Game theoretic approaches for Cloud Computing

Game-theoretic Approaches for
Modeling Cloud Environments
Presented by:
Ganesh Neelakanta Iyer

CCWS -2012, Coimbatore Institute of Technology,
Coimbatore, 9-August-2012

Wednesday, August 8, 12

About Me
Three years of Industry work experience in Bangalore, India
Finished masters from National University of Singapore in 2008.
Submitted PhD thesis under the guidance of A/Prof. Bharadwaj Veeravalli: August 2012

Research interests: Cloud computing, Game theory, Wireless Networks, Pricing

Personal Interests: Kathakali, Teaching, Traveling, Photography, Cooking

Website: http://ganeshniyer.com

2
©All Rights Reserved, Ganesh Neelakanta Iyer August 2012

Outline

• Overview of Cloud Computing and Major Challenges

• Overview of Game Theory

• Resource Allocation in Cloud - Bargaining theory

• Multiple Cloud Orchestration - Continuous Double Auctions

• Revenue Maximization on Mobile Clouds - Coalitional game theory

• Cloud Infrastructure Robustness and Security - Non-cooperative games

• Conclusions

3

Cloud Computing - A vision to reality

A quarter century ago, John Gage (Sun
Microsystems) made the prophetic
statement that:

“The network is the computer.”

Twenty-five years later, the advent of Cloud
Computing has finally made this a reality.
http://blog.industrysoftware.automation.siemens.com/blog/tag/john-gage/
http://historyofinformation.com/images/eniac.png
http://cloudcomputingcompaniesnow.com

http://www.tmforum.org/CloudServicesBrokerage/10617/home.html
4

Definition of Cloud Computing

NIST defines Cloud Computing as1:

“Cloud computing is a model for enabling ubiquitous,
convenient, on-demand network access to a shared pool of
configurable computing resources (e.g., networks, servers,
storage, applications, and services) that can be rapidly
provisioned and released with minimal management effort or
service provider interaction.”

http://cloudcomputingcompaniesnow.com/

[1] P. Mell and T. Grance. The NIST definition of cloud computing. NIST Special Publication 800-145, 2011.
5

Gartner Hype cycle for
Emerging Technologies: 2009-2011

http://www.gartner.com

6

Gartner Hype cycle for 2009
Emerging Technologies:
2009-2011

2011

2010

http://www.gartner.com

7

Characteristics of Cloud...

8


Elastic Computing

8


On-demand availability

Elastic Computing

8



Pay-as-you-go
Elastic Computing

8



Pay-as-you-go
Elastic Computing

Do your business

8


Different Services
Pay-as-you-go
Elastic Computing

Do your business

8

Challenges in moving into the Cloud

http://www.accenture.com/us-en/outlook/Pages/outlook-online-2011-challenges-cloud-computing.aspx
9


• Which CSP best matches my requirement?

9



• How secure is to move my data/job into a Cloud?

9




• How trust worthy are the CSPs?

9




• How trust worthy are the CSPs?

• How easy is to deal with lock-in?

9

Challenges faced by the providers

10


• How to offer the right price to increase the revenue?

10



• How to manage the resources efﬁciently?

10




• How do I know the behavior of my competitors?

10




• How do I know the behavior of my competitors?

• How to manage mobile applications on Mobile Clouds?

10

Rafﬂes Place, Singapore

Overview of
Game Theory ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012
11


Game Theory

• Study of how people interact and make decisions

• “…Game Theory is designed to address situations in which the outcome of a
person’s decision depends not just on how they choose among several
options, but also on the choices made by the people they are interacting
with…”

• The study of strategic interactions among economic (rational) agents and
the outcomes with respect to the preferences (or utilities) of those agents

12

What is a game?

13

What is a game?

A Game consists of
at least two players
a set of strategies for each player
a preference relation over possible outcomes

13

What is a game?

A Game consists of

Player is general entity
individual, company, nation, protocol, animal, etc

13

What is a game?

A Game consists of


Strategies
actions which a player chooses to follow

13

What is a game?

A Game consists of


Strategies

Outcome
determined by mutual choice of strategies

13

What is a game?

