Multiagent resource allocation in service networks

The term service network (SN) denotes a network of software services in which complex software applications are provided to customers by aggregating multiple elementary services. These networks are based on the service-oriented computing (SOC) paradigm, which defines the fundamental technical concepts for software services over electronic networks, e.g., Web services and, most recently, Cloud services. For the provision of software services to customers, software service providers (SPs) have to allocate their scarce computational resources (i.e., hardware and software) of a certain quality to customer requests. The SOC paradigm facilitates interoperability over organizational boundaries by representing business relationships on the software system level. Composite software services aggregate multiple software services into software applications. This aggregation is denoted as service composition. The loose coupling of services leads to SNs as dynamic entities with changing interdependencies between services. For composite software services, these dependencies exist across SN tiers; they result from the procurement of services, which are themselves utilized to produce additional services, and constitute a major problem for resource allocation in SNs. If these dependencies are not considered, the fulfillment of agreements may become unaccomplishable (overcommitment). Hence, the consideration of service dependencies is crucial for the allocation of service providers resources to fulfill customer requests in SNs. However, existing resource allocation methods, which could consider these dependencies -- such as combinatorial auctions with a central auctioneer for the whole SN -- are not applicable, since there are no central coordinating entities in SNs. The application of an allocation mechanism that does not consider these dependencies might negatively affect the actual service delivery; results are penalty payments as well as a damage to the reputation of the providers. This research is conducted in accordance to the design science paradigm in information system research. It is a problem-solving paradigm, which targets the construction and evaluation of IT artifacts. The objectives of this research are to develop and evaluate an allocation protocol, which can consider multi-tier service dependencies without the existence of central coordinating entities. Therefore, an interaction protocol engineering (IPE) perspective is applied to solve the problem of multi-tier dependencies in resource allocation. This approach provides a procedure model for designing interaction protocols for multiagent systems, and is closely related to the well-established area of communication protocol engineering. Automated resource allocation in SNs is analyzed in this research by representing the actors as autonomous software agents in the software system. The actors delegate their objectives to their software agents, which conduct the negotiations for service provision on their behalf. Thus, these agents communicate concerning the resource allocation; in this process, the sequence of communication interactions is crucial to the problem addressed. Interaction protocols define a structured exchange of defined messages between agents; they facilitate agent conversations. When multiple agents have to reach agreements by negotiation and bargaining, such as in case with allocating scarce resources, game theory provides means to formalize and analyze the most rational choice of actions for the interacting agents. Based on a formal framework for resource allocation in SNs, this research first performs a game-theoretic problem analysis; it is concerned with the existence, as well as the complexity of computing optimal allocations. In addition, Nash equilibria are analyzed for optimal allocations. Second, a distributed, auction-based allocation protocol, which prevents overcommitments and guarantees socially optimal allocations for single customer requests under certain assumptions, is proposed. Therefore, a game-theoretic model and an operationizable specification of the protocol are presented. Third, it is formally verified that the protocol enables multi-tier resource allocation and avoids overcommitments by proofs for the game-theoretic model and by model checking for the interaction protocol specification; using the model checker Spin, safety properties like the absence of deadlock are as well formally verified as the protocol enabling multi-tier resource allocation. Fourth, the efficacy and the benefits of the proposed protocol are demonstrated by multiagent simulation for concurrent customers. The experimental evaluation provides evidence of the protocols efficiency compared to the socially optimal allocation as a centralized benchmark in different settings, e.g., network topologies and different bidding policies. Der