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intelligent workload factoring for a hybrid cloud computing model

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
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





Blue Cloud



what is cloud computing
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.


Businesses, from startups to enterprises

Web 2.0-enabled PCs, TVs, etc.

4+ billion phones by 2010 [Source: Nokia]

cloud computing is not a reality yet for the majority
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.

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)


the economic advantage of hybrid cloud computing model a case study
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).


hybrid cloud computing architecture
Hybrid Cloud Computing architecture




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.

intelligent workload factoring problem formulation
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







A hypergraph partition problem model


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)

the fast top k data item detection algorithm
The fast top-k data item detection algorithm



Data popularity Pold

Data popularity


  • 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.
speedup analysis of the fast top k algorithm
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
Fast and memory-efficient workload factoring scheme

Arriving request

Panic mode?



Fast top-k data item

detection scheme

“Base zone”

Does it belong to

the top-k list?




“Trespassing zone”

“Base zone”



a complete request dispatching process in hybrid cloud computing
A complete request dispatching process in hybrid cloud computing

Arriving request

Workload factoring

Base zone

Trespassing zone

Workload shaping





Drop the



Available server?





Admit the


Drop the




testbed setup
Testbed setup


a http request

load controller

request forwarding

Dispatching decision

http reply





workload factoring evaluation results ii
Workload factoring evaluation: results (II)

Base zone

server capacity

Trespassing zone

server capacity

  • 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.
Multi-application workload management

Multi-application workload management architecture