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Workflow Resource Allocation through Auctions Abert Plà eXiT – University of Girona Advisors: Beatriz López & Javier Murillo 1.

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Presentation on theme: "Workflow Resource Allocation through Auctions Abert Plà eXiT – University of Girona Advisors: Beatriz López & Javier Murillo 1."— Presentation transcript:

1 Workflow Resource Allocation through Auctions Abert Plà eXiT – University of Girona Advisors: Beatriz López & Javier Murillo 1

2 eXiT: Control Engineering and Intelligent Systems University of Girona Interests – Machine Learning – Workflows – Multi-Agent Systems – Auctions Domains: – Medicine – Logístics eXiT research group Interests – Case Based Reasoning – Multivariate statistical process control – Signal Processing – Electrical 2

3 Girona 3

4 Preliminary Work – Domain – Workflow Monitoring & Modeling – MAS Workflow Management System Workflow Resource allocation through auctions – Auctions in workflows Multi-attribute auctions – GSP & Position Auctions – GSPMA 2 – Results Workflow Resource Allocation through Auctions 4

5 Hospital Medical device manufacturers Medical Device Maintenance Service 1. Preliminary Work

6 Hospital Medical Device Maintenance Service Preliminary Work 2 type of workflows: [1] – Expected workflows (Planned in advance using a planner) – Unexpected workflows (Faults in devices, delayed urgent workflows, etc.) [1] López et Al. 2010, Medical equipment maintenance support with service-oriented multi-agent services

7 Medical Device Maintenance Service Preliminary Work Other requirements: – Deadlines- Technician licenses – Quality of Service- … – - Payments made a posteriori Medical device manufacturers Technicians

8 Preliminary Work – Need to model the workflows of the MDMS Petri nets (RAPN) – Need to monitor concurrent workflows in the MDMS Workflow Management System Delay Detection 8

9 Workflow modeling Petri nets extension: Resource aware petrinets 9

10 Workflow modeling Resource-aware Petri net (include resources) Type: Transport Resources in the organization: 2 Currently used resources: 1 Fireable Type: Transport Resources in the organization: 2 Currently used resources: 2 Non Fireable 10

11 Workflow management system (WMS) Architecture 11

12 WMS  MAS-WMS Monitoring using agents [1] – 1 Workflow agent for each kind of workflow – 1 Resource agent for each resource WF Agent A WF Agent B WF Agent C WF Agent D Resource Agent 1 Resource Agent 2 Resource Agent n WMS MAS-WMS 12 External Resource Agent [1]Pla et Al. 2011, Petri Net Based Agents for Coordinating Resources in a Workflow Management System

13 MAS-WMS Resource agent – Handles a resource – Defends its interests – Characterized by cost, capacity, category, etc. Workflow agents – Handles all the workflow instances of a workflow pattern. – Resource allocation (Part 3) – Change workflow priority in case of delay 13

14 Workflow monitoring Each task has an estimated execution time (e.g. MeanTaskTime) The sum of MTT is the Workflow mean time Token contains information about the initial workflow time instant and the task initial time Using the token information we can know the workflow elapsed time (WET) and the time spent in the current task WET+remainingTasks(mean time) > Wfdeadline Early delay detection MeanTaskTime1 = m1MTT2 = m2 p 0 p1p1 p2p2 p3p3 p f WF_MeanTime = m1+m2+m3 MTT3= m3 Workflow Elapsed Time WF Deadline = prestablished 14

15 Workflow monitoring Experiments: Workflow simulations Workflows: RMI and MEEM Resources: 4 Technician Type A 1 Technician Staff Leader Parameters:500 time units p= WMS detects a possible delay Delay produced

16 Summary Workflows in Medical device maintenance service How to model workflows including information about the needed resources? – Resource Aware Petri Nets – Inclusion of Resource to Petri Nets How to monitor workflows and predict delays? – Monitorization on the task level – Workflow Management System 16

17 2. Workflow resource allocation through auctions 17

18 Resource allocation A workflow agent monitors and manages all the instances of a workflow type [1] – Call for auctions when they need resources Resource Agent 1 Resource Agent 2 Workflow Agent A Resource Type A 18

19 Resource allocation A workflow agent monitors and manages all the instances of a workflow type [1] – Call for auctions when they need resources Resource Agent 1 Resource Agent 2 Workflow Agent A Resource Type A 19

20 Resource allocation A workflow agent monitors and manages all the instances of a workflow type [1] – Call for auctions when they need resources Resource Agent 1 Resource Agent 2 Workflow Agent A Resource Type A 20

