Game theoretic approaches for Cloud Computing
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Transcript of 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
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©All Rights Reserved, Ganesh Neelakanta Iyer August 2012
About Me
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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
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
Wednesday, August 8, 12
©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
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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
Wednesday, August 8, 12
©All Rights Reserved, Ganesh Neelakanta Iyer August 20124
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
Wednesday, August 8, 12
©All Rights Reserved, Ganesh Neelakanta Iyer August 20125
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/
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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/
Wednesday, August 8, 12
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Gartner Hype cycle for Emerging Technologies: 2009-2011
http://www.gartner.com
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Gartner Hype cycle for Emerging Technologies: 2009-2011
http://www.gartner.com
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Gartner Hype cycle for Emerging Technologies: 2009-2011
http://www.gartner.com
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Gartner Hype cycle for Emerging Technologies:
2009-2011
2009
2011
2010
http://www.gartner.com
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Characteristics of Cloud...
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Characteristics of Cloud...
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Elastic Computing
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Characteristics of Cloud...
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Elastic Computing
On-demand availability
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Characteristics of Cloud...
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Elastic ComputingPay-as-you-go
On-demand availability
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Characteristics of Cloud...
8
Elastic ComputingPay-as-you-go
On-demand availability
Do your business
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Characteristics of Cloud...
8
Elastic ComputingPay-as-you-go
Different Services
On-demand availability
Do your business
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Challenges in moving into the Cloud
9
http://www.accenture.com/us-en/outlook/Pages/outlook-online-2011-challenges-cloud-computing.aspx
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Challenges in moving into the Cloud
• Which CSP best matches my requirement?
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http://www.accenture.com/us-en/outlook/Pages/outlook-online-2011-challenges-cloud-computing.aspx
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Challenges in moving into the Cloud
• Which CSP best matches my requirement?
• How secure is to move my data/job into a Cloud?
9
http://www.accenture.com/us-en/outlook/Pages/outlook-online-2011-challenges-cloud-computing.aspx
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Challenges in moving into the Cloud
• Which CSP best matches my requirement?
• How secure is to move my data/job into a Cloud?
• How trust worthy are the CSPs?
9
http://www.accenture.com/us-en/outlook/Pages/outlook-online-2011-challenges-cloud-computing.aspx
Wednesday, August 8, 12
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Challenges in moving into the Cloud
• Which CSP best matches my requirement?
• How secure is to move my data/job into a Cloud?
• How trust worthy are the CSPs?
• How easy is to deal with lock-in?
9
http://www.accenture.com/us-en/outlook/Pages/outlook-online-2011-challenges-cloud-computing.aspx
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Challenges faced by the providers
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Challenges faced by the providers
• How to offer the right price to increase the revenue?
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Challenges faced by the providers
• How to offer the right price to increase the revenue?
• How to manage the resources efficiently?
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Challenges faced by the providers
• How to offer the right price to increase the revenue?
• How to manage the resources efficiently?
• How do I know the behavior of my competitors?
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Challenges faced by the providers
• How to offer the right price to increase the revenue?
• How to manage the resources efficiently?
• How do I know the behavior of my competitors?
• How to manage mobile applications on Mobile Clouds?
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Overview of Game Theory 11
Raffles Place, Singapore
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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
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What is a game?
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What is a game?
A Game consists ofat least two players a set of strategies for each playera preference relation over possible outcomes
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What is a game?
A Game consists ofat least two players a set of strategies for each playera preference relation over possible outcomes
Player is general entityindividual, company, nation, protocol, animal, etc
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What is a game?
A Game consists ofat least two players a set of strategies for each playera preference relation over possible outcomes
Player is general entityindividual, company, nation, protocol, animal, etc
Strategiesactions which a player chooses to follow
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What is a game?
A Game consists ofat least two players a set of strategies for each playera preference relation over possible outcomes
Player is general entityindividual, company, nation, protocol, animal, etc
Strategiesactions which a player chooses to follow
Outcomedetermined by mutual choice of strategies
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What is a game?
