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Optimizing Cost and Performance for Content Multihoming

Hongqiang Harry LiuYe Wang

Yang Richard YangHao WangChen Tian

Aug. 16, 2012

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Content Multihoming is Widely Used

Content Publisher

Content Viewers

CDN-2 CDN-3CDN-1

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Why Content Multihoming: Performance Diversity

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Why Content Multihoming: Performance Diversity

Table: The fraction of successful deliveries for objects with streaming rate of 1Mbps | 2Mbps | 3Mbps.

Diversity in different areas

Diversity in different streaming rates

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Why Content Multihoming: Cost Diversity

Concave Function Region Based

Amazon CloudFrontVolume in a charging period

MaxCDN LiquidWeb

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Our Goal

• Design algorithms and protocols for content publishers to fully take advantage of content multihoming to optimize – publisher cost and – content viewer performance.

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Key Question

• A content object can be delivered from multiple CDNs, which CDN(s) should a content viewer use?

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Key Challenges

• Online vs statistical CDN performance– e.g., real-time network congestions or server

overloading

• Complex CDN cost functions– e.g., the cost of assigning one object to CDN(s)

depends on other assignments => coupling

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Our Approach: Two-Level Approach

Efficient Optimal Object Assignment Algorithm

Local, Adaptive Clients

Guidance from Content Publishers

Statistical Performance

OnlinePerformance

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Roadmap

• Motivations• Global optimization

– Problem definition– CMO: An efficient optimization algorithm

• Local active client adaptation• Evaluations

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Roadmap

• Motivations• Global optimization

Problem definition– CMO: An efficient optimization algorithm

• Local active client adaptation• Evaluations

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Problem Definition: Network Partition

Global Network Location Area

Object-i

it

1ait 2a

it4ait

5ait

7ait

8ait

3 0ait

6 0ait

Location Object Exclusionai

a

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Problem Definition:CDN Statistical Performance

Global Network

CDN-k

99%

92%

Target Performance: 90%

1ai 2ai

7aj

80%

71{ , }aakF i j Statistical Performance: e.g.,

probability of successful deliveries in an area

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,,

{ }

,

,

,

min

. . , , 0 : 1

, , , : 0

, , : 0

ai k

r a ak i k i

x k r a r

a ai i k

k

a ak i k

ai k

C x t

s t i a n x

i k a i F x

i k a x

Problem Definition:Optimization Formulation

Problem Q

is the fraction of traffic put into CDN-k for location object,ai kx

Charging function in region r of CDN-k

Traffic volume in charging region-r of CDN-k

ai

All requests are served

Performance constraints

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Solving Problem Q: Why not Standard Convex Programming or LP

• To minimize a concave objective function• Problem scale is too large to be tractable:

– N objects, A locations and K CDNs => N*A*K variables, and N*K constraints

– For example, given N=500K, A=200 and K=3 =>300M variables and 100M constraints

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Roadmap

• MotivationsGlobal optimization

– Problem definitionCMO: An efficient optimization algorithm

• Local active client adaptation• Evaluations

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Developing the CMO Algorithm: Base

• Problem Q has an optimal solution which assigns a location object into a single CDN.

• The object assignment problem is still hard:Assignment Space K CDNs and

N location objects => KN assignment possibilities

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CMO Key Idea: Reduction in the Outcome Space

Assignment Space Outcome (CDN Usage) Space

Infeasible Assignments

There are only vertices points, where is the # of charging regions (a small #)

There are up to points with CDNs and location objects

NKK N

KRN

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Mapping From Object Assignment to Outcome

CDNs CDN-1 CDN-2

Traffic in Region-1

Traffic in Region-2

Location Objects

Traffic in Area-1

Traffic in Area-2

11v

21v

12v

22v

11t 0 1

2t 0

0 21t 0 2

2t

21,1 1x 1

2,1 1x 22,2 1x

1 11 2t t 0

22t

21t

Example assumption: Area-i is in charging Region-i

11,1 1x

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Extensions

• CDN subscription levels (e.g. monthly plan)– Introducing CDN capacity constraints

• Per-request cost– Adding a row which indicates the #request in outcome

• Multiple streaming rates– Considering each video at each encoding rate as an

independent object

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Extension Example: Per-request Cost

CDNs CDN-1 CDN-2

Traffic in Region-1

Traffic in Region-2

#Request

Location Objects

Traffic in Area-1

Traffic in Area-2

#Request

11v

21v

12v

22v

11t 0 1

2t 0

0 21t 0 2

2t

1 2 11 1 2n n n 2

2n

1 11 2t t 0

22t

21t

11,1 1x 2

1,1 1x 12,1 1x

22,2 1x

21n

22n

11n

12n

Example assumption: Area-i is in charging Region-i

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From Algorithm to System

Optimizer

CDN1CloudFront

CDN2MaxCDNPassive Client

CPDNS

Resolve obj-i.cp.com

CNAMEd3ng4btfd31619.cloudfront.net

Client IP area

Long-term scale statistics

Fast-scale fluctuations

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Roadmap

• Motivations• Global optimization

– Problem definition– CMO: An efficient optimization algorithm

Local active client adaptation• Evaluations

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Active Clients

h11h12

h21

Primary CDNBackup CDN

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Informing Active Client

Optimizer

CDN1CloudFront

CDN2MaxCDN Active Client

Manifestation Server

Requestcp.com/sample.flv

Passive Client

Resolve obj-i.cp.com

Get CNAMEd3ng4btfd31619.cloudfront.net

Multiple CDNs with priorities

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How to Select Multiple CDNs?

