Service Oriented Grid: A Vision for the Financial Industry

Transcription

1 Service Oriented Grid: A Vision for the Financial Industry Prof. Dr. Wolfgang König, Dr. Michael Schwind Goethe University, Frankfurt Workshop on Agent-based Simulation and Modeling in Services Science International Christian University, Tokyo 2006 November, 22 Hier wird Wissen Wirklichkeit

2 Content Service Provision in Information Systems Grid Application in the Financial Industry: Asset Based Security Factory Combinatorial Auctioning for Capacity Allocation in ABS Factories Agentification: Agent-based Simulation for Combinatorial Auctions 2 Hier wird Wissen Wirklichkeit

3 Service Production in Information Systems Properties of Service Production in general and in Information Systems Widely fixed production capacities Non-storability and perishableness of production resources (opportunity costs) High volatility of demand Negligible marginal cost of additional units of production resources within capacity constraints (variable costs are nearly zero) Core Problem of Information Production is not the scarcity of the information product itself (arbitrary copying) but the indirect competition for the scarce physical resources involved in the production processes of the copies humans machines network infrastructure 3 Hier wird Wissen Wirklichkeit

4 Content Service Production versus Material Production Grid Application in the Financial Industry: Asset Backed Security Factory Combinatorial Auctioning for Capacity Allocation in ABS Factories Agentification: Agent-based Simulation for Combinatorial Auctions 4 Hier wird Wissen Wirklichkeit

5 Motivation New Challenges in the Financial Industry Legal regulations: Basel II, Sarbanes Oxley Act, MiFid Customer needs Private Banking: Individualized and customized asset management Corporate Finance: Increasingly complex financial engineering Consequences for the Management of IT Resources Steadily growing demand for IT resources Huge capacity requirements at peak load times (load balancing problem) Idle IT resources are increasingly expensive (opportunity costs) 5 Hier wird Wissen Wirklichkeit

6 Key to a Solution: Grid Technology Economized Grid Architecture Bundling of IT resource capacities employing Grid architecture Controlling of Grid systems using economically-inspired mechanisms including: Pricing, metering, billing, and accounting of resources and services Including willingness-to-pay and user preferences into the resource allocation process Service-oriented Grid Architecture Integration of services and resources by virtualization Usage-based billing for resources and services in Grid systems Application engineering capabilities for the provision of financial services Inclusion of service level aspects into resource provisioning, pricing, and billing 6 Hier wird Wissen Wirklichkeit

7 FINGRID Financial Grid Technical Perspective: Merger of Grid, Software Agent, and Service Oriented Architecture Economic Perspective: Introducing pricing, standardization, and workflow aspects GRID Agents Standardization Business Process Mgmt FINGRID SOA Pricing & Market Evaluation FINGRID Technical Perspective Economic Perspective 7 Hier wird Wissen Wirklichkeit

8 Example of an Application: Asset-backed Security Factory Asset-backed Securities Definition Bond type that is based on a pool of assets Typically assets which are highly illiquid are securitized, e.g. private mortgage A pool of assets has a distribution of risks Advantages Pooling reduces risk by diversification Introduces liquidity in illiquid markets and makes them tradable Reduces bankruptcy risk for the originator of credits Securitization makes assets attractive for a broad set of investors Trenching of ABS into instruments with different risk/return profiles attracts investors with different risk friendliness and investing time horizons 8 Hier wird Wissen Wirklichkeit

9 Example of an Application: Asset-backed Security Factory Asset-backed Securitization Portfolio Effect Pooled assets form the set of realizable portfolios (left) and reduce the diversifiable risk (right) Investors preference function and efficient frontier determine the optimal portfolio allocation Calculation of efficient frontier is quadratic programming problem (NP-hard) Expected Return Preference Function Risk Set of realizable Portfolios Efficient Frontier Diversifiable Risk Systematic Risk Risk Number of Assets 9 Hier wird Wissen Wirklichkeit