A Game consists ofat least two players a set of strategies for each playera preference relation over possible outcomes
Player is general entityindividual, company, nation, protocol, animal, etc
Strategiesactions which a player chooses to follow
Outcomedetermined by mutual choice of strategies
Preference relationmodeled as utility (payoff) over set of outcomes
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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
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http://customergauge.com/wordpress/wp-content/uploads/2008/10/power_retailers_oligopoly.jpghttp://cricketradius.com/wp-content/uploads/2011/11/fast-bowling.jpg
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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?
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Prisoner’s Dilemma: Revisited
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Prisoner’s Dilemma: Revisited
• Two suspects arrested for a crime
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Pris
oner
2
Prisoner 1
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Prisoner’s Dilemma: Revisited
• Two suspects arrested for a crime
• Prisoners decide whether to confess or not to confess
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Confess Not Confess
Confess
Not Confess
Pris
oner
2
Prisoner 1
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Prisoner’s Dilemma: Revisited
• 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
16
Confess Not Confess
Confess
Not Confess
Pris
oner
2
Prisoner 1
-3,-3
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Prisoner’s Dilemma: Revisited
• 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
16
Confess Not Confess
Confess
Not Confess
Pris
oner
2
Prisoner 1
-3,-3
-1,-1
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Prisoner’s Dilemma: Revisited
• 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
16
Confess Not Confess
Confess
Not Confess
Pris
oner
2
Prisoner 1
-3,-3
-1,-1-9,0
0,-9
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Prisoner’s Dilemma: Revisited
• 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?
16
Confess Not Confess
Confess
Not Confess
Pris
oner
2
Prisoner 1
-3,-3
-1,-1-9,0
0,-9
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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
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Confess Not Confess
Confess
Not Confess
Pris
oner
2
Prisoner 1
-3,-3-1,-1-9,0
0,-9
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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
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Resource Allocation in Cloud Computing Envirnments
19Ulu Watu, Bali, Indonesia
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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
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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.
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Resource Allocation in Cloud
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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
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.
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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)
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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.
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Axiomatic Bargaining Approaches
Nash Bargaining Solution (NBS)
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Solving, we obtain
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.
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Axiomatic Bargaining Approaches
Raiffa-Kalai-Smorodinsky Bargaining Solution (RBS)
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Solving, we obtain
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.
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Resource Allocation in Cloud
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Performance evaluation:
Deadline based Real-time task arrival
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.
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Resource Allocation in Cloud
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.
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.
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Resource Allocation in Cloud
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
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.
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Multiple Cloud Orchestration
28Melaca, MalaysiaWednesday, August 8, 12
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Cloud Orchestration
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• 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
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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
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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
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Typical Cloud Broker ecosystem showing the
players involved The Broker helps to connect the providers and users
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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/
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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
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Auc$ons(
Single( Double(
English( Dutch( First(Price( Call(Market( CDA(
Outcry( Outcry(Sealed=bid( Sealed=bid(
Vickery(
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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
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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.
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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
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Revenue Maximization in Mobile Clouds
35From my home, Thodupuzha, Kerala
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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
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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
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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.
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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
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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.
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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
Wednesday, August 8, 12
©All Rights Reserved, Ganesh Neelakanta Iyer August 2012
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.
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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
Wednesday, August 8, 12
©All Rights Reserved, Ganesh Neelakanta Iyer August 2012
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.
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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
Wednesday, August 8, 12
©All Rights Reserved, Ganesh Neelakanta Iyer August 2012
Cyber-Physical SystemsRobustness
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Phang Nga Bay, Thailand
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©All Rights Reserved, Ganesh Neelakanta Iyer August 2012
Attack and defense in cyber-physical systems
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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
Wednesday, August 8, 12
©All Rights Reserved, Ganesh Neelakanta Iyer August 2012
Attack and defense in cyber-physical systems
Cyber physical systems :- Systems which need cyber and physical components to function.