21 Resource allocation A workflow agent monitors and manages all the instances of a workflow type [1] – Call for auctions when they need resources Resource Agent 1 Resource Agent 2 Workflow Agent A Resource Type A AUCTION! 21

22 Reverse auction Workflow agent: the auctioneer – The buyer – Different attributtes to be fullfilled – Time Restritctions (Starting & Ending Time) – Resource Category (E.g. Tehcnician License) – Quality – … Resource agents: the bidders -The sellers -The one with the lowest bid wins the auction 22

23 Resource allocation 23

24 Resource allocation We have analyzed WDP using attributes independently [1] Price – Balanced market price – Providers equilibrium – Decrease costs for workflow agent – Suitable for internal & external providers – + Delays Time – Shortens workflow timings – Reduces number of delays – Indicated for dealing with internal providers – Arises cost – Faster resources can increase their prices 24 [1] Pla, Murillo and López Workflow resource allocation throught auctions

25 Part 3 – Multi-attribute auctions 25

26 Multi-attribute auctions 26 Not only satisfy the restriction

27 Multi-attribute auctions 27 Not only satisfy the restriction C 10: € Maybe ending before with a small price increase is helpful for the whole system

28 Multi-attribute auctions Evaluation function Score Determines the auctioneer preferences and the winner 28

29 Multi-attribute auction: The keys Evaluation function/WDP: – Multi-criteria functions How much to pay? – Vickrey? – First price? – Second price? – Our solution: Inspired on GSP auctions 29

30 Position auctions: GSP Used to assign a set of positions: – Internet advertising – Pay per click Generalized second price – The winning bid pays (or receives) the amount that bided the second best bidder 30 [2][1] [1] Varian Position auctions. [2] Athey and Ellison, Position auctions with consumer search

31 Position Auctions: Google Adsense Example: 1 position available for the “ipod” google search 31 BidderBidded payperclick PayperclickNº clicksRevenue Cdiscount eBay0.05 Mp3markt0.045 Fnac0.03 Darty0.025 Apple0.01

32 Position Auctions: Google Adsense 32 Cdiscount:0.05€ per click clicks 500€ Apple:0.01€ per click clicks 10000€ What if….

33 Position Auctions: Google Adsense Google adds a “quality” parameter which indicates the probability of a user to click an advert. [1] It is based in google traffic statistics, google page rank, etc. The parameter is provided by google itself. (The auctioneer) 33 [1] Varian Position auctions.

34 Position Auctions: Google Adsense Bided Ppc“Quality”Score (Ppc * Q) Cdiscount eBay Mp3markt Fnac Darty Apple Pay the minimum to beat the next bidder: ppc(b i ) = score(b i+1 )/q i ppc(apple) = 0.99/100 = € per click Google revenue: € * clicks = 99999€

35 Multi-attribute GSP auctions In Google Adsense the attributes are provided for the auctioneer, so they cannot be falsified. In the workflow domains, the attributes are provided by the bidders. Bidders can lie. 35

36 Workflow resource allcoation Task allocation for workflows: – Reverse auctions Auctioneers are the buyers Lowest bid wins – Payment after the task is done – Bundle of attributes provided by the bidders GSPMA 2 : Generalized Second Price Multi- Attribute Auction 36

37 GSPMA 2 : Generalized Second Price Multi-Attribute Auction Based on Google GSP Evaluation function to score and rank the bids The winning bidder receives the amount that it should have bided to beat the score of its following bid When breaking the agreement with the auctioneer (false attribute bid) they payment received is reduced in order to meet the same score with the “real” attributes. 37

38 GSPMA 2 : Evaluation Function Multi-criteria function that unifies the attributes and the bid: – Product (as in Google Ads) – Sum – Weighted Product – Weighted Sum … Linear preferences 38

39 GSPMA 2 : Payment The winning bidder recieves the amount that it should have bidded to match the second best bid: f(payment,a 1 i,…a n i ) = f(b i,a 1 i+1,…a n i+1 ) 39 PriceEnd TimeLicense*Score (product) Bidder A Bidder B *1 is equivalent to the best license payment * 12 * 1 = 270 payment = 270 / (12) payment = 22.5

40 GSPMA 2 : Payment Otherwhise, if the the resource breaks the agreement, the winning bidder recieves an amount which gives to te task attributes the same score as the initial bid f(payment,a 1’ i,…a n’ i ) = f(b i,a 1 i,…a n i ) 40