10 Comments: 7. Financial Contributions Summarize your client revenue supervised during the contribution period. This should detail the total revenue for the engagement not just the revenue you directly supervise. The appropriate contribution period for partners and associate partners is outlined below. A SSOCIATE PARTNERS Complete the financial tables below for FY02 Q4 actual (Q4 only) and FY03 Q3 estimate (estimate at Q3 for the entire fiscal year). a. Participation in Client Engagements (Indicate the financial activity for the overall client ment engage in the table and describe your role and contribution to this in the comments section.) - Coefficient Adjusted US $ Client Engagement Company 1 Company 2 Company 3 Company 4 Type of Work Cons ulting Cons ulting Cons ulting Cons ulting Your Role Cons ulting Del. AP Cons ulting Del AP Busi ness Dev AP Innov ation & Exp AP Est. % of time spent No. of Partners involved Client Net Revenue $m (approx) FY02 Client Margin % Client Margin $ 000 s FY03- Q3 Estimate Client Net Revenue $m (approx) Client Margin % Client Margin $ 000 s 60% % 0, , % % 0, , % , % 0,320 25% , % 0, , , , ,000 Totals 1.2 0, ,446 Millions Thousands Millions Thousands b. Client Group, OU MPs and Operating Group Chief Executiv es (FY02 data has been - stated re in FY03 terminology provided to you by Finance please complete this in the table below even if you have a final version of your FY02 CS Form). If you have any significant contributions in FY01 Q4, please detail these in comments section. - US $ Supervised Management Unit Period From -To (mm/yy - mm/yy) Net Revenue ($m) Year-onyear Growth (%) EVA (%) EVA ($m) Year-onyear EVA Growth (%) Example of an Application: Asset-backed Security Factory Small Banks Grid Engine Bank A Credit Data Product-Creation Simulation Optimization Portfolio-Mgmt Investment Banks ABS Placement Bank B Calculation Engine ABS Pool Benchmarking Bank C Business Operator Legal and Statutory Reports, Investor Reports Bank Z External Rating 10 Hier wird Wissen Wirklichkeit

11 Comments: 7. Financial Contributions Summarize your client revenue supervised during the contribution period. This should detail the total revenue for the engagement not just the revenue you directly supervise. The appropriate contribution period for partners and associate partners is outlined below. A SSOCIATE PARTNERS Complete the financial tables below for FY02 Q4 actual (Q4 only) and FY03 Q3 estimate (estimate at Q3 for the entire fiscal year). a. Participation in Client Engagements (Indicate the financial activity for the overall client ment engage in the table and describe your role and contribution to this in the comments section.) - Coefficient Adjusted US $ Client Engagement Company 1 Company 2 Company 3 Company 4 Type of Work Cons ulting Cons ulting Cons ulting Cons ulting Your Role Cons ulting Del. AP Cons ulting Del AP Busi ness Dev AP Innov ation & Exp AP Est. % of time spent No. of Partners involved Client Net Revenue $m (approx) FY02 Client Margin % Client Margin $ 000 s FY03- Q3 Estimate Client Net Revenue $m (approx) Client Margin % Client Margin $ 000 s 60 % % 0, , % % 0, , % , % 0,320 25% , % 0, , , , ,000 Totals 1.2 0, ,446 Millions Thousands Millions Thousands b. Client Group, OU MPs and Operating Group Chief Executiv es (FY02 data has been - stated re in FY03 terminology provided to you by Finance please complete this in the table below even if you have a final version of your FY02 CS Form). If you have any significant contributions in FY01 Q4, please detail these in comments section. - US $ Supervised Management Unit Period From -To (mm/yy - mm/yy) Net Revenue ($m) Year-onyear Growth (%) EVA (%) EVA ($m) Year-onyear EVA Growth (%) Example of an Application: Asset-backed Security Grid Factory Small Banks Credit Systems Client A Grid Engine Bank A Bank B Grid Agent Client B Grid Agent Provides benchmarking information Benchmarking Product-Creation Simulation Optimization Portfolio-Mgmt Calculation Engine Investment Banks Simulate, optimize, construct, and merge ABS portfolios and trenches ABS Placement Supports placement of big ABS trenches by investment banks Client C Credit Data ABS Pool Business Operator Bank C Grid Agent Client Z Allows on-demand customized portfolio construction supported by individual configuration agents in the Grid External ratings can be performed on the ABS fund data Legal and Statutory Reports, Investor Reports Bank Z Grid Agent External Rating Allows checks and reports on statuary compliance 11 Hier wird Wissen Wirklichkeit

12 Industry & Research FINGRID Consortium 12 Hier wird Wissen Wirklichkeit

13 FINGRID Research Issues Selected Questions Usability: Satisfying engineering support for financial toolset building? Security: Sufficient system security for participants in virtual workplaces? Quality: Adequate service level fulfillment and system stability? Allocation: Efficient allocation of resources to the service users? Pricing: Realization and user acceptance of pricing and billing models? 13 Hier wird Wissen Wirklichkeit

