game theoretic approaches for cloud computing

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

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

Wednesday, August 8, 12

©All Rights Reserved, Ganesh Neelakanta Iyer August 2012

About Me

2

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


http://ganeshniyer.com

http://ganeshniyer.com


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



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

http://blog.industrysoftware.automation.siemens.com/blog/tag/john-gage/http://historyofinformation.com/images/eniac.png

http://cloudcomputingcompaniesnow.com

Cloud Computing - A vision to reality



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.”

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

http://cloudcomputingcompaniesnow.com/





Gartner Hype cycle for Emerging Technologies: 2009-2011

http://www.gartner.com





Gartner Hype cycle for Emerging Technologies:

2009-2011

2009

2011

2010






Characteristics of Cloud...

8




8

Elastic Computing




8

Elastic Computing

On-demand availability




8

Elastic ComputingPay-as-you-go





8



Do your business




8


Different Services


Do your business



Challenges in moving into the Cloud

9

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






• 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



Overview of Game Theory 11

Raffles Place, Singapore



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 ofat least two players a set of strategies for each playera preference relation over possible outcomes

13



What is a game?


Player is general entityindividual, company, nation, protocol, animal, etc

13



What is a game?



Strategiesactions which a player chooses to follow

13



What is a game?




Outcomedetermined by mutual choice of strategies

13



What is a game?




Outcomedetermined by mutual choice of strategies

Preference relationmodeled 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

14

http://customergauge.com/wordpress/wp-content/uploads/2008/10/power_retailers_oligopoly.jpghttp://cricketradius.com/wp-content/uploads/2011/11/fast-bowling.jpg


http://customergauge.com/wordpress/wp-content/uploads/2008/10/power_retailers_oligopoly.jpg





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





16

Pris

oner

2

Prisoner 1






16

Confess Not Confess

Confess

Not Confess

Pris

oner

2

Prisoner 1







16

Confess Not Confess

Confess

Not Confess

Pris

oner

2

Prisoner 1

-3,-3








16

Confess Not Confess

Confess

Not Confess

Pris

oner

2

Prisoner 1

-3,-3

-1,-1









16

Confess Not Confess

Confess

Not Confess

Pris

oner

2

Prisoner 1

-3,-3

-1,-1-9,0

0,-9









• What should each prisoner do?

16

Confess Not Confess

Confess

Not Confess

Pris

oner

2

Prisoner 1

-3,-3

-1,-1-9,0

0,-9



Prisoner’s Dilemma: Nash Equilibrium

• Each player’s predicted strategy is the best response to the predicted strategies of other players

• No incentive to deviate unilaterally

• Strategically stable or self-enforcing

17

Confess Not Confess

Confess

Not Confess

Pris

oner

2

Prisoner 1

-3,-3-1,-1-9,0

0,-9



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

19Ulu Watu, Bali, Indonesia



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

20

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.




21

Resource(Allocator(

1 2

i

Rtot

Compute(Node(i"

Internet(Task(1(

Task(1(

Task(T(

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




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)

23

Solving, we obtain





Raiffa-Kalai-Smorodinsky Bargaining Solution (RBS)

24

Solving, we obtain





25

Performance evaluation:

Deadline based Real-time task arrival





26

Pricing AnalysisSymmetric:

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.





27

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




Multiple Cloud Orchestration

28Melaca, MalaysiaWednesday, August 8, 12


Cloud Orchestration

29

• 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

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

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





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




Typical Cloud Broker ecosystem showing the

players involved The Broker helps to connect the providers and users

31

Requirements and Architecture of a Cloud Broker

© OPTIMIS Consortium Page 3 of 27

3 Use Case Scenario The use of cloud based services in order to provide online services to customers is expected to

bring in a new era in the area of ICT infrastructure and delivery. In a simplistic scenario, a

service provider (SP) decides to host the service it wants to provide to an end customer on the

infrastructure provided by an infrastructure provider (IP). This is the current state of art in the

use of the cloud services and is considered as a simple scenario because of the limited

flexibility available to the SP to split its services into components and deploy them into

infrastructures provided by multiple IPs.

However it is expected that the growth and maturity of the cloud offerings would necessitate

the building of delivery models that will allow use of multiple IPs for the hosting of multiple

components in a single service. It is anticipated that the evolution of such a delivery model

would then lead to the formation of a service entity know as the Cloud Broker that provides

the SPs, at minimum, with a mechanism to choose a group of IPs from a list of available ones

for deploying various components of its service based on various parameters. This Work

Package considers the concept of this Cloud Broker in detail.

3.1 Storyboard

The main aim of WP 6.4 is to showcase the use of the OPTIMIS toolkit to build up a cloud

services brokerage ecosystem that allows the Service Provider (SP) to use multiple

Infrastructure services provided by respective Infrastructure Providers (IPs) by integrating

them in a way to as to implement a singular service or process. The aim is to utilize the various

components of the toolkit and thus leverage the work done in other work packages of the

project. While the initial WP’s Description of work (DoW) describes three scenario setups with

varying levels of complexity, in this deliverable we mould the presentation to concentrate on

the scenario specific to the cloud broker and use the other two scenarios as stepping stones

and stretch goals of the WP.

The Cloud Broker (CB) can be considered as an architectural, business and IT operations model

that enables the delivery and management of different cloud services in a framework that

provides consistent provisioning, security, administration and other support. In this use case,

an SP planning to deploy a service in the cloud approaches a CB with a given set of functional

requirements and constraints (including costs, performance etc.) with the aim of selecting the

best available match of IPs in terms of the functional requirements as well as other variable

constraints like cost, SLA parameters and other non-functional requirements like audit,

compliance and security capabilities.

Programmer

Cloud Broker

IDflex

IDbt

Flexiant

Users Users

Identity Brokerage

Entitlement Mgmt.

Policy Enforcement

Usage Monitoring,

Reporting

Admin

BT

Network defense,

Platform security

Service Provider

ARSYS

IDarsys

Figure 1: Cloud Broker ecosystem showing the players involved.

http://www.optimis-project.eu/


http://www.optimis-project.eu

http://www.optimis-project.eu


Auction Theory

• 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

Auc$ons(

Single( Double(

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

Outcry( Outcry(Sealed=bid( Sealed=bid(

Vickery(


http://en.wikipedia.org/wiki/Economic_theory

http://en.wikipedia.org/wiki/Economic_theory


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.

33Reference: 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



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

35From my home, Thodupuzha, Kerala



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

36

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



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




Cyber-Physical SystemsRobustness

41

Phang Nga Bay, Thailand



Attack and defense in cyber-physical systems

42

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




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

42






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

42







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








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








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




Summary...43

Water-puppetry, Vietnam



To Summarize...

44



To Summarize...

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

44



To Summarize...


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

44



To Summarize...



• Different aspects of Game Theory can be applied for tackling various problems in Cloud Computing environments

44



To Summarize...




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

44



To Summarize...





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

44



To Summarize...





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

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

44



THANK YOU!Wednesday, August 8, 12

game theoretic approaches for cloud computing

Education