Service acceptability rate and user satisfaction are becoming key factors to avoid client churn and secure the success of any Software as a Service (SaaS) provider. Nevertheless, the provider must also accommodate fluctuating workloads and minimize the cost it pays to rent resources from the cloud. To address these contradicting concerns, most of existing works carry out resource management unilaterally by the provider. Consequently, end-user preferences and her subjective acceptability of the service are mostly ignored. In order to assess user satisfaction and service acceptability recent studies in the domain of Quality of Experience (QoE) recommend providers to use quantiles and percentile to gauge user service acceptability precisely. In this article we propose an elastic, load-spike proof, and adaptive one-to-many negotiation mechanism to improve the service acceptability of an open SaaS provider. Based on quantile estimation of service acceptability rate and a learned model of the user negotiation strategy, this mechanism adjusts the provider negotiation process in order to guarantee the desired service acceptability rate while meeting the budget limits of the provider and accommodating workload fluctuations. The proposed mechanism is implemented and its results are examined and analyzed.
negotiation, adaptation, acceptability rate, SaaS, cloud computing
Accenture. (2013). Accenture 2013 global consumer pulse survey global & u.s. key findings report. Dublin, Ireland.
Al-Dhuraibi Y., Paraiso F., Djarallah N., Merle P. (2018). Elasticity in cloud computing: state of the art and research challenges. IEEE Transactions on Services Computing, vol. 11, no 2, p. 430–447.
An B., Lesser V., Irwin D., Zink M. (2010). Automated negotiation with decommitment for dynamic resource allocation in cloud computing. In Proceedings of the 9th international conference on autonomous agents and multiagent systems: volume 1-volume 1, p. 981–988.
Baarslag T., Hendrikx M. J., Hindriks K. V., Jonker C. M. (2015). Learning about the opponent in automated bilateral negotiation: a comprehensive survey of opponent modeling techniques. Autonomous Agents and Multi-Agent Systems, p. 1–50.
Casalicchio E., Silvestri L. (2013). Mechanisms for sla provisioning in cloud-based service providers. Computer Networks, vol. 57, no 3, p. 795–810.
ETSI. (s. d.). European telecommunications standards institute. http://www.etsi.org/.
Faratin P., Sierra C., Jennings N. R. (1998). Negotiation decision functions for autonomous agents. Robotics and Autonomous Systems, vol. 24, no 3, p. 159–182.
Hobfeld T., Schatz R., Egger S. (2011). Sos: The mos is not enough! In Quality of multimedia experience (qomex), 2011 third international workshop on, p. 131–136.
Hoßfeld T., Heegaard P. E., Varela M., Möller S. (2016). Qoe beyond the mos: an in-depth look at qoe via better metrics and their relation to mos. Quality and User Experience, vol. 1, no 1, p. 2.
Hou C. (2004). Predicting agents tactics in automated negotiation. In Intelligent agent technology, 2004. (iat 2004). proceedings. ieee/wic/acm international conference on, p. 127–133.
Index C. V. N. (2016). Cisco vni forecast and methodology, 2015-2020. cisco white paper, june 1, 2016.
ITU. (s. d.). International telecommunications union. https://www.itu.int/.
Ji S.-j., Zhang C.-j., Sim K.-M., Leung H.-f. (2014). A one-shot bargaining strategy for dealing with multifarious opponents. Applied intelligence, vol. 40, no 4, p. 557–574.
Lomuscio A. R.,Wooldridge M., Jennings N. R. (2003). A classification scheme for negotiation in electronic commerce. Group Decision and Negotiation, vol. 12, no 1, p. 31–56.
Mansour K., Kowalczyk R. (2012). A meta-strategy for coordinating of one-to-many negotiation over multiple issues. In Foundations of intelligent systems, p. 343–353. Springer.
Mansour K., Kowalczyk R. (2014). On dynamic negotiation strategy for concurrent negotiation over distinct objects. In Novel insights in agent-based complex automated negotiation, p. 109–124. Springer.
