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

Falloc

: Fair Network Bandwidth Allocation in IaaS

Datacenters via a Bargaining Game Approach

2 _{Huazhong University of Science and Technology, Wuhan, China} 3 _{The Chinese University of Hong Kong}

October 8, 2013 @ IEEE ICNP, Gottingen, Germany

Fangming Liu

In collaboration with Jian Guo 1,2, Haowen Tang1 ,2, Yingnan Lian1,2, Hai Jin 2

and John C.S. Lui 3

Outline

Motivation

• Fairness is important in datacenter networks

Problem

• How to achieve flexible fairness on bandwidth sharing

Idea

• Cooperation among VMs for flexible bandwidth allocation via a Bargaining Game

Solution

• Distributed cooperative algorithm

Evaluations

Motivation

Why fairness is important in IaaS datacenter networks

(Intra-DCNs)?

IaaS Clouds Hosting Increasingly More Apps

Datacenters for IaaS cloud services

36% growth

5

IaaS DCN: Challenges & Opportunities

Today’s IaaS cloud

• 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….

Issue I: Intra-DC Network is not fairly shared

Global view

• Different tenants sharing the same underlying intra-DCN

Tenants A

Tenants B

A is more aggressive (UDP, more TCP links)

B is more important (commercial transaction)

A will get more bandwidth

Total throughput

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

VMs

Congested links

The congested link is shared based on the number of TCP-flows

Transport layer fairness

VM-level fairness

Issue II: Bandwidth Guarantee

An existing approach

Server Switch

VMs

Allocate VMs in the topos

Reserve bandwidth for virtual clusters

9

Issue III: Utilization

An example of cloud service in DC:

•

The networking demands of cloud applications are

time-varying

•

Low network

utilization

if statically reserved

A Large Design Space for 3-way Tradeoffs

Problem

How to achieve

flexible fairness

on bandwidth sharing

for balancing such tradeoffs

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

13

Requirements 1

Guarantee

base

bandwidth

t1 t2

Base bandwidth: B

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

Requirement 2

Weight

• Important (expensive) jobs have larger weight

How to

• Share the bandwidth beyond the base bandwidth in proportion

Assign a

weight

for each VM

D - B

Base bandwidth: B

15

Problem

How to achieve these two goals, as

well as maintaining high utilization?

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.

Model formulation

Resources abstraction

•

Non-blocking core (full bisection bandwidth)

•

VMs located in servers

...

...

...

...

19

Model formulation

We know

• 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

Problem Characterization

Asymmetric Nash Bargaining Solution

𝑚𝑎𝑥 (

𝑟

− 𝐿

)

𝐿

≤ 𝑟

≤ 𝑈

, ∀𝑖, 𝑗 ∈ ℵ

𝑟

≤ 𝐶

, ∀𝑚 ∈ ℳ

𝑟

≤ 𝐶

, ∀𝑚 ∈ ℳ

Constraints for bound and server capacity

Why Nash bargaining solution in game?

21

DCN: An Ideal Network Environment

to be viewed as a Harmonious Society

Server Switch Poor VMs Rich VMs Harmonious society End Server Wealth flow

• Poor VMs: base bandwidth > bandwidth demand (Bi>Di)

• Rich competitor: base bandwidth ≤ bandwidth demand (Bi≤Di)

• Fairness: 1) Minimum guarantee for the poor 2) Maintain proportionality among the rich

Solution

Solution

• Lagrangian relaxation dual problem/ Subgradient method • Solution to the dual problem

• Solution to the primal problem: bandwidth allocation

𝜆

= max (0, 𝜆

− 𝜉(𝐶

− 𝑟

))

𝑟

= 𝐿

+

𝜆_𝑚 +𝜆_𝑙

𝜆_𝑚 can be solved by iteration on each server

𝑟_𝑖,𝑗 of a link can be solved with 𝜆 on two end servers

Distributed

23

Solution

Distributed cooperative algorithm

• Distributed: dual variable 𝜆_𝑚, 𝜆_𝑙

• Cooperative: bandwidth allocation 𝑟_𝑖,𝑗

𝑟

= 𝐿

+

𝜆

=

max (0, 𝜆

− 𝜉(𝐶

− 𝑟

))

𝜆

=

max (0, 𝜆

− 𝜉(𝐶

− 𝑟

))

Algorithm: Falloc (Fair allocation)

How does the algorithm work

• 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

𝜆

= max (0, 𝜆

− 𝜉(𝐶

− 𝑟

))

𝑟

= 𝐿

+

Evaluations

Implementation via SDN

Priority-based package on Layer 2

Modify VMM network I/O scheduler

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

27

Fairness

•

Guarantee bandwidth for H1 and H3

•

Share the bandwidth beyond the base bandwidth proportionally

for H2 and H4

•

Balance the tradeoff

Base bandwidth: 250 Mbps

Utilization

29

Algorithm efficiency

•

Convergence speed under

Falloc

•

Small step size: slow

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

31

Q&A

Your suggestion is appreciated

！

Thank you!

Prof. Fangming Liu

More details:

http://grid.hust.edu.cn/fmliu/

Email:

[email protected]