14 Content Service Provision in Information Systems Grid Application in the Financial Industry: : Asset Backed Security Factory Combinatorial Auctioning for Capacity Allocation in ABS Factories Agentification: Agent-based Simulation for Combinatorial Auctions 14 Hier wird Wissen Wirklichkeit

15 Allocation and Pricing of Grid Resources Combinatorial Auction as a Solution Real Time ABS Portfolio Optimization IT Resources Allocation? IT Services CPU Memory Network Disk Complementarities 1. Resources Portfolio Optimization complementary 3 Resources distributed required Disk failure Failure 2. Allocation of entire service Loss of ABS optimal deal dynamic ABS Risk Calculation 15 Hier wird Wissen Wirklichkeit

16 Combinatorial Auction Definitions Auction Auctions are resource allocation mechanisms based on a competitive bidding process over a single well defined object and involve a set of auction rules that specify how the winner is determined and how much he has to pay [Bichler et al. 2003, Wolfstetter 1996] Complementarity If the valuation of a bid bundle is higher than the valuation of the individual goods the effect is described as superadditivity. This results from complementarities in the bidder s utility function. [DeVries 2001] Combinatorial Auction Combinatorial auctions allow bidders to bid for bundles of goods, services or resources, while the valuation of the bundles depends on synergies between the individual goods, services or resources. [Cramton 2005] 16 Hier wird Wissen Wirklichkeit

17 Combinatorial Auction for Resource Allocation Combinatorial Auction Problem (CAP) Example Bidder b 3 1 and b 2 b 2 (E, 9 MU) Risk Calculation NET, t 1 NET, t 2 NET, t 3 (D, 8 MU) Resources {NET, CPU, MEM} (C, 8 MU) (A, 8 MU) CPU, t 1 CPU, t 2 CPU, t 3 Maximum? (B und D, 12 MU) MEM, t 1 MEM, t 2 MEM, t 3 (B, 4 MU) Portfolio Optimization Time Slots {t 1, t 2, t 3 } 3 Bidder {b 1, b 2, b 3 } bid for bundles {A, B, C, D, E} 17 Hier wird Wissen Wirklichkeit

18 Combinatorial Auction for Resource Allocation Combinatorial Auction Problem (CAP) Combinatorial Auction Problem max I under constraints: i= 1 j= 1 J j = 1 x i, j I J p = 1 = 1 i, j x i j i, j J 1 i, j i, j max q ( o, t) x q ( o, t) Resources: Time slots: Resource request: Price for bid : i, j i, j i, j Acceptance variable: Bid j of agent i: b Total return: b o t q p x i, j i, j Inc ( o, t) acc { 1,..., O} { 1,..., T} + { 0,1} Β + 18 Hier wird Wissen Wirklichkeit

19 Combinatorial Auction for Resource Allocation Intermediate Summary Lessons learned: Services: Widely fixed production capacities, non-storability and perishableness of production resources in IT service provision generates high opportunity costs Economization: One way to achieve the optimal usage of Grid computer infrastructure is to control resource allocation by employing economic principles Financial Applications: Modern financial applications require huge computational resources that must be available on demand Complementarities: IT resource networks exhibit strong resource complementarities that can be addressed by using a combinatorial auction 19 Hier wird Wissen Wirklichkeit

20 Content Service Provision in Information Systems Grid Application in the Financial Industry: Asset Backed Security Factory Combinatorial Auctioning for Capacity Allocation in ABS Factories Agentification: Agent-based Simulation for Combinatorial Auctions 20 Hier wird Wissen Wirklichkeit

21 Combinatorial Auction for Resource Allocation Agentification Agent-based Computational Economics (ACE) as a method to study modeled economies as settings of interacting agents. [Tesfatsion 2000, Axelrod & Tesfatsion 2006] Method Definition and Development of Optimization Goals Analysis of existing models Empirical study of requirements Deployment in an agentified world Test under various influences and parameterizations Comparison to given goals and real world phenomena 21 Hier wird Wissen Wirklichkeit

22 Combinatorial Auction for Resource Allocation Design of the Auctioning Process Market Mediator s Iterative Allocation- and Pricing Process Resource Status Pricing Resource Price Resource Provider Market Mediator Resource User Bid Acceptance Allocation Bid Submission 22 Hier wird Wissen Wirklichkeit