Begriff Service Network (SN) bezeichnet ein Netzwerk von Software-Services, in dem komplexe Software-Applikationen durch Aggregation mehrerer elementarer Services fur Kunden bereitgestellt werden. Diese Netzwerke basieren auf dem Paradigma des Service-oriented Computing, welches die grundlegenden technischen Konzepte fur Software-Services uber elektronische Netzwerke bereitstellt, d.h. Web Services und zuletzt Cloud-Computing. Fur die Bereitstellung von Software-Services fur Kunden mussen Software-Anbieter ihre knappen Ressourcen (d.h. Hardware und Software) einer bestimmten Qualitat zu Kundenanfragen allozieren, also entsprechende Ressourcen reservieren, um Software-Services in der vereinbarten Dienstgute bereitzustellen. Zusammengesetzte Software-Services aggregieren mehrere Software-Services zu Software-Applikations-Services. Diese Aggregation wird als Service-Komposition bezeichnet. Die lose Kopplung von Services macht SNs zu dynamischen Entitaten mit sich verandernden Interdependenzen zwischen den Services. Fur zusammengesetzte Software-Services existieren solche Abhangigkeiten uber mehrere SN-Stufen; sie ergeben sich durch die Beschaffung von Services, welche fur die Produktion von weiteren Services verwendet werden, und stellen ein Hauptproblem bei der Ressourcenallokation in SN dar. Werden diese Abhangigkeiten nicht berucksichtigt, kann die Erfullung von Vereinbarungen undurchfuhrbar werden (overcommitment). Daher ist die Berucksichtigung von Service-Abhangigkeiten bei der Allokation von Ressourcen der Service-Anbieter fur die Erfullung der Kundenanfragen in SNs entscheidend. Existierende Methoden der Ressourcenallokation, welche diese Abhangigkeiten berucksichtigen konnten -- wie kombinatorische Auktionen mit einem zentralen Auktionator fur das gesamte SN -- sind jedoch nicht anwendbar, da in SNs keine zentralen Koordinationsentitaten existieren. Der Einsatz eines Allokationsmechanismus, welcher diese Abhangigkeiten nicht berucksichtigt, kann die konkrete Service-Erbringung negativ beeinflussen und somit in Strafzahlungen und einer Beeintrachtigung der Reputation der Service-Anbieter resultieren. Die vorliegende Forschungsarbeit wird in Ubereinstimmung mit dem Design Science-Paradigma durchgefuhrt. Dabei handelt es sich um ein Problemlosungs-Paradigma, welches die Konstruktion und Evaluation von IT-Artefakten zum Ziel hat. Ziel dieser Forschungsarbeit ist die Entwicklung und Evaluation eines Allokationsprotokolls, welches mehrstufige Service-Abhangigkeiten ohne die Existenz zentraler, koordinierender Entitaten berucksichtigen kann. Zu diesem Zweck wird eine Interaction-Protocol-Engineering (IPE)-Perspektive eingenommen, um das Problem mehrstufiger Abhangigkeiten bei der Ressourcenallokation zu losen. Dieser Ansatz stellt ein Vorgehensmodell fur den Entwurf von Interaktionsprotokollen fur Multiagentensysteme zur Verfugung. Diese Forschungsarbeit analysiert die automatisierte Ressourcenallokation in SNs durch die Reprasentation der Akteure als autonome Softwareagenten im Softwaresystem. Die Akteure delegieren ihre Ziele an ihre Softwareagenten, welche in deren Auftrag die Verhandlung fur die Service-Erbringung durchfuhren. Somit kommunizieren diese Softwareagenten bezuglich der Ressourcenallokationen; dabei ist die Abfolge der Interaktionen fur das adressierte Problem elementar. Interaktionsprotokolle definieren einen strukturierten Austausch bestimmter Nachrichten zwischen Agenten. Wenn mehrere Agenten Vereinbarungen durch Verhandlungen treffen mussen, wie im Falle der Allokation knapper Ressourcen, stellt die Spieltheorie Methoden bereit, um rationale Entscheidungen der Aktionen fur interagierende Agenten zu analysieren. Basierend auf einem formalen Modell fur Ressourcenallokation in SN fuhrt diese Forschungsarbeit eine spieltheoretische Problemanalyse durch. Hierbei werden insbesondere mehrstufige Abhangigkeiten von Vereinbarungen berucksichtigt. Die Problemanalyse befast sich mit der Existenz sowie der Komplexitat der Berechnung optimaler Allokationen. Es wird ein verteiltes, Auktions-basiertes Allokationsprotokoll, welches overcommitments vermeidet, vorgeschlagen. Basierend auf dem spieltheoretischen Modell wird gezeigt, das das vorgeschlagene Protokoll overcommitments vermeidet und sozial optimale Allokationen fur einzelne Kundenanfragen unter bestimmten Annahmen garantiert. Daruber hinaus wird der Modellprufer Spin verwendet, um bestimmte formale Eigenschaften der Beschreibung des Protokolls zu beweisen. Abschliesend werden die Anwendbarkeit und der Nutzen des vorgeschlagenen Protokolls mittels Multiagentensimulation demonstriert. In den Simulationsexperimenten wird die Effizienz des Protokolls mit der optimalen Allokation als zentralisiertes Benchmark in unterschiedlichen Einstellungen (z.B. Netzwerktopologien oder Anzahl von Kunden- und Anbieter-Agenten) fur verschiedene Bietrichtlinien fur Anbieter verglichen.