A Game consists of


Strategies

Outcome
determined by mutual choice of strategies

Preference relation
modeled as utility (payoff) over set of outcomes

13

Game Theory:
Applications

• Economics: Oligopoly markets, Mergers
and acquisitions pricing, auctions

• Political Science: fair division, public
choice, political economy

• Biology: modeling competition between
tumor and normal cells, Foraging bees

• Sports coaching staffs: run vs pass or
pitch fast balls vs sliders

• Computer Science: Distributed systems,
Computer Networks, AI, scheduling http://customergauge.com/wordpress/wp-content/uploads/2008/10/power_retailers_oligopoly.jpg
http://cricketradius.com/wp-content/uploads/2011/11/fast-bowling.jpg

14

Prisoner’s Dilemma

• Two suspects arrested for a crime

• Prisoners decide whether to confess or not to confess

• If both confess, both sentenced to 3 months of jail

• If both do not confess, then both will be sentenced to 1 month of jail

• If one confesses and the other does not, then the confessor gets freed (0
months of jail) and the non-confessor sentenced to 9 months of jail

• What should each prisoner do?

15

Prisoner’s Dilemma: Revisited

16



Prisoner 1

Prisoner 2

16



• Prisoners decide whether to confess
or not to confess Prisoner 1

Not
Confess

Prisoner 2
Confess

Confess

Not
Confess

16




Not
• If both confess, both sentenced to 3 Confess

Prisoner 2
Confess
months of jail
Confess -3,-3

Not
Confess

16




Not

Prisoner 2
Confess
months of jail
Confess -3,-3
• If both do not confess, then both will
Not
be sentenced to 1 month of jail -1,-1
Confess

16




Not

Prisoner 2
Confess
months of jail
Confess -3,-3 0,-9
Not
be sentenced to 1 month of jail -9,0 -1,-1
Confess

• If one confesses and the other does
not, then the confessor gets freed (0
months of jail) and the non-confessor
sentenced to 9 months of jail

16




Not

Prisoner 2
Confess
months of jail
Confess -3,-3 0,-9
Not
be sentenced to 1 month of jail -9,0 -1,-1
Confess

• If one confesses and the other does
not, then the confessor gets freed (0
months of jail) and the non-confessor
sentenced to 9 months of jail

• What should each prisoner do?

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Prisoner’s Dilemma: Nash Equilibrium

• Each player’s predicted strategy is
the best response to the predicted Prisoner 1
strategies of other players
Not
Confess

Prisoner 2
Confess
• No incentive to deviate unilaterally
Confess -3,-3 0,-9

Not
-9,0 -1,-1
• Strategically stable or self-enforcing Confess

17

Rock-paper-scissors game

• A probability distribution over the pure strategies of the game

• Rock-paper-scissors game

• Each player simultaneously forms his or her hand into the shape of either a
rock, a piece of paper, or a pair of scissors

• Rule: rock beats (breaks) scissors, scissors beats (cuts) paper, and paper
beats (covers) rock

• No pure strategy Nash equilibrium

• One mixed strategy Nash equilibrium – each player plays rock, paper and
scissors each with 1/3 probability

18

Resource Allocation
in Cloud Computing
Envirnments

Ulu Watu, Bali, Indonesia 19

Resource Allocation in Cloud

Problem
under
considera0on
is
“Resource
Alloca,on
and

Pricing
Strategies
for
tasks
in
Compute
Cloud
Environments”.

We
employ
“Axioma,c
Bargaining
Approaches
to
derive
the

op,mal
solu,on
for
alloca,ng
resources
in
a
Compute
Cloud”.

•
Nash
Bargaining
Solu0on
(NBS)
and
Raiﬀa
Bargaining
Solu0on
(RBS)
•

Handling
various
parameters
such
as
deadline,
budget

constraints
etc
•

Introduc0on
of
asymmetric
pricing
scheme
for
CSPs
•

Handling
auto-‐elas0city,
fairness

Reference: Ganesh Neelakanta Iyer and Bharadwaj Veeravalli, “On the Resource Allocation and Pricing Strategies in Compute
Clouds Using Bargaining Approaches”, IEEE International Conference on Networks (ICON 2011), Singapore, December 2011.

20


1
Task(1( 2

Internet( Task(1(
Resource( i Compute(
Allocator( Node(i"
Task(T(

Rtot

Compute(Cloud(Environment(
Suitable
for
both
independent
tasks,
Bag-‐of-‐Tasks
(BoT)
and
tasks
from
workﬂow
schemes

Assump?on:
Tasks
are
known
apriori,
but
it
can
handle
real-‐?me
arrival
of
tasks

Coopera?ve
game
theory
framework
21

Axiomatic Bargaining Approaches

Good to derive fair and Pareto-optimal solution

Pareto optimal: It is impossible to increase the allocation of a connection without strictly decreasing
another one.

It assumes some desirable and fair properties, deﬁned using axioms, about the outcome of the
resource bargaining process.