23 Combinatorial Auction for Resource Allocation Scenario Four computational resource types: Memory, CPU, Network, Disk MEM NET DSK MEM CPU DSK MEM NET DSK MEM DSK Task Agent 1 Task Agent 2 Task Agent 3 Task Agent 4 Market Mediator Resource Agent 1 Resource Agent 2 Resource Agent 3 Resource Agent 4 MEM DSK MEM CPU NET DSK MEM NET DSK MEM CPU NET 23 Hier wird Wissen Wirklichkeit

24 Combinatorial Auction for Resource Allocation Bids Service users submit requests b i, j for resource capacity q(o, t) by formulating bid matrices Bid Time Slot (t) Resources (o) t 1 t 2 t 3 t 4 t 5 t 6 t 7 t 8 CPU Network Disk Memory Hier wird Wissen Wirklichkeit

25 Combinatorial Auction for Resource Allocation Agentification resource agent auctioneer task agent comp-resource-cap() request-resource-info init-budget() inform-start-of-auction init-auction() inform-resource-cap call-for-proposal propose create-bids() book-resource() execute-task() compute-allocation() request-resource inform-task-execution debit-price() refresh-budget() inform-acceptance inform-task-execution init-next-auction() 25 Hier wird Wissen Wirklichkeit

26 Combinatorial Auction for Resource Allocation Combinatorial Auctioneer Task Controls the iterative resource allocation process Optimizes resource usage by accepting only the combination of bids with the highest return Optimization Return per auction round is maximized Problems Allocation process is time critical in large scale systems The calculation of prices for the resources for the information of the system users has not been solved satisfyingly for complementarities between the resources 26 Hier wird Wissen Wirklichkeit

27 Combinatorial Auction for Resource Allocation CAP Solution Method Method Greedy (GR) Bids are sorted according to the ratio of price and resource units included, bids are included into the final allocation until capacity limit is exceeded Simulated Annealing (SA) Bids are added and removed controlled by a temperature mechanism that allows the temporal occurrence of inferior solutions in connection with a penalty function Genetic Algorithm (GA) Uses random-key coding which allows to integrate the resource constraints of the CAP into the GA mechanism with tournament selection [Schwind et al. 2003a] Benchmark Use of several structured, stochastically generated resource requests [Schwind et al. 2003a] 27 Hier wird Wissen Wirklichkeit

28 Combinatorial Auction for Resource Allocation CAP Solution Method computation time solution quality 75 Agents GR SA GA IP Solution Method Time (sec) ,1 0,01 Quality 100% 98% 96% 94% 92% 90% 88% 86% 84% 82% 80% 75 Agents GR SA GA IP Solution Method Benchmark for a simulation with 75 agents Simulation with for solution methods for the calculation of the CAP: Greedy (GR), Simulated Annealing (SA) and Genetic Algorithm (GA) compared to Integer Programming (IP) [Schwind et al. 2003a] 28 Hier wird Wissen Wirklichkeit

29 Combinatorial Auction for Resource Allocation Resource Prices Shadow Prices (Dual Problem of Relaxed CAP) O min z q ( o, t ) sp O T = max ot, under constraints T o= 1 t= 1 O T o= 1 t= 1 o= 1 t= 1 q ( o, t) sp = p B i, j ot, i, j q ( o, t) sp +δ p B i, j i, j + ot, i, j Accepted bids: Rejected bids: Reduced cost: Shadow : Shadow price in round B B δ sp + + i, j 0 + ot, 0 shad o k: V ( k) B B + 0 Shadow price for resource o over period T: shad o = ot, V ( k) sp o O T t= 1 Market value of a resource unit: T sp q( o, t) t= 1 vo k = o O T qot (, ) ot, ( ) {1,..., } t= 1 29 Hier wird Wissen Wirklichkeit

30 Combinatorial Auction for Resource Allocation Prices under Complementarities Market Prices for Resources Scarcities of Resources 1,4 1,2 1 0,8 Market Price 1 0,8 0,6 Scarcity 0,6 0,4 0,4 0,2 0, Round Round Res. 1 Res. 2 Res. 3 Res. 4 Res. 1 Res. 2 Res. 3 Res. 4 Resource Prices Market price reaction for four complementary resources based on shadow prices in an iterative combinatorial action with 10 competitive agents Complementarities cause strong increase of market price in round 25, if resource 1 is cut by 37 % [Schwind et al. 2006b] 30 Hier wird Wissen Wirklichkeit