[1]  Christof Weinhardt,et al.  How to Coordinate Value Generation in Service Networks – A Mechanism Design Approach , 2009 .

[2]  Nicholas R. Jennings Agent-Oriented Software Engineering , 1999, MAAMAW.

[3]  Michael P. Wellman,et al.  Combinatorial auctions for supply chain formation , 2000, EC '00.

[4]  Stefan Kirn,et al.  Towards model checking & simulation of a multi-tier negotiation protocol for service chains , 2010, AAMAS.

[5]  Gerhard Weiss,et al.  Multiagent Systems and Societies of Agents , 2000 .

[6]  Dipti Srinivasan,et al.  An Introduction to Multi-Agent Systems , 2010 .

[7]  Francis G. McCabe,et al.  Reference Model for Service Oriented Architecture 1.0 , 2006 .

[8]  Steven Tuecke,et al.  The Open Grid Services Architecture , 2004, The Grid 2, 2nd Edition.

[9]  M. Walker On the Nonexistence of a Dominant Strategy Mechanism for Making Optimal Public Decisions , 1980 .

[10]  William Vickrey,et al.  Counterspeculation, Auctions, And Competitive Sealed Tenders , 1961 .

[11]  Gerard J. Holzmann,et al.  Design and validation of computer protocols , 1991 .

[12]  Kevin Crowston,et al.  The interdisciplinary study of coordination , 1994, CSUR.

[13]  J. Neumann,et al.  Theory of games and economic behavior , 1945, 100 Years of Math Milestones.

[14]  Tran Cao Son,et al.  Semantic Web Services , 2001, IEEE Intell. Syst..

[15]  Nicholas R. Jennings,et al.  Managing commitments in multiple concurrent negotiations , 2005, Electron. Commer. Res. Appl..

[16]  Arthur C. Graesser,et al.  Is it an Agent, or Just a Program?: A Taxonomy for Autonomous Agents , 1996, ATAL.

[17]  Tuomas Sandholm,et al.  Distributed rational decision making , 1999 .

[18]  Nicholas R. Jennings,et al.  The Gaia Methodology for Agent-Oriented Analysis and Design , 2000, Autonomous Agents and Multi-Agent Systems.

[19]  Laura Giordano,et al.  Specifying and verifying interaction protocols in a temporal action logic , 2007, J. Appl. Log..

[20]  Nicholas R. Jennings,et al.  Intelligent agents: theory and practice , 1995, The Knowledge Engineering Review.

[21]  Mary Jo Bitner,et al.  Customer contributions and roles in service delivery , 1997 .

[22]  I. Melzer Web Services Description Language , 2010 .

[23]  Michael C. Jäger,et al.  A Model for the Aggregation of QoS in WS Compositions Involving Redundant Services , 2006, J. Digit. Inf. Manag..

[24]  Les Gasser,et al.  Social Conceptions of Knowledge and Action: DAI Foundations and Open Systems Semantics , 1991, Artif. Intell..

[25]  Gerard J. Holzmann,et al.  The Model Checker SPIN , 1997, IEEE Trans. Software Eng..

[26]  H. Simon,et al.  The sciences of the artificial (3rd ed.) , 1996 .

[27]  Michael N. Huhns,et al.  Multiagent systems and societies of agents , 1999 .

[28]  Stefan Kirn,et al.  A Multi-Tier Negotiation Protocol for Logistics Service Chains , 2010, ECIS.

