cost-efficient deployment of distributed software services · 2015-02-17 · cost-efficient...

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PhD Thesis defense, 12.05.2011Máté J. Csorba

Máté J. Csorba

[email protected]

Cost-efficient deployment of

distributed software services

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o The deployment problem

• MAPE, notation, target environment

o Intro to CEAS

o How to encode pheromones

o Scenario 1 : Collaborating software components

o Scenario 2 : Replication management

o Scenario 3 : Cloud computing

o Validation using ILP

o Summary & not covered

Short introduction & contents

Cost-efficient deployment of distributed software services

Cost functions

Bio-inspired decentralized optimization strategies

Cross-validation

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The deployment problem and its complexity

#S

#N

Deployment

mapping

Focus on:

- load-balancing,

- remote comm.,

- replica management,

- and financial costs

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o REQs 1 – 6 :

1. Efficiency

2. Robustness

3. Autonomicity

4. Adaptivity

5. Scalability

6. Generality &

Extensibility

In the context of the MAPE cycle

Deployment

mapping

#

S

#N

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Target environment

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o Constants:

• N = {n1, n2};

• D = {d1}; d1 = {n1, n2};

• S = {S1}; C = {ci, cj}; K = {kk};

o Variables:

• D1 = {d1}; H1 = {n1, n2};

• L1 = {30 => n1, 20 => n2};

• M1 = {ci => n1, cj => n2};

Notation example

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Cross-Entropy Ant System

o Originally: robust, distributed path management in network nodes

• Schoonderwoerd et al. – ant based routing scheme (1997)

• Rubinstein – Cross-Entropy for stochastic optimisation (1999)

• Helvik & Wittner – distributed, autoregressive variant (2003)

o 2 types of ants:

• Explorer => cover up the search space, i.e. do random search

• Normal => optimize mapping

o 2 phases in an iteration

• Forward search => ants looking for a deployment mapping

• Backtracking => ants update the pheromone database

Introduction to CEAS

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cost F()r updatetemp(cost)

CEAS deployment strategies – how it works

n1

n2

n3

n4

n5

n6

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select : mn,rget : ln,r

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Fwd. searchPheromone update

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o Parallel nests

• In joining / splitting networks

o Load-sampling

• To facilitate load-balancing and gather information

o Guided random hop-selection

• Taboo-listing

• Cover domains and then nodes

o Binding & release of instances / maintenance

• Ease convergence

• Conditional

CEAS – auxiliary adjustments

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CEAS – in a class–diagram

o Implemented in Simula/DEMOS

o Peersim (Java) ongoing

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Pheromone encodings

o Tables distributed over the possible hosts

Instances

Ye

s / N

o

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o Deployment of collaborating components – UML 2.0 collaborations

o PAPER A – C

o Target

• Load-balancing (i.e. min. deviation from average load)

• Minimization of remote communication

Scenario 1 : Collaborating software components

o E.g.

S1 : a simple client-server

S2 : authorization and

authentication server

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with global load-balance estimate

Scenario 1 (cont’d)

and communication cost

o Deployment cost function

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27 = max DB size

exploration

117 =

optimal cost


o Example run from PAPER A

Iterations

Co

st

/ D

B s

ize

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where

o Deployment costs for multiple services

• Eliminating global knowledge (~T )

• Multiplicative (~better convergence)

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o Examples with 3 services from PAPER C


Node error & repair New node

Iterations

Co

st

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o Deployment of replicas

o PAPER D – E

o Target

• Load-balancing

• Node- & domain-disjointness

Scenario 2 : Replication management

o E.g.

S3 : a simple

DB replica

S4 : 3-way

replication

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where

and

Domain-disjointness Node-disjointness Load-balancing

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o Example from PAPER E

• 65 replicas, 11 nodes in 5 domains

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• Costs of service S10

• 2000 explorer iterations

• Splitting of d1 after 4000 iterations

• Merging the regions after 5000 iterations


o Example from PAPER E

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o Deployment of VM replicas in a cloud-like environment

o PAPER F

o Target

• Load-balancing

• Node- & domain-disjointness

• Minimize financial costs

Scenario 3 : Cloud computing

• 5 private (costs 0), 2

public clouds (costs 1

and 10)

• 18 clusters, 130

nodes (cluster sizes

of 10 or 5 nodes)

• 5 x 25 services,

replication level 5,

=> 625 VMs

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o where the weighting function is defined as

o and the sum of financial costs is

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o Combination of cost function terms in F4


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o Results of 100 simulation runs each setting

• No cluster weights

• Linear weighting

• Exponential weighting


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Validation using ILP

o PAPER G

o ILP (AMPL/CPLEX)

Examples

Cost

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Validation using ILP (cont’d)

o ILP built with objective:

o and corresponding constraints:

o with the variables:

o Cross-validation of exact solutions vs. heuristics

• Global-view vs. decentralized

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Impediments:

• global view in memory,

• no support for dynamicity,

• mi,j size explosion

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Summary

o CEAS

• Heuristic optimization

• Decentralized algorithm, ant-like agents

o Application domains

• Deployment of collaborating sw components

• Deployment of replicas

• VM instance placement in private and public clusters

1) Cost functions

2) Heuristic algorithms

3) Modeling scenarios

4) Cross-validation

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Publications

o Category I. – Validation of mappings

o Category II. – Load-balancing and communication costs

o Category III. – Load-balancing and replication costs

o Category IV. – Cluster costs

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o Translation of the simulations to PeerSim

o Additional ILP models

o The power saving dimension

o Migration costs

o Scaling and larger problem sizes

o Incremental scaling

o Coordination and designated nest selection

o Better and broader service models

o Introducing clients

o Costs derived from service models

o Deployment diagrams

o Feedback to functional design

o Experiments using a middleware platform, e.g. DARM

Not covered, not implemented, not yet given up

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Thank You for your attention!*

* and many thanks to

Poul E. Heegaard,

Peter Herrmann, and

Hein Meling

cost-efficient deployment of distributed software services · 2015-02-17 · cost-efficient...

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