31 Toolset for Combinatorial Auction Problems Capabilities Simulates an iterative allocation processes in a Grid Emulates various bidding strategies of the agents in the allocation process Solves the CAP with different solution methods (GA, SA, Greedy) Generates different types of request patterns (bit matrices) Visualizes resource prices and resource scarcity Allows benchmarking of solution methods with different request patterns Emulates drop of resource capacity 31 Hier wird Wissen Wirklichkeit

32 Toolset for Combinatorial Auction Problems Demonstration 32 Hier wird Wissen Wirklichkeit

33 Further Research Other applications of combinatorial auctions for resource allocation to foster economic usage of grid systems [Neumann et al. 2006] Implementation of the auction mechanism in the task-scheduler of a grid System [FinGrid 2006] Development of a decision support mechanism for the configuration of combinatorial auctions according to user specifications [König et al. 2006] 33 Hier wird Wissen Wirklichkeit

34 Literature AuYoung, A., Chun, B.N., Snoeren, A.C., and Vahdat, A. "Resource Allocation in Federated Distributed Computing Infrastructures," Proceedings of the 1st Workshop on Operating System and Architectural Support for the On-demand IT Infrastructure, San Francisco, USA, Chun, B.N., Buonadonna, P., AuYoung, A., Ng, C., Parkes, D.C., Shneiderman, J., Snoeren, A.C., and Vahdat, A. "Mirage: A Microeconomic Resource Allocation System for SensorNet Testbeds," Proceedings of the 2nd IEEE Workshop on Embedded Networked Sensors (EmNetS-II) Sidney, Australia, König, W., and Schwind, M. "Entwurf von kombinatorischen Auktionen für Allokations- und Beschaffungsprozesse," in: Herausforderungen der Wirtschaftsinformatik: Festschrift für Prof. Krallmann, B. Rieger and D. Karagiannis (eds.), Springer Verlag, Berlin, 2005, pp Lai, K., Huberman, B.A., and Fine, L. "Tycoon: A Distributed Market-based Resource Allocation System," HP Labs, Palo Alto, CA, USA, Neumann, D., Holtmann, C., and Orwat, C. "Grid-Economics," Wirtschaftsinformatik (48:3) 2006, pp Ng, C., Parkes, D.C., Seltzer, M., Virtual Worlds: Fast and strategy proof auctions for dynamic resource allocation. In: Proceedings of the third ACM Conference on Electronic Commerce (EC- 2003), San Diego, CA, 2003, pp Schnizler, B., Neumann, D., Veit, D., Weinhardt, C., Trading Grid Services - A Multi-attribute Combinatorial Approach, European Journal of Operational Research, forthcoming, 2006 Schwind, M. "Design of Combinatorial Auctions for Allocation and Procurement Processes," 7th International Conference on E-Commerce Technology 2005, München, Germany, 2005, pp Hier wird Wissen Wirklichkeit

35 Literature Schwind, M., and Gujo, O. "Using Shadow Prices in a Combinatorial Grid with Proxy-bidding Agents," Proceedings of the 8th International Conference on Enterprise Information Systems (ICEIS 2006); Paphos; Cyprus, 2006a. Schwind, M., Gujo, O., and Stockheim, T. "Dynamic Resource Prices in a Combinatorial Grid System," Proceedings of the IEEE Joint Conference on E-Commerce Technology (CEC'06) and Enterprise Computing, E-Commerce and E-Services (EEE'06), San Francisco, US, 2006b. Schwind, M., Stockheim, T., and Gujo, O. "Agents' Bidding Strategies in a Combinatorial Auction Controlled Grid Environment," Proceedings of the AAMAS 2006 Trading Agent Design and Analysis / Agent-Mediated Electronic Commerce Joint Workshop, Hakodate, Japan, 2006c. Schwind, M., Stockheim, T., and Rothlauf, F. "Optimization Heuristics for the Combinatorial Auction Problem," Proceedings of the Congress on Evolutionary Computation CEC 2003, Canberra, Australia, 2003a, pp Schwind, M., Stockheim, T., and Seibel, S. "Price Controlled Resource Allocation for the Provision of Information Products and Services Employing Combinatorial Auctions," in: Proceedings of the 11th European Conference on Information Systems (ECIS), Naples, Italy, 2003b. Stonebraker, M., Devine, R., Kronacker, M., Litwin, W., Pfeffer, A., Sah, A., and Staelin, C. "An Economic Paradigm for Query Processing and Data Migration in Mariposa," University of California, Berkeley, Waldspurger, C.A., Hogg, T., Huberman, B.A., Kephart, J., and Stornetta, S. "Spawn: A Distributed Computational Economy," Software Engineering (18:2) 1991, pp Hier wird Wissen Wirklichkeit