41 GSPMA 2 : Payment f(payment,a 1’ i,…a n’ i ) = f(b i,a 1 i,…a n i ) If the real ending time is 14 payment * 14 * 11 = 180 payment = 180/14 payment = PriceEnd TimeLicense*Score (product) Bidder A Bidder B

42 GSPMA 2 : Payment Example of evaluations and payments when using just the ending time attribute: 42

43 GSPMA 2 : Incentive comptibility With this mechanism truthful bidding is the dominant strategy [1] – Exactness – Monotonicity – Critical – Participation Tried to find a counter-example using a constraint solver (Z3). It does not exist. Requirement: evalutaion function must be strictly monotonic 43 [1] Lehman, O’Callaghand and Shoham 2002, Truth revelation in approximately efficient combinatorial auctions

44 GSPMA 2: Performance Comparation with another Multi-attribute auction mechanism: – Parkes Vickrey Multi-attribute adaptation [1] Parameters for the task/resource allocation: – Minimum initial time and Maximum Ending Time – Resource licenses (1 to 10 where) – Economic cost Agents strategies: – Cheating [2] – Adaptative [3] 44 [1] Parkes and Kalagnanam 2005, Iterative multiattribute vickrey auctions. [2] Muñoz and Murillo 2008, Agent UNO: Winner in the 2nd Spanish ART competition [3] Lee and Szymanski 2005, A novel auction mechanism for selling time-sensitive e-services

45 GSPMA 2 : Performance Comparation with Parkes 2005 mechanism: 45 Cheating agents Adaptative agents

46 GSPMA 2 : Performance Comparation with Parkes 2005 mechanism: 46 Cheating agents Adaptative agents

47 GSPMA 2 : Performance Switching cheating strategy to adaptative strategy Cheaters

48 GSPMA 2 : Performance Comparation with Parkes 2005 mechanism: – More efficient against cheating agents – Less delayed workflows – More expensive for the workflows

49 Conclusions Managing unexpected workflows in a Medical Device Maintenance Service. Need to allocate workflow tasks to resources. Reverse multi- attribute auctions – Quality, time, licenses, etc. Generalized Second Price multi-attribute auctiosn 49

50 Current work Adapt GSPMA 2 to combinatorial auctions: – Combine VCG auctions with GSPMA 2 Select winners and scores using VCG Transform scores to payments using GSPMA 2 fafa – Test incentive compatibility – Apply to new domains 50

51 Future Work Repetitive auctions Multicriteria functions – Test the incentive compatibility for other MCf – Is the best way to evaluate the bid? Non linear preferences Test new cheating agents 51

52 Workflow Resource Allocation through Auctions Abert Plà eXiT – University of Girona Advisors: Beatriz López & Javier Murillo 52

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56 Multi-attribute auction: The keys Vickrey auction example PriceEnd TimeValue (price*end_time) Auctioneer rank Bid 1100€11002 Bid 2150€ Bid 3250€ Part 4 – Multi-attribute auctions 56

57 Multi-attribute auction: Vickrey problem Vickrey auction example (second price) PriceEnd TimeValue (price*end_time) Auctioneer rank Bid 1100€11002 Bid 2150€ Bid 3250€ Part 4 – Multi-attribute auctions 57

58 Multi-attribute auction: Vickrey problem Vickrey auction example (second price) PriceEnd TimeValue (price*end_time) Auctioneer rank Bid 1100€11002 Bid 2150€ Bid 3250€ BID 2 wants 150€  Gets 100€  NOT FAIR! Part 4 – Multi-attribute auctions 58

59 Multi-attribute auction: Vickrey problem Pay function acording to WDP (value function): · How much better is the winner than the second? · Related with the evaluation function Pay = 100* (1/0.5) = 200 PriceEnd TimeValue (price*end_time) Auctioneer rank Bid 1100€11002 Bid 2150€ Bid 3250€

60 Multi-attribute auction: Vickrey problem Pay function acording to WDP (value function): Example 1: proportional Pay =sec(bid_price)* sec(bid_time)/Win(bid_time) Pay = 100* (1/0.5) = 200 PriceEnd TimeValue (price*end_time) Auctioneer rank Bid 1100€11002 Bid 2150€ Bid 3250€ BID 2 wants 150€  Gets 200€  FAIR! 60

61 What to study Evaluation functions – Multicriteria How evaluation function, WDP and payment mechanism are related – How to avoid cheating in repetitive auctions Is it incentive compatible? … Is it a good smart solution? Part 4 – Multi-attribute auctions 61

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63 Repetitive 63

64 Workflow resource allocation through auctions 2 Workflow types Many instantiations (under demand) All need resources! Part 3 – – Resource allocation (auctions) 64


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