Two approaches:

Nash Bargaining Solution (NBS)

Raiffa-Kalai-Smorodinsky Bargaining Solution (RBS)

22

Nash Bargaining Solution (NBS)

Solving, we obtain

23


Raiffa-Kalai-Smorodinsky Bargaining Solution (RBS)

Solving, we obtain

24

Performance evaluation:

Deadline based Real-time task arrival

Clouds Using Bargaining Approaches”, IEEE International Conference on Networks (ICON 2011), Singapore, December 2011. 25


Pricing Analysis
Symmetric:

price/resource = $0.75

Asymmetric:

A value in [0.5,1.0]

Tasks specify maximum budget

Current CSPs follow symmetric pricing schemes (EC2, Azure)

Introducing asymmetric pricing approach, which would give adequate ﬂexibility in managing the
resources as well as generating more revenue.

26


Observations:
•Allocation in NBS and RBS depends on bargaining power and is within the Pareto boundary
•When NBS maximizes the product of the gain of all players, RBS in addition considers how much
other players gave up

•NBS efﬁciently utilizes maximum number of resources
•RBS indirectly maps to an energy efﬁcient solution by meeting the deadline with less number of
resources.

•RBS effectively handles auto-elasticity and task dynamics
•NBS is shown to be suitable for shorter deadline tasks whereas RBS is for handling tasks of
longer deadline tasks.

•Asymmetric pricing scheme

27

Multiple Cloud Orchestration

Melaca, Malaysia
28

Cloud Orchestration

• Relates to the connectivity of IT and business process levels between Cloud
environments.

• As cloud emerges as a competitive sourcing strategy, a demand is clearly arising for the
integration of Cloud environments to create an end-to-end managed landscape of
cloud-based functions.

• Beneﬁts include

• Helps users to choose the best service they are looking for (for example the cheapest or the
best email provider)

• Helps providers to offer better services and adapt to market conditions quickly

• Ability to create a best of breed service-based environment in which a change of provider
does not break the business process

Reference: Ganesh Neelakanta Iyer, Bharadwaj Veeravalli and Ramkumar Chandrasekaran, “Broker-agent based Cloud Service Arbitrage
Mechanisms using Sealed-bid Double Auctions and Incentives”, Journal of Network and Computer Applications (JNCA), Elsevier 2012

http://lookout.atos.net/en-us/enabling_information_technologies/cloud_orchestration/default.htm 29

Cloud Brokers

• Cloud Broker plays an intermediary role to help customers locate the best
and the most cost-effective CSP for the customer needs

• One stop solution for Multiple Cloud Orchestration (aggregating, integrating,
customizing and governing Cloud services for SMEs and large enterprises)

• Advantages are cost savings, information availability and market adaptation

• As the number of CSPs continues to grow, a single interface (Broker) for
information, combined with service, could be compelling to companies that
prefer to spend more time with their Clouds than doing the research.

• Some ways to implement :- Auctions, Incentives

30

ARSYS
BT
Flexiant Identity Brokerage
Entitlement Mgmt.
IDbt IDarsys
IDflex
Policy Enforcement

Cloud Broker Usage Monitoring,
Reporting

Network defense,
Platform security
Admin

Programmer
Users Service Provider
Users http://www.optimis-project.eu/

Figure 1: Cloud Broker ecosystem showing the players involved.

Typical Cloud Broker
© OPTIMIS Consortium Page 3
ecosystem showing the The Broker helps to connect the
providers and users
players involved
31

Auction Theory
Auc$ons(

Single( Double(
Outcry( Sealed=bid( Outcry( Sealed=bid(

English( Dutch( Vickery( First(Price( Call(Market( CDA(

• In economic theory, an auction may refer to any mechanism or set of trading rules for exchange.

• English Auction:open ascending price auction.
• Dutch Auction:open descending price auction.
• Vickery Auction: Sealed-bid second price auction
• First Price auction: Highest bidder pays the price they submitted
• Call Market: Mediator determines market clearing price based on number of bid and ask orders.
• CDA: Continuous Double Auctions

32

Sealed-bid Continuous Double Auctions

CDA: Continuous Double Auctions

The Continuous Double Auction (CDA) is a mechanism to match buyers and sellers
of a particular good, and to determine the prices at which trades are executed.
Instead, in non-institutional trade-determination, buyers and sellers can choose to
accept a bid or ask, and then update their allocation, at any point in time.

33

Sealed-bid Continuous Double Auctions

Comparison of revenue

Hit Ratio is the ratio of the
number of successful auctions
to the total number of auctions.

Fair revenue for all users

Lowers user expenditure at the
expense of response-time for
choosing appropriate CSP.


34

Revenue Maximization
in Mobile Clouds

From my home, Thodupuzha, Kerala
35

Mobile Cloud Environments

Mobile cloud computing combines wireless access service and cloud
computing to improve the performance of mobile applications.