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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
Wednesday, August 8, 12
©All Rights Reserved, Ganesh Neelakanta Iyer August 2012
Attack and defense in cyber-physical systems
Cyber physical systems :- Systems which need cyber and physical components to function.
Examples: Cloud Computing systems, Sensor network systems, Communication networks
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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
Wednesday, August 8, 12
©All Rights Reserved, Ganesh Neelakanta Iyer August 2012
Attack and defense in cyber-physical systems
Cyber physical systems :- Systems which need cyber and physical components to function.
Examples: Cloud Computing systems, Sensor network systems, Communication networks
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.
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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
Wednesday, August 8, 12
©All Rights Reserved, Ganesh Neelakanta Iyer August 2012
Attack and defense in cyber-physical systems
Cyber physical systems :- Systems which need cyber and physical components to function.
Examples: Cloud Computing systems, Sensor network systems, Communication networks
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.
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.
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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
Wednesday, August 8, 12
©All Rights Reserved, Ganesh Neelakanta Iyer August 2012
Attack and defense in cyber-physical systems
Cyber physical systems :- Systems which need cyber and physical components to function.
Examples: Cloud Computing systems, Sensor network systems, Communication networks
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.
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
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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
Wednesday, August 8, 12
©All Rights Reserved, Ganesh Neelakanta Iyer August 2012
Summary...43
Water-puppetry, Vietnam
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©All Rights Reserved, Ganesh Neelakanta Iyer August 2012
To Summarize...
44
Wednesday, August 8, 12
©All Rights Reserved, Ganesh Neelakanta Iyer August 2012
To Summarize...
• Bargaining theory, Auction Theory, Coalition games, Non-cooperative games etc.
44
Wednesday, August 8, 12
©All Rights Reserved, Ganesh Neelakanta Iyer August 2012
To Summarize...
• Bargaining theory, Auction Theory, Coalition games, Non-cooperative games etc.
• Resource allocation, Cloud orchestration, Robustness, Security, Mobile Clouds etc.
44
Wednesday, August 8, 12
©All Rights Reserved, Ganesh Neelakanta Iyer August 2012
To Summarize...
• Bargaining theory, Auction Theory, Coalition games, Non-cooperative games etc.
• Resource allocation, Cloud orchestration, Robustness, Security, Mobile Clouds etc.
• Different aspects of Game Theory can be applied for tackling various problems in Cloud Computing environments
44
Wednesday, August 8, 12
©All Rights Reserved, Ganesh Neelakanta Iyer August 2012
To Summarize...
• Bargaining theory, Auction Theory, Coalition games, Non-cooperative games etc.
• Resource allocation, Cloud orchestration, Robustness, Security, Mobile Clouds etc.
• Different aspects of Game Theory can be applied for tackling various problems in Cloud Computing environments
• Topics not covered (much more than what is discussed)
44
Wednesday, August 8, 12
©All Rights Reserved, Ganesh Neelakanta Iyer August 2012
To Summarize...
• Bargaining theory, Auction Theory, Coalition games, Non-cooperative games etc.
• Resource allocation, Cloud orchestration, Robustness, Security, Mobile Clouds etc.
• Different aspects of Game Theory can be applied for tackling various problems in Cloud Computing environments
• Topics not covered (much more than what is discussed)
Repeated games, Dynamic games, Bayesian games, Combinatorial auctions .......
44
Wednesday, August 8, 12
©All Rights Reserved, Ganesh Neelakanta Iyer August 2012
To Summarize...
• Bargaining theory, Auction Theory, Coalition games, Non-cooperative games etc.
• Resource allocation, Cloud orchestration, Robustness, Security, Mobile Clouds etc.
• Different aspects of Game Theory can be applied for tackling various problems in Cloud Computing environments
• Topics not covered (much more than what is discussed)
Repeated games, Dynamic games, Bayesian games, Combinatorial auctions .......
Energy minimization, Reliability, Trust and Risk modeling in Clouds......
44
Wednesday, August 8, 12
©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
©All Rights Reserved, Ganesh Neelakanta Iyer August 2012
THANK YOU!Wednesday, August 8, 12