[29]  Nicholas R. Jennings,et al.  Commitments and conventions: The foundation of coordination in multi-agent systems , 1993, The Knowledge Engineering Review.

[30]  Michael P. Wellman,et al.  Decentralized Supply Chain Formation: A Market Protocol and Competitive Equilibrium Analysis , 2003, J. Artif. Intell. Res..

[31]  Jörg Leukel,et al.  Towards Ontology-based QoS Aggregation for Composite Web Services , 2010, GI Jahrestagung.

[32]  Ioannis Kotsiopoulos,et al.  Enhancing Service Selection by Semantic QoS , 2009, ESWC.

[33]  Anand S. Rao,et al.  An architecture for real-time reasoning and system control , 1992, IEEE Expert.

[34]  Sarit Kraus,et al.  Negotiation and Cooperation in Multi-Agent Environments , 1997, Artif. Intell..

[35]  Amit P. Sheth,et al.  Modeling Quality of Service for Workflows and Web Service Processes , 2002 .

[36]  J. Nash,et al.  NON-COOPERATIVE GAMES , 1951, Classics in Game Theory.

[37]  P. K. Kannan,et al.  E-Service: New Directions in Theory and Practice , 2002 .

[38]  Drasko Tomic,et al.  Economics of the cloud computing , 2011, 2011 Proceedings of the 34th International Convention MIPRO.

[39]  Nicholas R. Jennings,et al.  Efficient mechanisms for the supply of services in multi-agent environments , 1998, ICE '98.

[40]  Martin Bichler,et al.  An experimental analysis of multi-attribute auctions , 2000, Decis. Support Syst..

[41]  Anne H. H. Ngu,et al.  QoS-aware middleware for Web services composition , 2004, IEEE Transactions on Software Engineering.

[42]  P. Hill Tangibles, intangibles and services: a new taxonomy for the classification of output , 1999 .

[43]  Edmund M. Clarke,et al.  Model Checking , 1999, Handbook of Automated Reasoning.

[44]  T. P. Hill On Goods and Services , 1977 .

[45]  Michael R. Genesereth,et al.  Software agents , 1994, CACM.

[46]  Carl Hewitt,et al.  Open Information Systems Semantics for Distributed Artificial Intelligence , 1991, Artif. Intell..

[47]  Daniel A. Menascé,et al.  Composing Web Services: A QoS View , 2004, IEEE Internet Comput..

[48]  Mark A Walker,et al.  On the Generic Nonoptimality of Dominant-Strategy Allocation Mechanisms: A General Theorem That Includes Pure Exchange Economies , 1990 .

[49]  Mark von Rosing,et al.  Business Process Model and Notation - BPMN , 2015, The Complete Business Process Handbook, Vol. I.

[50]  Ian Sommerville,et al.  QoSOnt: a QoS ontology for service-centric systems , 2005, 31st EUROMICRO Conference on Software Engineering and Advanced Applications.

[51]  Jean Gadrey,et al.  THE CHARACTERIZATION OF GOODS AND SERVICES: AN ALTERNATIVE APPROACH , 2000 .

[52]  Ken Binmore,et al.  Fun and games : a text on game theory , 1992 .

[53]  Arthur H. M. ter Hofstede,et al.  Capabilities: Describing What Services Can Do , 2003, ICSOC.

[54]  Nicolas Maudet,et al.  Negotiating Socially Optimal Allocations of Resources , 2011, J. Artif. Intell. Res..

[55]  David M. Booth,et al.  Web Services Architecture , 2004 .

[56]  D.C. Parkes,et al.  Distributed implementations of Vickrey-Clarke-Groves mechanisms , 2004, Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, 2004. AAMAS 2004..

[57]  Vijayan Sugumaran,et al.  Ontology-Based QoS Aggregation for Composite Web Services , 2013, Wirtschaftsinformatik.

[58]  Arthur H. M. ter Hofstede,et al.  What's in a Service? , 2002, Distributed and Parallel Databases.

[59]  Stefan Kirn,et al.  Adaptive SLA management along value chains for service individualization , 2009 .

[60]  A. H. Bond,et al.  An Analysis of Problems and Research in DAI , 1988 .

