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Intelligent Workload Factoring for A Hybrid Cloud Computing Model
Hui Zhang Guofei Jiang Haifeng Chen Kenji Yoshihira Akhilesh Saxena
NEC Laboratories AmericaPrinceton, NJ
July 10th, 2009
www.nec-labs.com
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IT trends: Internet-based services and Cloud Computing
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
Trend on IT infrastructure
– Adoption of cloud computing architecture.
• Computations return to the data centers.
– Promise of management simplification, energy saving, space reduction, …
Blue Cloud
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What is Cloud Computing?
4+ billion phones by 2010 [Source: Nokia]
Web 2.0-enabled PCs, TVs, etc.
Businesses, from startups to enterprises
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]
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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.
Private cloud? Public cloud?Choose one,
please! Let me think about it.
•What about current application platform?•What about data privacy?•What about the performance?•Why the full package?
….
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Local data center (small, dedicated)
A hybrid cloud computing infrastructure model
Remote cloud (large, pay per
use)
Dynamic Workload
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.
User requests
User requestsWorkload factoringWorkload factoring
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The economic advantage of hybrid cloud computing model: a case study
To host Yahoo! Video website
workload
A local data center hosting 100%
workload
Hosting solution
Annual Cost ($$)
Cost on running a 790-servers data
center
A local data center:workload of 95% time
Amazon EC2: peak workload of 5% time
+
Amazon EC2 hosting
100% workload
Workload Factoring
US $ 1.384M
†
†
†: assume over-provisioning
over the peak load
Cost on running a 99-servers data
center
+US $ 7.43K
‡
‡
‡: only consider server cost. Amazon EC2 pricing: $0.10 per machine hour – Small Instance (Default).
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Hybrid Cloud Computing architecture
Design goals 1.smoothing the workload dynamics in the base zone application platform and avoiding overloading scenarios through load redirection;
2.making trespassing zone application platform agile through load decomposition not only on the volume but also on the application data popularity.
(1) (2)(3)
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Intelligent workload factoring: problem formulation
cutjt
jtccutsizeMin )()(
2,1
,...,2,1)1()(
tand
KkforCVW tk
tk
t
)( jtc
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.
Solution: – fast data frequency estimation
• Graph model generation– greedy bi-section partition
• Hypergraph partition [Karypis99]
Loc. 1 Loc. 2d1
d3
d2
d5
d4
d6
A hypergraph partition problem model (NP-hard)
Where:
Subject to
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)
jt
)( kt VW
tkC
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The fast top-k data item detection algorithm
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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: Let NCbefore 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.
))2
1)((),2
1)((()('
TpTpTp
)(
)(
Tp
TpX
before
after
2X
NN
CbeforeC
after
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Fast and memory-efficient workload factoring scheme
“Base zone”
Arriving request
n
ny
“Trespassing zone”
Fast top-k data item detection scheme
end
end
“Base zone”
end
yPanic mode?
Does it belong to the top-k list?
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A complete request dispatching process in hybrid cloud computing
Round-robin dispatching
Arriving request
Trespassing zone
n
end end
LWL
Base zoneWorkload factoring
Workload shaping
Available server?
Drop the request
Admit the request
drop admit
end
Drop the request
endy
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Testbed setup
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EC2 S3
load controller
a http request
request forwarding
Dispatching decision
http replyrtsp://streamServer_x//…
rtsp://streamServer_x//…
IWF
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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)
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Base zoneserver 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