36 Content Appendix 36 Hier wird Wissen Wirklichkeit

37 Service Production in Information Systems Service Oriented Architecture Definition A methodology for organizations to coordinate loosely coupled services for a consumer (whether internal or external to the firm) to utilize that reflect the efficient completion of underlying business processes Properties The SOA platform is a delivery mechanism Process can work across queuing technologies, VoIP, messages, or any other mechanism that holds data Web services is just a transport mechanism The service is a consumable product: once done, work complete Business processes are shifted to infrastructure: processes happen in the infrastructure Contracts with business negotiated based on services, not products 37 Hier wird Wissen Wirklichkeit

38 Combinatorial Auction for Resource Allocation Budget Mechanism Closed Economy Equal initial budgets for all agents Budget refresh per round proportionally to the auctioneer s income Task- und Resource agents operate as economic entity (prosumer model) Bid submission only if agents budgets are greater than bid price Amount of virtual currency remains constant during simulation Auctioneer administrates agents budgets 38 Hier wird Wissen Wirklichkeit

39 Combinatorial Auction for Resource Allokation Bidding Agents Tasks Formulation of bids according to budget and utility function Optimization Objective Procurement of resource capacity according to the preferences of the service users Utility function Trade-off between waiting time until bid acceptance and resource capacity per MU Bidding Behavior Price increment p for the next round in case of bid rejection Initial bid price p ini relative to the market price of the last round Two bidder types: Quantity maximizer (e.g. database job) and time minimizer (e.g. video conference) 39 Hier wird Wissen Wirklichkeit

40 Combinatorial Auction for Resource Allocation Bidding Behavior of Agents Bidding Behavior p i, j ( k) ini BG if k 1 = L M J v ( k 1) q ( o, t) p if k = 1 O T inc o= 1 t= 1 o i, j inc ini ( ) p = p + l Δp Current round of bid b : i, j Waiting time for bid b : i, j Number of XOR-bids: Number of bids per round: Current round of auction: l L J M k inc Increment for price multiplicator p : Δp + Initial price multiplicator: p ini + Initial budget: BG ini + 40 Hier wird Wissen Wirklichkeit

41 Combinatorial Auction for Resource Allocation Agents Utility Function Utility Function U a a = x q ( o, t) O T (, ) i, j i j B o= 1 t= 1 i, j ( acc ) l a β α Utility function of agent a: {(, ), } Bids of agent a: B i j i j Mean waiting time: Quantity weight: Time weight: U l a a acc a α β Hier wird Wissen Wirklichkeit

42 Combinatorial Auction for Resource Allocation Prices under Complementarities 100 Relative Price Change Shortening of Resource 1 Res. 1 Res. 2 Res. 3 Res. 4 Resource prices with stepwise shortening Relative price change as a ratio of shadow prices before and after shortening of resource one under a stepwise reduction by a maximum of seven resource units [Schwind et al. 2006b] 42 Hier wird Wissen Wirklichkeit

43 Combinatorial Auction for Resource Allocation Bidding Strategies Strategy space: initial bid level and price increment Ø Waiting time until acceptance of desired resource combination Acquired resource units over 50 rounds with constant budget for all agents 43 Hier wird Wissen Wirklichkeit

44 Combinatorial Auction for Resource Allokation Bidding Strategies Simulation parameters 3 bidders with fixed bidding behavior: P = 0.2, P ini = 0.5 Variable test bidder: P = , P ini = Utility values: Quantity maximizer: α = 0.5, β = 0.01 Time minimizer: α = 0.5, β = Hier wird Wissen Wirklichkeit

45 Related Research Combinatorial Auctions in Grid Systems MIRAGE: Combinatorial auction for resource allocation in a distributed system Bellagio: Second price auction that tries to achieve strategy proofness [Chun et al. 2004] [Ng et al. 2004] SHARE: CA-based allocation scheme grounding on a simple greedy mechanism SORMA: CA-based multi-attributive trading architecture for Grid services [AuYoung et al. 2004] [Schnizler et al. 2006] 45 Hier wird Wissen Wirklichkeit