Optimizing Model Performance and Process Flow by jls43972

VIEWS: 18 PAGES: 29

									     SOA 09 Annual Meeting & Exhibit
           October 25-28, 2009

Session 73 PD, Optimizing Model Performance
              and Process Flow

                 Moderator:
     Carl Desrochers, FSA, FCIA, MAAA

                Presenters:
      Craig W. Reynolds, FSA, MAAA
            Brian Rhoads, CFA
                Jim Skirvin
Cluster Modeling Overview
Liabilities, Assets, or Scenarios



                                  Presented by:
                                  Craig Reynolds, FSA, MAAA
                                  Consulting Actuary
                                  craig.reynolds@milliman.com




Trends in Actuarial Modeling

        Past Past           Present                 Future



                       Faster hardware and
                       software often make
 Actuarial modeling    seriatim calculations    Cluster Modeling
  often required for   practical                 makes nested
 calculations to run                             stochastic and
  in an acceptable      •Grid processing       massive stochastic
      timeframe         •Job threading
                                                 runs practical
                        •Faster processors




                              2
Modeling May Always be Necessary
 Particularly nested stochastics (stochastic in stochastic)
 Examples: IFRS, FAS 133, SOP 03-1, Dynamic Hedging, PBA,
 Option Pricing, VA CARVM, C-3 Phase 2 and 3, Fair Value




                                    3




Nested Stochastic Runtimes

 Sample calculation specifications
 – 1 million policies
 – 30-year projections
 – Quarterly calculations of IFRS, PBA, or other stochastic reserves
   across 500 paths
 – 10,000 scenarios
 Implications-Sometimes seriatim cannot be done
   600 trillion policy-path projections
 – At 1000 cell paths per second, this is still:
   • 600 billion seconds
       19 thousand years
 Clearly we cannot rely on hardware or software alone!

                                    4
Cluster Modeling Does it Better

 Do not ask: To model or not to model?




 Instead ask: When you have to model, how to do it best?




                                  5




Living in a World With Modeling
 Classic Modeling Techniques
 – Some rule-based (age modeling, issue-date modeling)
 – Some judgment-based (minor plans to major plans)
 – Focused on validation of initial balance sheet
 – Assumes that reproduction of initial amounts implies good
   reproduction of future earnings

 Challenges
 – Keeping up-to-date with new plans
 – Managing and measuring model noise
 – Making auditors happy

                                  6
 Cluster Modeling Diagram-Two Dimensions
            (Liability Example: Opening reserve and FY premium)
           (Asset Example: Book/Par Ratio and Yield to Maturity)

Two Dimensional Plot of      Assign Policies to      Gross up Central Points
Policies of Various Sizes        Clusters




                                    7




Cluster Modeling Eases Challenges
 Any product or asset type
 Better compression ratios for a given model-to-actual fit
 Easily automated with little upfront effort
 Maintained and applied in similar ways at later valuation dates
 Allows customization to place different priorities on different
 measures of model fit
 Can be applied to seriatim or modeled in-force
 Allows easy adjustment to the number of model points to
 produce more or less model granularity, depending on the
 application
 Allows easy on-the-fly analysis of model fit for differing levels of
 model granularity, without rerunning a model

                                    8
Key Cluster Modeling Concepts
 Location Variable: Any value that you want the model to
 closely reproduce, e.g.,
 –   Opening reserves or premiums in-force
 –   First-year premiums
 –   First-year claims
 –   Net-liability cash flow in each of the first five years
 –   Asset coupon rate
 –   Book / Par ratio
 –   Present value of profits
 Values normalized by dividing by sample standard deviation
 Users define the list of variables and capture their values in an
 inventory report

                                              9




Key Cluster Modeling Concepts

  Distance Function: A measure to show how “far away” any
  two policies or cusips or scenarios are from each other in n-
  dimensional space
  Euclidean distance operating on normalized location-variable
  values, with each variable representing one spatial dimension
  May assign weights to scale distances in certain dimensions to
  reflect importance of this dimension
(Var11 − Var12 ) 2 + (Var 21 − Var 2 2 ) 2 + (Var 31 − Var 32 ) 2




                                              10
Key Cluster Modeling Concepts

 Size: One component of the importance of each policy
 – Typically face amount or units in-force
 – Might also be account value in-force, annuity benefit amount, asset
   par value, or some other user-defined quantity

