Consistency in scalable systems

[EN] While eventual consistency is the general consistency guarantee ensured in cloud environments, stronger guarantees are in fact achievable. We show how scalable and highly available systems can provide processor, causal, sequential and session consistency during normal functioning. Failures and network partitions negatively affect consistency and generate divergence. After the failure or the partition, reconciliation techniques allow the system to restore consistency.This work has been supported by EU FEDER and Spanish MICINN under research grants TIN2009-14460-C03-01 and TIN2010-17193.Ruiz Fuertes, MI.; Pallardó Lozoya, MR.; Muñoz-Escoí, FD. (2012). Consistency in scalable systems. En On the Move to Meaningful Internet Systems: OTM 2012. Springer Verlag (Germany). 7566:549-565. https://doi.org/10.1007/978-3-642-33615-7_7S5495657566Ahamad, M., Bazzi, R.A., John, R., Kohli, P., Neiger, G.: The power of processor consistency. In: Proceedings of the Fifth Annual ACM Symposium on Parallel Algorithms and Architectures, SPAA 1993, pp. 251–260. ACM, New York (1993), http://doi.acm.org/10.1145/165231.165264Alvarez, A., Arévalo, S., Cholvi, V., Fernández, A., Jiménez, E.: On the Interconnection of Message Passing Systems. Inf. Process. Lett. 105(6), 249–254 (2008)Amazon Web Services LLC: Amazon Simple Storage Service (S3). Website (March 2011), http://aws.amazon.com/s3/Baker, J., Bond, C., Corbett, J.C., Furman, J.J., Khorlin, A., Larson, J., Léon, J., Li, Y., Lloyd, A., Yushprakh, V.: Megastore: Providing Scalable, Highly Available Storage for interactive services. In: 5th Biennial Conf. on Innovative Data Systems Research (CIDR), Asilomar, CA, USA, pp. 223–234 (January 2011)Baldoni, R., Beraldi, R., Friedman, R., van Renesse, R.: The Hierarchical Daisy Architecture for Causal Delivery. Distributed Systems Engineering 6(2), 71–81 (1999)Bernstein, P.A., Hadzilacos, V., Goodman, N.: Concurrency Control and Recovery in Database Systems. Addison-Wesley (1987)Bernstein, P.A., Reid, C.W., Das, S.: Hyder - A Transactional Record Manager for Shared Flash. In: 5th Biennial Conf. on Innovative Data Systems Research (CIDR), Asilomar, CA, USA, pp. 9–20 (January 2011)Bershad, B.N., Zekauskas, M.J., Sawdon, W.A.: The Midway Distributed Shared Memory System. In: Proc. IEEE CompCon Conf. (1993)Brewer, E.A.: Towards Robust Distributed Systems (Abstract). In: Proc. ACM Symp. Princ. Distrib. Comput., p. 7 (2000)Budhiraja, N., Marzullo, K., Schneider, F.B., Toueg, S.: The Primary-Backup Approach. In: Mullender, S.J. (ed.) Distributed Systems, 2nd edn., ch. 8, pp. 199–216. Addison-Wesley, ACM Press (1993)Campbell, D.G., Kakivaya, G., Ellis, N.: Extreme Scale with Full SQL Language Support in Microsoft SQL Azure. In: Intnl. Conf. on Mngmnt. of Data (SIGMOD), pp. 1021–1024. ACM, New York (2010), http://doi.acm.org/10.1145/1807167.1807280Cholvi, V., Jiménez, E., Anta, A.F.: Interconnection of distributed memory models. J. Parallel Distrib. Comput. 69(3), 295–306 (2009)Cooper, B.F., Ramakrishnan, R., Srivastava, U., Silberstein, A., Bohannon, P., Jacobsen, H., Puz, N., Weaver, D., Yerneni, R.: PNUTS: Yahoo!’s hosted data serving platform. PVLDB 1(2), 1277–1288 (2008)Daudjee, K., Salem, K.: Lazy Database Replication with Ordering Guarantees. In: Proc. Int. Conf. Data Eng., pp. 424–435. IEEE-CS (2004)Daudjee, K., Salem, K.: Lazy Database Replication with Snapshot Isolation. In: Proc. Int. Conf. Very Large Data Bases, pp. 715–726. ACM (2006)DeCandia, G., Hastorun, D., Jampani, M., Kakulapati, G., Lakshman, A., Pilchin, A., Sivasubramanian, S., Vosshall, P., Vogels, W.: Dynamo: Amazon’s Highly Available Key-value Store. In: ACM Symp. Oper. Syst. Princ., pp. 205–220 (2007)Fernández, A., Jiménez, E., Cholvi, V.: On the interconnection of causal memory systems. J. Parallel Distrib. Comput. 64(4), 498–506 (2004)Gilbert, S., Lynch, N.A.: Brewer’s Conjecture and the Feasibility of Consistent, Available, Partition-Tolerant Web Services. ACM SIGACT News 33(2), 51–59 (2002)Goodman, J.R.: Cache Consistency and Sequential Consistency. Tech. Rep. 61, SCI Committee (March 1989)Gray, J., Helland, P., O’Neil, P.E., Shasha, D.: The Dangers of Replication and a Solution. In: Proc. ACM SIGMOD Int. Conf. Manage. Data, pp. 173–182. ACM (1996)Helland, P., Campbell, D.: Building on Quicksand. In: Proc. Bienn. Conf. Innov. Data Syst. Research (2009), www.crdrdb.orgHutto, P., Ahamad, M.: Slow Memory: Weakening Consistency to Enhance Concurrency in Distributed Shared Memories. In: Proceedings of the 10th International Conference on Distributed Computing Systems, pp. 302–311 (May 1990)Johnson, S., Jahanian, F., Shah, J.: The Inter-group Router Approach to Scalable Group Composition. In: ICDCS, pp. 4–14 (1999)Kraska, T., Hentschel, M., Alonso, G., Kossmann, D.: Consistency Rationing in the Cloud: Pay only when it matters. PVLDB 2(1), 253–264 (2009)Lamport, L.: How to Make a Multiprocessor Computer that Correctly Executes multiprocess programs. IEEE Trans. Computers 28(9), 690–691 (1979)Lipton, R.J., Sandberg, J.S.: Pram: A Scalable Shared Memory. Tech. Rep. CS-TR-180-88, Princeton University, Department of Computer Science (September 1988)Mosberger, D.: Memory Consistency Models. Operating Systems Review 27(1), 18–26 (1993)Ruiz-Fuertes, M.I., Muñoz-Escoí, F.D.: Refinement of the One-Copy Serializable Correctness Criterion. Tech. Rep. ITI-SIDI-2011/004, Instituto Tecnológico de Informática, Valencia, Spain (November 2011)Stonebraker, M., Madden, S., Abadi, D.J., Harizopoulos, S., Hachem, N., Helland, P.: The End of an Architectural Era (It’s Time for a Complete Rewrite). In: 33rd Intnl. Conf. on Very Large Data Bases (VLDB), pp. 1150–1160. ACM Press, Vienna (2007)Terry, D.B., Demers, A.J., Petersen, K., Spreitzer, M., Theimer, M., Welch, B.B.: Session Guarantees for Weakly Consistent Replicated Data. In: Proc. Int. Conf. Parallel Distrib. Inform. Syst., pp. 140–149. IEEE-CS (1994)Vogels, W.: Eventually Consistent. Communications of the ACM (CACM) 52(1), 40–44 (2009)VoltDB, Inc.: VoltDB technical overview: A high performance, scalable RDBMS for Big Data, high velocity OLTP and realtime analytics. Website (April 2012), http://voltdb.com/sites/default/files/PDFs/VoltDBTechnicalOverview_April_2012.pdfWiesmann, M., Schiper, A.: Comparison of Database Replication Techniques Based on Total Order Broadcast. IEEE T. Knowl. Data En. 17(4), 551–566 (2005

