed code can be moved to a real OpenFlow network without any change VM Switch Priority-based package on Layer 2 Modify VMM network I/O scheduler VM Pkt from VM Pkt from VM P Priority Queue Normal Queue Pkt from VM 26 Priority:1 Priority:2 26

27 Fairness Base bandwidth: 250 Mbps Guarantee bandwidth for H1 and H3 Share the bandwidth beyond the base bandwidth proportionally for H2 and H4 Balance the tradeoff 27

28 Utilization Under MapReduce Workloads (e.g., Hadoop Word Count, Sort, Join) Falloc s utilization is 18.8% higher than that of reservation method, nearly as high as best effort manner 28

29 Algorithm efficiency Convergence speed under Falloc Small step size: slow 29

30 Summary Falloc An application-layer bandwidth allocation protocol using cooperation for bandwidth allocation in multiplexed IaaS datacenters via Bargaining Game Not only provide flexible fairness for VMs by balancing the tradeoff between bandwidth guarantee and proportional bandwidth share, but also maintain high network utilization Towards mutual benefits for both cloud providers and tenants Performance guarantee, fairness and high-utilization under multiplexed IaaS datacenter networks 30

31 Q&A Your suggestion is appreciated! Thank you! Prof. Fangming Liu More details: