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www.nec-labs.com. Intelligent Workload Factoring for A Hybrid Cloud Computing Model. Hui Zhang Guofei Jiang Haifeng Chen Kenji Yoshihira Akhilesh Saxena NEC Laboratories America Princeton, NJ July 10 th , 2009. IT trends: Internet-based services and Cloud Computing.

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Intelligent workload factoring for a hybrid cloud computing model l.jpg

www.nec-labs.com

Intelligent Workload Factoring for A Hybrid Cloud Computing Model

Hui Zhang Guofei Jiang Haifeng Chen Kenji Yoshihira Akhilesh Saxena

NEC Laboratories America

Princeton, NJ

July 10th, 2009


It trends internet based services and cloud computing l.jpg

IT trends: Internet-based services and Cloud Computing

  • Trend on IT infrastructure

    • Adoption of cloud computing architecture.

      • Computations return to the data centers.

    • Promise of management simplification, energy saving, space reduction, …

  • Trend on IT applications

    • Adoption of service oriented architectures & Web 2.0 applications, e.g.

      • Software as a Service (SaaS)

      • Mobile commerce

      • Open collaboration

      • Social networking

      • Mashups

Public

clouds

Private

clouds

Blue Cloud

Google

applications


What is cloud computing l.jpg

What is Cloud Computing?

  • An emerging computing paradigm

    • Data & services : Reside in massively scalable data centers

      • Can be ubiquitously accessed from any connected devices over the internet.

  • The unique points to cloud computing users are the Elastic infrastructure and the Utility model: provision on demand, charge back on use.

[IBM]

Businesses, from startups to enterprises

Web 2.0-enabled PCs, TVs, etc.

4+ billion phones by 2010 [Source: Nokia]


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Cloud Computing is not a reality yet for the majority

  • “Little Investment In Cloud & Grid Computing for 2009.”

  • “CIOs are looking primarily to tested, well-understood technologies that can result in savings or increased business efficiencies whose support can be argued from a financial point of view”

    • a survey by Goldman Sachs & Co., July 2008.

  • What about current application platform?

  • What about data privacy?

  • What about the performance?

  • Why the full package?

  • ….

Private cloud? Public cloud?

Choose one, please!

Let me think about it.


Slide5 l.jpg

IT customers can have the best Total Cost of Ownership (TCO) strategy with their applications running on a hybrid infrastructure

Local data center, small and fully utilized for best application performance.

Remote cloud, infinite scaling, use on demand and pay per use.

A hybrid cloud computing infrastructure model

Remote cloud (large, pay per use)

5% workload,

1% time

User requests

Workload factoring

User requests

Dynamic Workload

95% workload,

100% of time

Local data center (small, dedicated)

5


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The economic advantage of hybrid cloud computing model: a case study

Annual Cost ($$)

Hosting solution

Cost on running a 790-servers data center

A local data center

hosting 100% workload

To host Yahoo! Video website workload

Amazon EC2: peak workload of 5% time

US $ 7.43K

+

+

Cost on running a 99-servers data center

A local data center:

workload of 95% time

Workload Factoring

Amazon EC2 hosting 100% workload

US $ 1.384M

†: assume over-provisioning over the peak load

‡: only consider server cost. Amazon EC2 pricing: $0.10 per machine hour – Small Instance (Default).

6


Hybrid cloud computing architecture l.jpg

Hybrid Cloud Computing architecture

(1)

(2)

(3)

Design goals

smoothing the workload dynamics in the base zone application platform and avoiding overloading scenarios through load redirection;

making trespassing zone application platform agile through load decomposition not only on the volume but also on the application data popularity.


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Intelligent workload factoring: problem formulation

  • Solution:

    • fast data frequency estimation

      • Graph model generation

    • greedy bi-section partition

      • Hypergraph partition [Karypis99]

  • Problem statement:

  • Input:

    • requests (r1, r2, …, rM).

    • data objects (d1,d2, …,dN).

    • request-data relationship types (t1=(di,dj,…), t2=(dx,dy,…),…, tR)

      • each request belongs to one of the R types

  • Output:

    • Request partition schemes (R1, R2,…, RK) and data partition schemes (D1,D2,…,DK ) for K locations.

  • Problem: a fast online mechanism to make the optimal decision on request and data partition for minimal cross-location data communication overhead.

Loc. 2

Loc. 1

d4

d1

d3

d6

d5

d2

A hypergraph partition problem model

(NP-hard)

Subject to

Where:

request type i;

# of requests for type-i;

sum of the vertex weights in Location-k

Loc-i capacity of res. type t (1: storage, 2: computing)


The fast top k data item detection algorithm l.jpg

The fast top-k data item detection algorithm

Time

t0

Data popularity Pold

Data popularity

Pnew

  • Design goal

    • Starting at t0, reach an estimation accuracy on the top-k data items in Pnew within the minimal time.

  • The key ideas leading to the detection speedup

    • filtering out old popular data items in a new distribution

    • filtering out unpopular data items in this distribution.


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Speedup analysis of the fast top-k algorithm

  • Problem model

    • Formally, for a data item T, we define its actual request rate p(T) = total requests to T/total requests .

    • FastTopK will determine an estimate p’(T) such that with probability greater than α.

      • We use Zα denote the percentile for the unit normal distribution. For example, if α = 99.75%, then Zα = 3.

  • Main speedup result

    • Define an amplification factor X for the rate change of a data item before and after the historical topk-K filtering as

    • Theorem 1: LetNCbefore be the number of samples required for basic fastTopK, and NCfafter be the number of samples required for filtering fastTopK

    • Notation: X2 speedup of the detection process even with a X-factor on rate amplification due to historical information filtering.


Fast and memory efficient workload factoring scheme l.jpg

Fast and memory-efficient workload factoring scheme

Arriving request

Panic mode?

n

y

Fast top-k data item

detection scheme

“Base zone”

Does it belong to

the top-k list?

n

end

y

“Trespassing zone”

“Base zone”

end

end


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A complete request dispatching process in hybrid cloud computing

Arriving request

Workload factoring

Base zone

Trespassing zone

Workload shaping

Round-robin

dispatching

admit

drop

Drop the

request

LWL

Available server?

n

y

end

end

Admit the

request

Drop the

request

end

end


Testbed setup l.jpg

Testbed setup

IWF

a http request

load controller

request forwarding

Dispatching decision

http reply

S3

EC2

rtsp://streamServer_x//…

rtsp://streamServer_x//…


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Workload factoring evaluation: incoming requests

t0


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Workload factoring evaluation: results (I)


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Workload factoring evaluation: results (II)

Base zone

server capacity

Trespassing zone

server capacity


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Conclusions

  • We present the design of intelligent workload factoring, an enabling technology for hybrid cloud computing.

    • Targeting enterprise IT systems to adopt a hybrid cloud computing model where a dedicated resource platform runs for hosting application base loads, and a separate and shared resource platform serves trespassing peak load of multiple applications.

  • The key points in our research work

    • Matching infrastructure elasticity with application agility is a new cloud computing research topic.

    • Workload factoring is one general technology in boosting application agility.

      • CDN load redirection is a special case.


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Backup slides


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Multi-application workload management

Multi-application workload management architecture


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