 Importance = (Size) * (Distance to nearest neighbor)




                                  11




Key Cluster Modeling Concepts

 Segment: A group that each policy belongs in, such that no
 policy will be mapped outside of its segment
 LOB or asset class will always be a segment
 Can also be things like premium period, insurance period,
 GAAP era, reserve basis, issue year, or plan code
 Use of segments shrinks compression time and may improve
 model mapping results across other scenarios




                                  12
Cluster Modeling Algorithm
Compute the distance of every policy from every other in its segment
Compute the Importance of each policy as the product of (size) *
(distance to nearest neighbor) for each policy
Identify the policy with the least importance. Map it to its nearest
neighbor within the same segment
Repeat until the desired number of cells is obtained
For each resulting cluster, pick the point in the cluster that is closest to
the average location of all cells in that cluster. Use this point to
represent the cluster
Gross up or add up all in-force data associated with the destination cell
Review model fit
Refine location variables and weights as desired and repeat

                                     13




Applying Cluster Modeling to Scenarios

 Location variables could be equity indexes or interest rates
 Clustering might be independent of liabilities or assets to be
 modeled
 Most useful for mean rather than tail analysis




                                     14
Cluster Options for a Large Stochastic Model

 Compress Liabilities
 Compress Assets
 Compress Scenarios
 All of the above:
 – Improving speed by multiple orders of magnitude




                                15




Cluster Model
versus
Replicating Portfolio




                                16
Replicating Portfolio

  Replicating portfolio is an alternate way to reduce runtime

  Search for a portfolio of assets to represent the cash flows

  Advantage:
   – Reduce liability model to a small subset of assets
   – Asset valuations may be done by closed form solution in some
     cases
   – Greater insight into nature of liabilities
   – Easy aggregation of results across LOBs

  Particularly useful for nested stochastic environment

                                17




Replicating Portfolio

  Key Limitations
  – Does not allow testing varying liability assumptions or experience
  – Does not project income statements or balance sheets
  – Replication process may be challenging for products with
    complex policyholder behavior




                                18
Cluster Case Studies




                                 19




Case Study 1: A Life / Health Model

 120,000 model points in original model
 Mix of traditional life and health products
 200 model points in cluster “model of model”
 Liability focused—but could just as easily have been assets




                                 20
 Case Study 1: Location Variables

   Initial reserve (weight 1)
   First projection year premiums (weight 1)
   First projection year claims (weight 1)
   PV of proxy profits (weight 8)
   PV of proxy profits through 10 projection years (weight 6)
   PV of proxy profits through 20 projection years (weight 6)




                                         21




 Case Study 1: Results

                         Original             New       Difference        Ratio
    Initial Reserve         372,911           371,605          (1,306)     99.65%

 First-Year Premiums            85,708         81,645          (4,063)     95.26%

  First-Year Claims             36,485         35,162          (1,322)     96.38%

    PV of Profits           154,467           154,444             (23)     99.99%

PV of Profits—10 years          77,808         77,634           (174)      99.78%

PV of Profits—20 years      119,924           120,001                77   100.06%




                                         22
Case Study 1: More Results
                                              Selected Income Statement Items
                                          120,000 cell Model Versus 200 Cell Model
90,000


80,000
                     Comm. & Exp.
70,000


60,000
                          Other Benefits
50,000


                                      Surrender Benefits
40,000
                                                                  Premiums                  Dist. Earnings

30,000


20,000


10,000


   -
         1   2   3    4   5   6   7   8    9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30


                                                             23




Case Study 2: A Large Seriatim Term Model
       Only the base scenario is used for calibration
       Despite this, we have excellent model fit for other scenarios
       with 4000 to 1 compression!
                                      1,200,000             300 Cell
         Scenario                     Seriatim               Model            Difference           Ratio

       Base                                     4,309               4,295                  14       100.3%

       Mortality*115%                           3,649               3,651                  (3)        99.9%

       Mortality*85%                            4,978               4,945                  33       100.7%

       Lapse*115%                               3,714               3,685                  29       100.8%

       Lapse*85%                                5,251               5,266                (15)         99.7%


                                                             24
Case Study 3: A Variable Annuity Model

 200,000 policies with GMDB, GMAB, GMWB, GMIB


 Original company classic model was 9,000 cells


 Excellent fit of cluster model to original model across
 scenarios, despite using only two scenarios for calibration