• The same CMO algorithm, where input CDNs are virtual CDNs (ranked CDN combinations)

• Example: Select 2 CDNs (primary + backup) for an active client:– Each pair of CDNs is a “virtual CDN”: k’ = (k, j) – Fk’ : the set of location objects that CDN k and CDN j

together can achieve performance requirement• Each with 90% statistics => together > 90%

– Objective function: for each location object ia , primary CDN k delivers the normal amount of traffic and backup j incurs backup amount of traffic.

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Active Clients: Adaptation Goals

• QoE protection (feasibility): – Achieve target QoE through combined available

resources of multiple CDN servers

• Prioritized guidance: – Utilize the available bandwidth of a higher priority

server before that of a lower priority server

• Low session overhead (stability): – No redistributing load among same-priority servers

unless it reduces concurrent connections

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Active Clients: Control Diagram

h11 h12

h11+h12

Primary CDN Backup CDNh11<R and h12>R

h11>R and h12<R

h11<R and h12<R?

h11<R and h12<Rh11+ h12>R

h12>Rh11>R

h11+h12+h21

h11 + h12<R

h11 + h12>R

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Realizing Control Diagram: Key Ideas

• Controlling the windows– AIMD– Total load control

• Using the sliding windows– Priority assignment

h11

h12

h21

Pieces to request in T

# pieces can be downloaded from the server in a period T

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Roadmap

• Motivations• Global optimization

– Problem definition– CMO: An efficient optimization algorithm

• Local active client adaptationEvaluations

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CMO Evaluation Setting

• 6-month traces from two VoD sites (CP1 and CP2):– Video size– #request in each area (learned from clients’ IP)

• Three CDNs– Amazon CloudFront– MaxCDN– CDN3 (private)

• #request prediction– Directly using #request last month in each area

• Compare 5 CDN selection strategies:– Cost-only, Perf-only, Round-robin, Greedy, CMO

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Cost Savings of CMO

Avg Saving: ~35% compared with Greedy

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Cost Savings of CMO

All three CDNs have good performance in US/EU

Avg Saving: ~40% compared with Greedy

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Active Client Evaluation Setting

• Clients– 500+ Planetlab nodes with Firefox 8.0 + Adobe Flash

10.1

• Two CDNs– Amazon CloudFront– CDN3

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Active Client Test Cases

• Stress test– CDN3 as primary; CloudFront as backup

• Two servers in two CDNs: primary1, backup1• Two servers in the primary CDN: primary1, primary2

– Control primary1’s capacity• Step-down -> recover• Ramp-down -> recover• Oscillation

• Large scale performance test:– CloudFront as primary, CDN3 as backup– We saw real performance degradations

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Stress Tests (Step-down)

Step-down Recovery

Different Priority

Same Priority

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Stress Tests (Ramp-down)

Ramp-down

Recovery

Different Priority

Same Priority

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Stress Tests (Oscillation)

Different Priority

Same Priority

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Active Client QoE Gain (CloudFront + CDN3)

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Active Client: Cost Overhead

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Conclusions

• We develop and implement a two-level approach to optimize cost and performance for content multihoming: – CMO: an efficient algorithm to minimize publisher cost

and satisfy statistical performance constraints– Active client: an online QoE protection algorithm to

follow CMO guidance and locally handle network congestions or server overloading.

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Q&A

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Related Work and Conclusions

• CDN switchers: seamless switch from one CDN to another– One Pica Image

• CDN Load Balancers: executing traffic split rules among CDNs– Cotendo CDN– LimeLight traffic load balancer– Level 3 intelligent traffic management

• CDN Agent: CDN business on top of multiple CDNs– XDN– MetaCDN

• CDN Interconnection (CDNi)– Content multihoming problem still exists in the CDN delegations.

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Backup Slides

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Searching Extremal Assignments

Space of V

(1) Separation Lemma: *

* *, : , 0is extremal P P V V

(2) Recall: vv

V v e (3) We prove: *

* *, , : , ,k vis extremal P v k v P v e P v e

P

*V

V

(4) With a proper , we can find an extremal assignment:• For each object , there is a unique minimum element in set

Pv { , | }kP v e k

How to find a proper ?

How to enumerate all possible extremal assignments?

P

*

* *, : , ,v vv v

is extremal P P v e P v e

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Picking Proper P

A special subset of P (S’): all elements in are distinct , : , ,k jv k j P v e P v e

, 0k jP v e e 1 k jv e e

2 k jv e e

P

We prove:• Each extremal assignment can be found by an element in S’• Two interior points from the same cell find the same extremal assignment

Cell Enumeration of Hyperplane Arrangements

Conclusion:• All possible extremal assignments are exhausted by S’.• The number of extremal assignments is no more than the #cell (polynamial

with #object).

A Proper P:• there is a unique minimum element in set v { , | }kP v e k

{ , | }kP v e k

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Realizing Control Diagram: Key Ideas

• Yry (revise next slide) Draw a figure w/–An active client–3 cdn servers–Label a sliding window to conn. to each CDN

–Say 3 key techniques to control and use the sliding window: total