[61]  P. K. Kannan,et al.  E-service: a new paradigm for business in the electronic environment , 2003, CACM.

[62]  Nicolas Maudet,et al.  On the Communication Complexity of Multilateral Trading: Extended Report , 2005, Autonomous Agents and Multi-Agent Systems.

[63]  Cristiano Castelfranchi,et al.  Guarantees for Autonomy in Cognitive Agent Architecture , 1995, ECAI Workshop on Agent Theories, Architectures, and Languages.

[64]  Cristiano Castelfranchi,et al.  Artificial Intelligence Modelling social action for AI agents , 2003 .

[65]  Winfried Lamersdorf,et al.  Jadex: A BDI Reasoning Engine , 2005, Multi-Agent Programming.

[66]  Ian T. Foster,et al.  The Anatomy of the Grid: Enabling Scalable Virtual Organizations , 2001, Int. J. High Perform. Comput. Appl..

[67]  Devika Subramanian,et al.  Provably Bounded Optimal Agents , 1993, IJCAI.

[68]  Ronald M. Harstad,et al.  Computationally Manageable Combinational Auctions , 1998 .

[69]  Michael Wooldridge,et al.  An Introduction to Game Theory and Decision Theory , 2002 .

[70]  R. V. van Nieuwpoort,et al.  The Grid 2: Blueprint for a New Computing Infrastructure , 2003 .

[71]  Munindar P. Singh A semantics for speech acts , 1997, Annals of Mathematics and Artificial Intelligence.

[72]  D. Verma,et al.  Supporting Service Level Agreements on IP Networks , 1999 .

[73]  Marc S. Robinson,et al.  Collusion and the Choice of Auction , 1985 .

[74]  David J. Israel,et al.  Plans and resource‐bounded practical reasoning , 1988, Comput. Intell..

[75]  H. Moulin Axioms of Cooperative Decision Making , 1988 .

[76]  Schahram Dustdar Web Services Workflows—Composition, Co-Ordination, and Transactions in Service-Oriented Computing , 2004, Concurr. Eng. Res. Appl..

[77]  Michael Luck,et al.  A Formal Framework for Agency and Autonomy , 1995, ICMAS.

[78]  Nicholas R. Jennings,et al.  Coordinating multiple concurrent negotiations , 2004, Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, 2004. AAMAS 2004..

[79]  Nicolas Maudet,et al.  On optimal outcomes of negotiations over resources , 2003, AAMAS '03.

[80]  Yoav Shoham,et al.  Combinatorial Auctions , 2005, Encyclopedia of Wireless Networks.

[81]  Jerry R. Hobbs,et al.  DAML-S: Semantic Markup for Web Services , 2001, SWWS.

[82]  Wil M. P. van der Aalst,et al.  Workflow Patterns , 2004, Distributed and Parallel Databases.

[83]  Nicholas R. Jennings,et al.  On agent-based software engineering , 2000, Artif. Intell..

[84]  Michael Wooldridge,et al.  Reasoning about rational agents , 2000, Intelligent robots and autonomous agents.

[85]  Christian Kray,et al.  The eager bidder problem: a fundamental problem of DAI and selected solutions , 2002, AAMAS '02.

[86]  Eduardo Alonso Fernández,et al.  Rules of encounter: designing conventions for automated negotiation among computers , 1995 .

[87]  Arne Andersson,et al.  Integer programming for combinatorial auction winner determination , 2000, Proceedings Fourth International Conference on MultiAgent Systems.

[88]  Øystein Haugen,et al.  Enhancing UML to Formalize the FIPA Agent Interaction Protocol , 2008, ATOP@AAMAS.

[89]  David S. Johnson,et al.  Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .

[90]  Grady Booch,et al.  Object-Oriented Analysis and Design with Applications , 1990 .

[91]  Bo An,et al.  Characterizing Contract-Based Multiagent Resource Allocation in Networks , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[92]  Chris Rose,et al.  A Break in the Clouds: Towards a Cloud Definition , 2011 .

[93]  Reid G. Smith,et al.  The Contract Net Protocol: High-Level Communication and Control in a Distributed Problem Solver , 1980, IEEE Transactions on Computers.