                                 25




Case Study 3: A Variable Annuity Model
                     Ending Surplus ($millions) for
   4000         Bottom 98% of Scenarios Under 3 Models
                                              Original
   3000
                                              9000 cells

   2000


   1000
                                                         50 cell

      0
                              250 cell
  -1000


  -2000


  -3000



                                 26
Good Fit For Tail Analysis as Well

             Ending Surplus ($millions) for Bottom
   500          100 Scenarios Under 4 Models


     -



  (500)


                                                     250
 (1,000)
                                                     50
                                                     Seriatim
 (1,500)
                                                     9,000

 (2,000)



 (2,500)



                                   27




Case Study 4: An Existing Asset Model
 First attempt. 8-1 compression on runoff MBS and Bonds




                                   28
Case Study 4: An Existing Asset Model
             Stochastic    PVDE             PVDE
              Scenario    Cluster          Original     Fit Error
                 1            3,429             3,423         0.2%
                 2            3,359             3,359         0.0%
                 3            3,120             3,127        -0.2%
                 4            3,616             3,595         0.6%
                 5            3,476             3,471         0.1%
                 6            3,310             3,308         0.1%
                 7            3,545             3,541         0.1%
                 98           3,619             3,602         0.5%
                 99           2,730             2,752        -0.8%
                100           3,783             3,767         0.4%


              Averages        3,356             3,351         0.1%
                Max           3,813             3,799         1.1%
                Min           2,619             2,629        -0.8%

                                      29




Case Study 5: Scenario Clustering

 2160 cell VA GMWB pricing model
 Attempting to determine risk neutral cost of GMWB benefit
 Mean with 1000 scenarios = 52 basis points
 Mean with 50 clustered scenarios = 50 basis points
 80% of cells within 5 basis points
 95% of cells within 10 basis points
 Good enough? Perhaps within the precision of the assumptions




                                      30
Case Study 5 – GMWB

 Run time from 1 hour    3 minutes

                             50
                  1000
                          Clustered
                Scenarios
                          Scenarios
       Plan       GMWB          GMWB      Match
      000001        66               66    99%
      000002        56               54    96%
      000003        59               59    99%


                           31




Case Study 6 – GMAB

                            50
                 1000
                         Clustered
               Scenarios
                         Scenarios
      Plan       GMAB           GMAB      Match

     000001         39           40       103%

     000002         33           34       104%

     000003         34           36       106%


                           32
Case Study 7 – GMIB

                           50
                1000
                        Clustered
              Scenarios
                        Scenarios
     Plan      GMIB        GMIB     Match

    000001       65         61      94%

    000002       58         54      92%

    000003       59         55      92%


                      33




Case Study 8 – GMDB

                           50
                1000
                        Clustered
              Scenarios
                        Scenarios
      Plan     GMAB        GMDB     Match

     000001      30         29      98%

     000002      26         26      99%

     000003      25         26      101%


                      34
Implementation Steps

 Define location variables, calibration scenarios, and inventory
 reports
 Identify target number of cells and assign weights to calibration
 variables
 Identify validation criteria
 Implement compression
 Validate
 Refine as needed
 Algorithm is not system dependent

                                35
         Jim Skirvin– Cloud Computing Evangelist
                       October 2009




                                                                               1




THE HOLY GRAIL : ENTERPRISE EFFICIENCY

  EFFICIENCY DELIVERS FLEXIBILITY AND THE ABILITY TO CREATE
       SHAREHOLDER VALUE AND COMPETITIVE ADVANTAGE




     CEO                    CIO                                  CFO
  We need to be          We need to be                         We need to
   responsive to        responsive AND                     minimize cost and
  changes in the           maximize                         risk, and ensure
      market               efficiency                          compliance




                                                 IT
                                         We need to create,
                                         deliver and maintain
                                          flexible IT services
                                                                               2
    CLOUD COMPUTING ENABLES
    THE EFFICIENT ENTERPRISE

 Competitiveness                  Control                  Choice




         Cut Capital          …While Automating        …While Remaining
       And Operational        Quality Of Service…          Independent
     Costs By Over 50%.                                   Of Hardware,
    For All Applications...   …With pay-as-you-go       Operating System,
      Shift from fixed to     business flexibility…   Application Stack, And
        variable costs                                  Service Providers



