This paper presents a game theory based cooperative resource cost negotiation scheme using agents, in a cloud computing environment. The scheme comprises of consumer agents, broker agents, and resource provider agents. The resource cost negotiation takes place between these agencies to expedite flexible pricing of cloud services, which adopts a many-to-many negotiation model for negotiating the cost of the resources between the consumers and providers. The scheme is simulated by using Java agent development environment (JADE) framework. The results obtained are compared with existing works on game theory and agent based models. It is observed that the consumers and brokers achieve lesser negotiation time and higher successful job offers at lesser cost, while IaaS cloud service providers (ICSPs) achieve higher resource utilization rate. Payoffs are also analyzed to test the effectiveness of the proposed scheme.
WooldridgeM., An introduction to multiagent systems, John Wiley & Sons, Second Edition, 2016.
2.
LiX.ZhangH. and ZhangY., Deploying mobile computation in cloud service, Proceedings of the 1st International Conference on Cloud Computing, Beijing, China (2009), 301–311.
3.
BirjeM.N.ManviS.S. and DasS.K., Reliable resource brokering scheme in wireless grids based on non-cooperative bargaining game, Journal of Network and Computer Applications, Elsevier39(1) (2014), 266–279.
4.
MashayekhyL. and TusharW., Incentive-compatible online mechanism for resource provisioning and allocation in cloud, Proceedings of 7th International Conference on Cloud Computing, IEEE, Alaska, USA (2018), 56–61.
5.
ShyamG.K. and ManviS.S., Resource allocation in cloud computing using agents, Proceedings of the IEEE International Conference on Advance Computing Conference (IACC), Bangalore, India (2016), 374–380.
6.
SmithR.G., The contract net protocol: High-level communication and control in a distributed problem solver, IEEE Transactions on Computers3(5) (2017), 1104–1113.
7.
SimK.M. and WangS.Y., Flexible negotiation agent with relaxed decision rules, Transactions on Systems, Man and Cybernetics, IEEE34(3) (2016), 1602–1608.
8.
HsiehY. and CaoJ., Performance evaluation of an agent-based resource management infrastructure for grid computing, Proceedings of 1st International Symposium on Cluster Computing and the Grid, IEEE, Cartagena, Colombia (2017), 311–318.
9.
CaoJ.KerbysonD.J. and NuddG.R., Performance evaluation of an agent-based resource management infrastructure for grid computing, Proceedings of 1st IEEE International Symposium on Cluster Computing and the Grid3(2) (2011), 311–318.
10.
CaoJ.KerbysonD.J. and NuddG.R., High performance service discovery in large-scale multiagent and mobile-agent systems, International Journal of Software Engineering and Knowledge Engineering11(5) (2012), 621–641.
11.
CaoJ.KerbysonD.J. and NuddG.R., Use of agent based service discovery for resource management in metacomputing environment, Proceedings of 7th International Euro-Par Conference, Beijing, China (2014), 882–886.
12.
CasanovaH. and DongarraJ., Applying NetSolve’s network-enabled server, Journal of Computational Science & Engineering, IEEE5(3) (2012), 57–67.
13.
DuW.RajagopalanB. and ChapinS.J., Resource management in legion, Journal of Future Generation Computer Systems, Elsevier15(5) (2013), 583–594.
14.
FosterI. and KesselmanC., The grid: Blueprint for a new computing infrastructure, Morgan-Kaufmann, 7th Edition, 2012.
15.
UlleE.NicolasM.FaribaS. and FrancescaT., Negotiating socially optimal allocations of resources, Journal of Artificial Intelligence25(5) (2006), 315–348.
16.
BuyyaR.AbramsonD.GiddyJ. and StockingerH., Economic models for resource management and scheduling in grid computing, Journal of Concurrency and Computation: Practice and Experience (CCPE)14(13) (2012), 1507–1542.
17.
LangF., Developing dynamic strategies for multi-issue automated contracting in the agent based commercial grid, Proceedings of IEEE International Symposium on Cluster Computing and Grid, Delhi, India (2005), 342–349.
18.
GimpelH.LudwigH.DanA. and KearneyR., PANDA: Specifying policies for automated negotiations of service contracts, Proceedings of International Conference on Service Oriented Computing, LNCS, Springer-Verlag, Heidelberg (2003), 287–302.
19.
SimK.M., G-commerce, market-driven G-negotiation agents and grid resource management, IEEE Transactions on Systems, Man and Cybernetics36(6) (2013), 1381–1394.
20.
SimK.M. and NgK.F., A relaxed criteria bargaining protocol for grid resource management, Proceedings of Sixth IEEE International Symposium on Cluster Computing and Grid Workshops, Singapore (2012), 1–13.
21.
De SarkarA.RoyS.GhoshD.MukhopadhyayR. and MukherjeeN., An adaptive execution scheme for achieving guaranteed performance in computational grids, Journal of Grid Computing8(1) (2010), 109–131.
22.