Möller S., Raake A. (2014). Quality of experience. Springer. Najjar A. (2015). Multi-agent negotiation for qoe-aware cloud elasticity management. Thèse de doctorat non publiée, École nationale supérieure des mines de Saint-Étienne.
Najjar A., Boissier O., Picard G. (2017a). An adaptive one-to-many negotiation to improve the service acceptability of an open saas provider. In International workshop on agent-based complex automated negotiations (acan).
Najjar A., Boissier O., Picard G. (2017b). Aquaman: An adaptive qoe-aware negotiation mechanism for saas elasticity management (extended abstract). In International conference on autonomous agents and multi-agent systems (aamas).
Najjar A., Gravier C., Serpaggi X., Boissier O. (2016, Oct). Modeling user expectations satisfaction for saas applications using multi-agent negotiation. In 2016 ieee/wic/acm international conference on web intelligence (wi), p. 399-406.
Najjar A., Mualla Y., Boissier O., Picard G. (2017, Aug). Aquaman: Qoe-driven cost-aware mechanism for saas acceptability rate adaptation. In 2017 ieee/wic/acm international conference on web intelligence (wi).
Najjar A., Serpaggi X., Gravier C., Boissier O. (2014). Survey of elasticity management solutions in cloud computing. In Continued rise of the cloud, p. 235–263. Springer.
Najjar A., Serpaggi X., Gravier C., Boissier O. (2016). Multi-agent systems for personalized qoe-management. In Teletraffic congress (itc 28), 2016 28th international, vol. 3, p. 1–6.
North M. J., Howe T. R., Collier N. T., Vos J. (2005). The repast simphony runtime system. In Agent 2005 conference on generative social processes, models, and mechanisms. argonne, illinois, usa: Argonne national laboratory.
Pruitt D. G. (2013). Negotiation behavior. Academic Press.
Rahwan I., Kowalczyk R., Pham H. H. (2002). Intelligent agents for automated one-to-many e-commerce negotiation. In Australian computer science communications, vol. 24, p. 197–204.
Reichl P., Egger S., Schatz R., D’Alconzo A. (2010). The logarithmic nature of qoe and the role of the weber-fechner law in qoe assessment. In Communications (icc), 2010 ieee international conference on, p. 1–5.
Richter J., Baruwal Chhetri M., Kowalczyk R., Bao Vo Q. (2012). Establishing composite slas through concurrent qos negotiation with surplus redistribution. Concurrency and Computation: Practice and Experience, vol. 24, no 9, p. 938–955.
Négociation multi-agents « one-to-many » pour le SaaS 625
Sackl A., Schatz R. (2013). Evaluating the impact of expectations on end-user quality perception. In Proceedings of international workshop perceptual quality of systems (pqs), p. 122–128.
Savitzky A., Golay M. J. (1964). Smoothing and differentiation of data by simplified least squares procedures. Analytical chemistry, vol. 36, no 8, p. 1627–1639.
Schatz R., Fiedler M., Skorin-Kapov L. (2014). Qoe-based network and application management. In Quality of experience, p. 411–426. Springer.
Siebenhaar M., Lampe U., Schuller D., Steinmetz R. et al. (2012). Concurrent negotiations in cloud-based systems. In Economics of grids, clouds, systems, and services, p. 17–31. Springer.
Son S., Sim K. M. (2012). A price-and-time-slot-negotiation mechanism for cloud service reservations. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 42, no 3, p. 713–728.
Talia D. (2012). Clouds meet agents: Toward intelligent cloud services. IEEE Internet Computing, no 2, p. 78–81.
Thompson L. L., Wang J., Gunia B. C. (2010). Negotiation. Annual review of psychology, vol. 61, p. 491–515.
Thurstone L. L. (1927). Psychophysical analysis. The American journal of psychology, vol. 38, no 3, p. 368–389.
Wooldridge M. (2009). An introduction to multiagent systems. John Wiley & Sons.
Zeithaml V. A., Berry L. L., Parasuraman A. (1993). The nature and determinants of customer expectations of service. Journal of the academy of Marketing Science, vol. 21, no 1, p. 1–12.