                                                                               3




    GARTNER AGREES




DELL CONFIDENTIAL                                                              4
LEVERAGING CLOUD SERVICES IS ON
THE CIO AGENDA

        Top CIO Reasons To
      Leverage Cloud Services*

    1.
                    Scalability On Demand/
                  Flexibility To The Business
                                                          Cloud
                                                         Services
    2.                  Reduced Hardware




                                                   81%
                       Infrastructure Costs



    3.
                       Reduced IT Staffing/
                       Administration Costs



    4.
                Access To Skills/Capabilities
              We Have No Interest In Developing
                         In-house
                                                  of CIO’s plan to leverage
                                                       cloud services
Source: CIO Research



                                                                              5




A CLEAR TECHNOLOGY DEFINITION




                         A New Approach to
                            Delivering IT
                         Infrastructure as a
                               Service

  • Hardware management is abstracted
  • Applications and services independent of infrastructure or people
  • Companies purchase infrastructure on pay-as-you-go open basis
  • Variable pricing and capacity (up or down)
  • Transition of IT from CapEx to OpEx
                                                                              6
LARGE CLOUD PROVIDERS HAVE
BASED THEIR BUSINESS ON..
Key Solutions                                  Key Lessons
   Custom designed servers and                    Variation is costly
   storage
                                                  Great sensitivity to energy
   Modular data centers                           efficiency
   containers
                                                  High demand for latest
   Tailored service model                         performance technology
   Data Center design &                           Unused features must be
   implementation                                 eliminated
                                                  Massive scale & rapid
                                                  deployment times are normal
                                                  Different service model required




                                                                                                 7




CLOUD SERVICES
CHOOSE THE RIGHT PARTNER WHO HAS SIGNIFICANT EXPERIENCE IN
DELIVERING PRAGMATIC CLOUD SERVICES TO ENTERPRISES AND IS
HELPING CIOS TO PLOT THE CLOUD JOURNEY

Cloud Software-as-a-Service IT Mgmt (Public Cloud)
                                                                       Single largest
• Service delivered via the public Internet to consumers or             footprint for
                                                                      Enterprise SaaS
  enterprises                                                             IT mgmt
                                                                      5500+ customers
• Scale without user intervention and are typically billed by usage


Cloud Infrastructure-as-a-Service (Hybrid Cloud)
                                                                      300 TB of cloud
                                                                       archiving for
• Combines internal IT systems with public cloud services             Enterprise email
                                                                       archiving and
                                                                        eDiscovery
• Hybrid cloud enables use of excess capacity with no upfront
  investment


Cloud Infrastructure-as-a-Service (Private Cloud)                       Private cloud
                                                                         offering for
                                                                        hospitals for    New
                                                                         Electronic
• Deployed behind a firewall for an organization’s internal use       Medical Records
                                                                                         Focus
                                                                                          Area
                                                                      (EMR) archiving
• Private cloud enables IT to be delivered “as a service”

                                                                                                 8
CLOUDS DRIVE EFFICIENCY
THROUGH VIRTUALIZATION
DRAMATICALLY INCREASES ASSET UTILIZATION
WHILE CUTTING LABOR COSTS


                              Server Utilization*
                                                            +280%
                                     +150%                          Large Scale Cloud
                                                                       Economics

         Today

       Physical                    Virtual                 Cloud

         Today
                                     - 30%

                                                            - 40%
                              Data Center Labor




                                                                                        9




 … WITH TRADITIONAL IT WORKLOADS
 BEING CONSUMED “AS-A-SERVICE” CLOUD
 SERVICES
 END USERS CONSUME IT IN A VERY DIFFERENT WAY FROM TRADITIONAL IT
 CONSUMPTION MODELS: PAY-AS-YOU-GO, ELASTIC PROVISIONING, TURN-
 ON AND TURN-OFF




     Software-                  Infrastructure-             Platform-
    as-a-Service                 as-a-Service              as-a-Service


 Applications on Demand      Computing Resources on     App Development /
(E.G. CRM, Collaboration        Demand (Servers,      Production Environment
         IT Mgmt)              Databases, Storage)         on Demand


                           Illustrative Examples




                 ITAAS                       ITAAS
                                                                                        10
CLOUD IMPLEMENTATION
MODELS
THERE ARE TWO COMPLEMENTARY APPROACHES TO IMPLEMENTING
CLOUD SERVICES, EACH WITH THEIR OWN PROS AND CONS