GhoshP.BasuK. and DasS.K., A game theory-based pricing strategy to support single/ multiclass job allocation schemes for bandwidth-constrained distributed computing systems, IEEE Transactions on Parallel and Distributed Systems18(3) (2007), 289–306.
23.
SimK.M. and ShiB., Concurrent negotiation and coordination for controlling grid resource co-allocation, IEEE Transactions on Systems, Man and Cybernetics40(2) (2010), 753–766.
24.
SimK.M., A market-driven model for designing negotiation agents, Journal of Computational Intelligence18(4) (2002), 618–637.
25.
SimK.M., A market-driven model for designing negotiation agents, Journal of Computational Intelligence, Springer18(4) (2002), 618–637.
26.
GrinevasS. and WacheH., Cloud broker: Bringing intelligence to cloud, Proceedings of Third IEEE International Conference on Cloud Computing, Singapore (2012), 544–545.
27.
ChaisinS. and LeeB.S, Optimal virtual placement across multiple cloud providers, Proceedings of Fourth IEEE International Conference on Services Computing, Vienna, Austria (2016), 103–110.
28.
GattiN.GiuntaF.D and MarinoS., Alternative offer bargaining with one-sided uncertain deadline, Journal of Artificial Intelligence172(8) (2011), 1119–1157.
29.
PascualF.RzadcaK. and TrystramD., Cooperation in multiorganization scheduling, Proceedings of International Euro-Par Conference, Spain (2017), 65–71.
30.
ManviS.S. and ShyamG.K., Resource management for IaaS in cloud computing: A survey, Elsevier Journal of Network and Computer Applications41(1) (2014), 424–440.
31.
ShyamG.K. and ManviS.S., Modeling resource virtualization concept in cloud computing using finite state machines, International Journal of Cloud Computing, Inderscience 1(5) (2015), 251–261.
32.
RahimiM.R. and VenkatasubramanianN., MAPCloud: Mobile applications on an elastic and scalable 2-tier cloud architecture, Proceedings of 5th International Conference on Utility and Cloud Computing, IEEE, Chicago, USA (2012), 83–90.
33.
RahimiM.R. and VenkatasubramanianN., MuSIC: Mobility-aware optimal service allocation in mobile cloud computing, Proceedings of 6th International Conference on Cloud Computing, IEEE, Chicago, USA (2013), 75–82.
34.
RahimiM.R.RenJ. and LiC.H., Mobile cloud computing: A survey, state of art and future directions, Journal of Mobile and Network Applications, Springer19(2) (2014), 133–143.
35.
WeiG. and AthanasiosV., A game theoretic method of fair resource allocation for cloud computing services, Journal of Supercomputing, Springer54(2) (2010), 252–259.
36.
ZhangH. and WernerF., A framework for truthful online auctions in cloud computing with heterogeneous user demands, Proceedings of International Conference on Computer Communications, IEEE, Turin, Italy (2013), 1510–1518.
37.
MashayekhyL. and LiL., An online mechanism for resource allocation and pricing in clouds, Transactions on Computers, IEEE29(4) (2014), 424–440.
GarciaJ.O. and SimK.M., Family of heuristics for agent-based elastic cloud bag-of-tasks for concurrent scheduling, Elsevier Journal of Future Generation Computer Systems29(7) (2013), 1682–1699.
40.
TranT.T. and BeckJ.C., Decomposition methods for the parallel machine scheduling problem with setups, Journal of Computing, Springer28(1) (2015), 83–95.
41.
ShyamG.K. and ManviS.S., Virtual resource prediction in cloud computing: A Bayesian approach, Journal of Network and Computer Applications, Elsevier65(1) (2016), 44–55.
42.
IaaS providers, Available online at: http://www.tomsitpro.com/articles/iaas-providers,1–1560.html, Accessed on 2016.
43.
ShyamG.K. and ManviS.S., Co-operation based game theoretic approach for resource bargaining in cloud computing environment, International Conference on Advances in Computing, Communications and Informatics (ICACCI), Kochi, India, August 7-8, 374–380.
44.
UllerI.KowalczykR. and BraunP., Towards agent-based coalition formation for service composition, Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT), Vienna, Austria (2006), 73–80.
45.
PascualF.RzadcaK. and TrystramD., Cooperation in multi-organization scheduling, Proceedings of International Euro-Par Conference, Beijing, China (2011).
46.
TaylorG., Iterated prisoner’s dilemma in MATLAB: Archive for the game theory, Available at http://maths.straylight.co.uk/archives/category/game-theory, Accessed on March 29, 2007.
47.
GrozevN. and BuyyaR., Inter-cloud architectures and application brokering: Taxonomy and survey, Journal of Concurrency and Computation: Practice and Experience (CCPE)44(3) (2014), 269–290.
48.
BuyyaR.AbramsonD.GiddyJ. and StockingerH., Economic models for resource management and scheduling in grid computing, Journal of Concurrency and Computation: Practice and Experience (CCPE)14(13) (2012), 1507–1542.