[94]  Claudio Bartolini,et al.  Agent-based service composition through simultaneous negotiation in forward and reverse auctions , 2003, EC '03.

[95]  M. Satterthwaite,et al.  Efficient Mechanisms for Bilateral Trading , 1983 .

[96]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[97]  Martin Bichler,et al.  Design science in information systems research , 2006, Wirtschaftsinf..

[98]  C. W. Smith Auctions: The Social Construction of Value , 1989 .

[99]  Marlon Dumas,et al.  Specification of composite trading activities in supply chain management , 2005, 2005 IEEE International Conference on e-Technology, e-Commerce and e-Service.

[100]  E. Rasmussen Games and Information , 1989 .

[101]  Nicholas R. Jennings,et al.  Developing a bidding agent for multiple heterogeneous auctions , 2003, TOIT.

[102]  Asit Dan,et al.  Web services agreement specification (ws-agreement) , 2004 .

[103]  Marlon Dumas,et al.  Specification and execution of composite trading activities , 2007, Electron. Commer. Res..

[104]  Gero Mühl,et al.  QoS aggregation for Web service composition using workflow patterns , 2004 .

[105]  H. Simon,et al.  Models of Bounded Rationality: Empirically Grounded Economic Reason , 1997 .

[106]  Victor R. Lesser,et al.  Leveled Commitment Contracts and Strategic Breach , 2001, Games Econ. Behav..

[107]  Michael Wooldridge,et al.  Game Theory and Decision Theory in Multi-Agent Systems , 2002, Autonomous Agents and Multi-Agent Systems.

[108]  Jerry R. Green,et al.  Characterization of Satisfactory Mechanisms for the Revelation of Preferences for Public Goods , 1977 .

[109]  Harlan D. Mills,et al.  Theory of Modules , 1987, IEEE Transactions on Software Engineering.

[110]  Erich Schikuta,et al.  Aggregating Hierarchical Service Level Agreements in Business Value Networks , 2009, BPM.

[111]  Ian Sommerville,et al.  QoSOnt: a QoS ontology for service-centric systems , 2005 .

[112]  Erik Christensen,et al.  WSDL: Web Service Description Language , 2001 .

[113]  Jeffrey S. Rosenschein,et al.  Rational interaction: cooperation among intelligent agents , 1986 .

[114]  Christopher D. Walton,et al.  Verifiable agent dialogues , 2007, J. Appl. Log..

[115]  Les Gasser,et al.  Research Directions for Service-Oriented Multiagent Systems , 2005, IEEE Internet Comput..

[116]  E. Maskin,et al.  The Implementation of Social Choice Rules: Some General Results on Incentive Compatibility , 1979 .

[117]  Howard Raiffa,et al.  Decision analysis: introductory lectures on choices under uncertainty. 1968. , 1969, M.D.Computing.

[118]  Allen Newell,et al.  The Knowledge Level , 1989, Artif. Intell..

[119]  Victor R. Lesser,et al.  Issues in Automated Negotiation and Electronic Commerce: Extending the Contract Net Framework , 1997, ICMAS.

[120]  Mike P. Papazoglou,et al.  Service-oriented computing: concepts, characteristics and directions , 2003, Proceedings of the Fourth International Conference on Web Information Systems Engineering, 2003. WISE 2003..

[121]  Marc-Philippe Huget,et al.  Interaction Protocol Engineering in Multiagent Systems , 2003 .

[122]  Yann Chevaleyre,et al.  Issues in Multiagent Resource Allocation , 2006, Informatica.

[123]  Herbert A. Simon,et al.  Models of Man: Social and Rational. , 1957 .

[124]  Nicholas R. Jennings,et al.  An integrated trust and reputation model for open multi-agent systems , 2006, Autonomous Agents and Multi-Agent Systems.

[125]  Victor R. Lesser,et al.  Efficient Management of Multi-Linked Negotiation Based on a Formalized Model , 2005, Autonomous Agents and Multi-Agent Systems.

[126]  Sarah Rothstein E Service New Directions In Theory And Practice , 2016 .

[127]  Randy H. Katz,et al.  A view of cloud computing , 2010, CACM.