Game theoretic approaches for Cloud Computing
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Slides of the talk in Indo-US workshop on CCWS 2012

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Game theoretic approaches for Cloud Computing Presentation Transcript

  • 1. Game-theoretic Approaches for Modeling Cloud Environments Presented by: Ganesh Neelakanta Iyer CCWS -2012, Coimbatore Institute of Technology, Coimbatore, 9-August-2012Wednesday, August 8, 12
  • 2. 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 2012Wednesday, August 8, 12
  • 3. 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 2012Wednesday, August 8, 12
  • 4. 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 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 5. 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 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 6. 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 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 7. 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 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 8. 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 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 9. Gartner Hype cycle for Emerging Technologies: 2009-2011 http://www.gartner.com 6 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 10. Gartner Hype cycle for Emerging Technologies: 2009-2011 http://www.gartner.com 6 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 11. Gartner Hype cycle for Emerging Technologies: 2009-2011 http://www.gartner.com 6 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 12. Gartner Hype cycle for 2009 Emerging Technologies: 2009-2011 2011 2010 http://www.gartner.com 7 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 13. Characteristics of Cloud... 8 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 14. Characteristics of Cloud... Elastic Computing 8 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 15. Characteristics of Cloud... On-demand availability Elastic Computing 8 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 16. Characteristics of Cloud... On-demand availability Pay-as-you-go Elastic Computing 8 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 17. Characteristics of Cloud... On-demand availability Pay-as-you-go Elastic Computing Do your business 8 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 18. Characteristics of Cloud... On-demand availability Different Services Pay-as-you-go Elastic Computing Do your business 8 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 19. Challenges in moving into the Cloud http://www.accenture.com/us-en/outlook/Pages/outlook-online-2011-challenges-cloud-computing.aspx 9 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 20. Challenges in moving into the Cloud • Which CSP best matches my requirement? http://www.accenture.com/us-en/outlook/Pages/outlook-online-2011-challenges-cloud-computing.aspx 9 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 21. Challenges in moving into the Cloud • Which CSP best matches my requirement? • How secure is to move my data/job into a Cloud? http://www.accenture.com/us-en/outlook/Pages/outlook-online-2011-challenges-cloud-computing.aspx 9 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 22. 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? http://www.accenture.com/us-en/outlook/Pages/outlook-online-2011-challenges-cloud-computing.aspx 9 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 23. 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? http://www.accenture.com/us-en/outlook/Pages/outlook-online-2011-challenges-cloud-computing.aspx 9 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 24. Challenges faced by the providers 10 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 25. Challenges faced by the providers • How to offer the right price to increase the revenue? 10 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 26. Challenges faced by the providers • How to offer the right price to increase the revenue? • How to manage the resources efficiently? 10 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 27. 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? 10 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 28. 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? 10 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 29. Raffles Place, Singapore Overview of Game Theory ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012 11Wednesday, August 8, 12
  • 30. 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 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 31. What is a game? 13 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 32. 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 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 33. 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 Player is general entity individual, company, nation, protocol, animal, etc 13 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 34. 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 Player is general entity individual, company, nation, protocol, animal, etc Strategies actions which a player chooses to follow 13 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 35. 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 Player is general entity individual, company, nation, protocol, animal, etc Strategies actions which a player chooses to follow Outcome determined by mutual choice of strategies 13 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 36. 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 Player is general entity individual, company, nation, protocol, animal, etc Strategies actions which a player chooses to follow Outcome determined by mutual choice of strategies Preference relation modeled as utility (payoff) over set of outcomes 13 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 37. 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 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 38. 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 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 39. Prisoner’s Dilemma: Revisited 16 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 40. Prisoner’s Dilemma: Revisited • Two suspects arrested for a crime Prisoner 1 Prisoner 2 16 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 41. Prisoner’s Dilemma: Revisited • Two suspects arrested for a crime • Prisoners decide whether to confess or not to confess Prisoner 1 Not Confess Prisoner 2 Confess Confess Not Confess 16 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 42. Prisoner’s Dilemma: Revisited • Two suspects arrested for a crime • Prisoners decide whether to confess or not to confess Prisoner 1 Not • If both confess, both sentenced to 3 Confess Prisoner 2 Confess months of jail Confess -3,-3 Not Confess 16 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 43. Prisoner’s Dilemma: Revisited • Two suspects arrested for a crime • Prisoners decide whether to confess or not to confess Prisoner 1 Not • If both confess, both sentenced to 3 Confess 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 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 44. Prisoner’s Dilemma: Revisited • Two suspects arrested for a crime • Prisoners decide whether to confess or not to confess Prisoner 1 Not • If both confess, both sentenced to 3 Confess Prisoner 2 Confess months of jail Confess -3,-3 0,-9 • If both do not confess, then both will 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 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 45. Prisoner’s Dilemma: Revisited • Two suspects arrested for a crime • Prisoners decide whether to confess or not to confess Prisoner 1 Not • If both confess, both sentenced to 3 Confess Prisoner 2 Confess months of jail Confess -3,-3 0,-9 • If both do not confess, then both will 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? 16 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 46. 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 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 47. 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 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 48. Resource Allocation in Cloud Computing Envirnments Ulu Watu, Bali, Indonesia 19 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 49. 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 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 50. Resource Allocation in Cloud 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 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 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 51. 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) 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. 22 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 52. Axiomatic Bargaining Approaches Nash Bargaining Solution (NBS) 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. 23 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 53. Axiomatic Bargaining Approaches Raiffa-Kalai-Smorodinsky Bargaining Solution (RBS) 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. 24 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 54. Resource Allocation in Cloud 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. 25 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 55. Resource Allocation in Cloud 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. 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. 26 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 56. Resource Allocation in Cloud 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. 27 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 57. Multiple Cloud Orchestration Melaca, Malaysia 28 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 58. 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 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 59. 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 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 30 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 60. 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 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 61. 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 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 62. 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. 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 33 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 63. 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. 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 34 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 64. Revenue Maximization in Mobile Clouds From my home, Thodupuzha, Kerala 35 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 65. 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 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 66. 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. 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 37 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 67. 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. 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 38 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 68. 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. 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 39 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 69. 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. 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 40 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 70. Phang Nga Bay, Thailand Cyber-Physical Systems Robustness 41 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 71. 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 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 72. Attack and defense in cyber-physical systems Cyber physical systems :- Systems which need cyber and physical components to function. 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 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 73. 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 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 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 74. 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. 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 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 75. 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 players best strategy. 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 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 76. 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 players best strategy. Existence and solutions of pure and mixed-strategy Nash Equilibria can be found 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 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 77. Water-puppetry, Vietnam Summary... 43 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 78. To Summarize... 44 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 79. To Summarize... • Bargaining theory, Auction Theory, Coalition games, Non-cooperative games etc. 44 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 80. To Summarize... • Bargaining theory, Auction Theory, Coalition games, Non-cooperative games etc. • Resource allocation, Cloud orchestration, Robustness, Security, Mobile Clouds etc. 44 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 81. 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 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 82. 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 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 83. 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 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 84. 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 ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 85. ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12
  • 86. THANK YOU! ©All Rights Reserved, Ganesh Neelakanta Iyer August 2012Wednesday, August 8, 12