                  Private                                          Public
                  Cloud                                            Cloud
                 Services                                         Services



No external dependencies on                           Minimal capital requirements,
   delivery of service level                           no upfront risk/commitments
   Control of security, audit                            Costs scale with usage
    Data can remain onsite                              Scale capacity up/down
   Requires scale for model                                 Choices emerge
            viability

                …One Size Does Not Fit All For Cloud Services
                                                                                            11




CIO DECISION PATH
CLOUD SERVICES TRADEOFFS WILL FORCE CIOS TO EVALUATE PRIVATE
CLOUD SERVICES ALTERNATIVES AND A NEW CATEGORY OF CLOUD
SERVICES – HYBRID CLOUD SERVICES

                                        CIO
                                      strategic and
                                        practical
                                     considerations
            Private                                                     Public
            Cloud                                                       Cloud
           Services                                                    Services
   Scale out architectures
   (Virtualized production,           Hybrid                        Application resources
 Development on demand and                                          (IT management on-
    Storage on demand)                Cloud                        demand, business apps
                                     Services                            on demand)




 On-Premise            Hosted                Hybrid Cloud is the combination of
Private Cloud       Private Cloud           private and public cloud infrastructure
                                               working as a single cloud service
  Hosted by         Dedicated, but
  Internal IT        Hosted by
                      3rd party               Hybrid cloud services critical to
                                                    bridging tradeoffs

                                                                                            12
                                                       VIRTUALIZATION IS A LOGICAL
                                                       STARTING PLACE
                                                   The drive for multi-tenancy and workload migration suggest the need for an
                                                   abstraction layer. More importantly the operational model which surrounds
                                                   virtualization is the right basis on which to build cloud services
                                                          Server
                                                          Server                        Automated
                                                                                        Automated                                 Real time
                                                                                                                                  Real time                   Micro billing &
                                                                                                                                                              Micro billing &
                                                       consolidation
                                                       consolidation                    workflows &
                                                                                        workflows &                               capacity
                                                                                                                                  capacity                     self-funding
                                                                                                                                                               self-funding
                                                                                         integrated
                                                                                         integrated                              correlation
                                                                                                                                 correlation                 business models
                                                                                                                                                             business models
                                                                                         processes
                                                                                         processes

                                                                           Tiered
                                                                           Tiered                      Intelligent
                                                                                                       Intelligent                           Self-service
                                                                                                                                             Self-service            Alternative
                                                                                                                                                                     Alternative
                                                                       virtualization
                                                                       virtualization                  Placement
                                                                                                       Placement                                                      Sourcing
                                                                                                                                                                      Sourcing
                                                                            pods
                                                                            pods




                                                         IT Infrastructure                       Virtualize        Cloud
                                                                                                                  Services
                                                                                                                             On-premise         Cloud
                                                                                                                                               Services
                                                                                                                                                          Hybrid cloud
                                                                                                                                                          Services
                                                                                                 Infrastructure              Private cloud
                                                                                                                             Services
                                                                                                 Adopt Flex
                                                                                                 Computing                   Hosted Private
                                                                                                                             cloud Services
                                                             Facilities                   Consolidate and
                                                                                          Standardize
                                                                                          Infrastructure

                                                                                                                                                                                   13




 JUMPING INTO THE CLOUD STARTS
 WITH STANDARDIZATION,
 VIRTUALIZATION & OPTIMIZATION
                                                                                        CLOUD ROADMAP
 Cloud Services Enabled Efficient Enterprise Journey




                                                                                          • Leverage cloud services for Disaster recovery and archiving
                                                          Drive Scale With Public • Selectively move Development/test environment to cloud
                                                            and Hybrid Clouds     • Move select legacy application workloads to cloud
                                                                                          • Evaluate Hybrid clouds for data burst capacity and archiving



                                                                                          •   Private cloud infrastructure for virtualized & scaleout workloads
                                                          Improve Flexibility             •   Private cloud infrastructure for development environments
                                                          With Private Clouds             •   Migrate Application workloads to cloud (i.e. collaboration)




                                                            Reduce IT Costs               • Reduce management TCO with SaaS-based IT management
                                                         Standardize, Virtualize,         • Virtualize to create a cloud ready infrastructure stack
                                                               Optimize                   • Standardize on x86 open standards based technologies