Mobile applications can ofﬂoad some computing modules (such as online
gaming) to be executed on a powerful server in a cloud.

A scenario where multiple CSPs cooperatively offer mobile services to users.

Coalition games

Reference: Dusit Niyato, Ping Wang, Ekram Hossain, Walid Saad, and Zhu Han, “Game Theoretic Modeling of Cooperation among
Service Providers in Mobile Cloud Computing Environments”, IEEE Wireless Communications and Networking Conference, 2012

36

Coalition Game: An example

Players = {1,2,3}

All nonempty subset (named as coalition) {1}, {2}, {3}, {1,2}, {1,3}, {2,3}, {1,2,3}

A cost function c related to all coalitions. c({1}) = v1, c({2}) = v2, ..., c({1,2,3}) = v7

c(S) is the amount that the players in the coalition S have to pay collectively in
order to have access to a service.


37

Coalition Game: Core

•The problem is to ﬁnd the core of this coalition game.

•Core is a cost distribution of the grand coalition such that no other coalition
can obtain an outcome better for all its members than the current assignment.

•There may not exist any core.

•Emptiness of the core.

•There may exist many cores.

•Some players would unhappy with the cost allocation.

38

Coalition Game: Example

We want to ﬁnd the cost allocation {x1, x2, x3} such that

x1+x2+x3 = c({1,2,3})

x1 ≦ c({1})
x2 ≦ c({2})
x3 ≦ c({3})
x1+x2 ≦ c({1, 2})
x1+x3 ≦ c({1, 3})
x2+x3 ≦ c({2, 3})

Given a solution in the core, there is no incentive for a player to leave the grand
coalition.

39

Mobile Clouds and Coalition Game

•Mobile applications are supported by the mobile CSPs in which the radio
(bandwidth) and computing (servers) resources are reserved for the users.

•To improve resource utilization and revenue, mobile CSPs cooperate to form a
coalition and create a resource pool for users running mobile applications.

•Revenue sharing among the CSPs is based on a coalitional game.

•With a coalition, providers can optimize the capacity expansion, which
determines the reserved bandwidth and servers for a resource pool.

•The objective of provider is to maximize the proﬁt from supporting mobile
applications through a resource pool.

40

Phang Nga Bay, Thailand

Cyber-Physical Systems
Robustness
41

Attack and defense in cyber-physical systems

Reference: Chris Y. T. Ma, Nageswara S. V. Rao and David K. Y. Yau, “A Game Theoretic Study of Attack and Defense in Cyber-Physical
Systems”, The First IEEE International Workshop on Cyber-Physical Networking Systems, 2011

42


Cyber physical systems :- Systems which need cyber and physical components to
function.


42


function.

Examples: Cloud Computing systems, Sensor network systems, Communication networks


42


function.


Players: Defenders aim to keep the system functioning and the attacker aims to disrupt.
Actions represent the resources deployed by the defender and disrupted by the attacker,
respectively.


42


function.


respectively.

Costs and beneﬁts: Each player has a payoff function U consisting of two parts: beneﬁt B
and/or cost C. The attacker incurs a cost in launching an attack, and the defender incurs a
cost in deploying the resources. In a game, either player will aim to maximize its payoff
given the other player's best strategy.


42


function.


respectively.

Costs and beneﬁts: Each player has a payoff function U consisting of two parts: beneﬁt B
and/or cost C. The attacker incurs a cost in launching an attack, and the defender incurs a
cost in deploying the resources. In a game, either player will aim to maximize its payoff
given the other player's best strategy.

Existence and solutions of pure and mixed-strategy Nash Equilibria can be found


42

Water-puppetry, Vietnam

Summary... 43

To Summarize...

44

To Summarize...
• Bargaining theory, Auction Theory, Coalition games, Non-cooperative games
etc.

44

To Summarize...
etc.
• Resource allocation, Cloud orchestration, Robustness, Security, Mobile
Clouds etc.

44

To Summarize...
etc.
Clouds etc.
• Different aspects of Game Theory can be applied for tackling various
problems in Cloud Computing environments

44

To Summarize...
etc.
Clouds etc.

• Topics not covered (much more than what is discussed)

44

To Summarize...
etc.
Clouds etc.


Repeated games, Dynamic games, Bayesian games, Combinatorial
auctions .......

44

To Summarize...
etc.
Clouds etc.


Repeated games, Dynamic games, Bayesian games, Combinatorial
auctions .......

Energy minimization, Reliability, Trust and Risk modeling in Clouds......

44

THANK YOU!

Game theoretic approaches for Cloud Computing

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