Start

                                                                          Available now                Available in 2010                      Hype cycle
                                                                                                                                                                                   14
 CLOUD STRATEGY
Cloud Imperatives                              Strategy Tenets
Open architectures and standards         Innovate and deliver standards-based
will prevail                             infrastructure specifically designed for
                                         clouds
The cloud software stack should be
built from best of breed and tailored    Provide open architectures and
to the function it serves                frameworks that drive cloud
                                         interoperability
Virtualizing on x86 hardware is vital
for mainstream apps                      Lead in the delivery of cloud services

Interoperability between public and      Power the worlds largest clouds,
private clouds will be key               extending the knowledge and expertise
                                         developed there into the worlds leading
Cloud services will be as important      private clouds
as infrastructure
                                         Assist our customers in transitioning
                                         from legacy, proprietary data centers to
                                         next generation clouds & data centers

                                         Further the adoption of clouds as a
                                         underpinning green technology

                                                                                    15




 YOUR ACTION PLAN

                                               ices
                                       d  serv ore
                                  clou        m
                       a rt the build aise
                   • St rney to nterpr
                     jou ient E               hing
                                                    -
                       Effic            v eryt d
                                  z e e ou             ation
                            tuali e for cl virtualiz
                      • Vir selin s is
                         ba nomic ore                     ng
                                                    tarti
                          eco is the c y and s
                            – it petenc
                             com t                        s-a-
                                                     re-a
                              poin            oftw
                                                   a
                                      ra ge s IT          duce
                             •  Leve ice for nt – Re and
                                 serv ageme re TCO ith
                                  man structu ience w
                                   infrad exper ces
                                    buil d servi                er in
                                         u                 , lay oud
                                                     lized cl
                                                                                    16
                                     clo
START ON YOUR JOURNEY TO
ENTERPRISE EFFICIENCY

Datacenter optimization services               Cloud Storage solution
 – X86 migration & standardization              – Email archiving and cloud storage
 – Virtual infrastructure plan, design and     Data Center Solutions and
   implementation services”                    Infrastructure
 – Datacenter storage optimization             Servers and Storage solutions
 – DC facility remediation and consolidation   designed for Virtualization
   services                                     – PowerEdge
SaaS IT management services                     – EqualLogic storage
 – Desktop Management                           – OpenManage

 – Server monitoring & Management              Best Practices
 – Data backup reporting and management         – Cloud readiness assessment
 – Virtual environment Management &             – Scale out cloud hardware with reference
   Optimization                                   architectures
 – Disaster recovery and Crisis Management




                                                                                            17




                                      BACKUP




                                                                                            18
WHY ARE CIOS ATTRACTED TO
CLOUD SERVICES?
     BEYOND BREAK-THROUGH INFRASTRUCTURE SCALABILITY,
      CLOUD SERVICES REDUCE OVERALL IT COSTS, IMPROVE
         FLEXIBILITY, DELIVER AN EFFICIENT ENTERPRISE

                           • Reduces IT budget by 10-15%, Capex and Opex
   Reduce IT Costs         • Cuts application-related operational costs up to 50%
   for Applications
                           • Cuts application provisioning times as much as 96%



                           • Shifts cost/benefit decisions to the business
  Improve Flexibility
                           • Rapidly scales-up or scales-down capacity
   for The Business
                           • Minimizes upfront investment requirements and risk



                           • Leverages cloud scale to release datacenter capacity
  Drive Scale Based
                           • Enables shift of resources from lights-on to strategic
 Enterprise Efficiency
                           • Drives green agenda for energy efficiency in the DC


                                                                                      19




ASSESSING THE CLOUD
PERSPECTIVE
Migrating traditional in‐sourced IT service workloads to Cloud Services requires a 
        careful assessment based on application workload characteristics

                                                What makes a good cloud
                                                    app workload?
  Business                Can I leverage a
Requirements              Cloud Service?        • Scale-out app

                                                • Runs well in virtualized
                                                  environment

                           Is the application   • Not tied to legacy systems
  Workloads                      suitable
                             for the Cloud?
                                                • Latency tolerant

                                                • New / modern app
                                                  development
Architecture              Is the deployment
                              optimized?




                                                                                      20

								
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