INTERCONEXIÓN DE SISTEMAS DISTRIBUIDOS

El objetivo de esta tesis de máster es realizar un estudio bibliográfico de las principales publicaciones que plantean arquitecturas, protocolos y soluciones de interconexión de sistemas en el estado del arte para resolver el problema de la escalabilidad en sistemas distribuidos de memoria compartida y en sistemas distribuidos de paso de mensajes.Pallardó Lozoya, MR. (2010). INTERCONEXIÓN DE SISTEMAS DISTRIBUIDOS. http://hdl.handle.net/10251/11308Archivo delegad

Scalability approaches for causal multicast: a survey

The final publication is available at Springer via http://dx.doi.org/10.1007/s00607-015-0479-0Many distributed services need to be scalable: internet search, electronic commerce, e-government... In order to achieve scalability, high availability and fault tolerance, such applications rely on replicated components. Because of the dynamics of growth and volatility of customer markets, applications need to be hosted by adaptive, highly scalable systems. In particular, the scalability of the reliable multicast mechanisms used for supporting the consistency of replicas is of crucial importance. Reliable multicast might propagate updates in a pre-determined order (e.g., FIFO, total or causal). Since total order needs more communication rounds than causal order, the latter appears to be the preferable candidate for achieving multicast scalability, although the consistency guarantees based on causal order are weaker than those of total order. This paper provides a historical survey of different scalability approaches for reliable causal multicast protocols.This work was supported by European Regional Development Fund (FEDER) and Ministerio de Economia y Competitividad (MINECO) under research Grant TIN2012-37719-C03-01.Juan Marín, RD.; Decker, H.; Armendáriz Íñigo, JE.; Bernabeu Aubán, JM.; Muñoz Escoí, FD. (2016). Scalability approaches for causal multicast: a survey. Computing. 98(9):923-947. https://doi.org/10.1007/s00607-015-0479-0S923947989Adly N, Nagi M (1995) Maintaining causal order in large scale distributed systems using a logical hierarchy. In: IASTED Intnl Conf on Appl Inform, pp 214–219Aguilera MK, Chen W, Toueg S (1997) Heartbeat: a timeout-free failure detector for quiescent reliable communication. In: 11th Intnl Wshop on Distrib Alg (WDAG), Saarbrücken, pp 126–140Almeida JB, Almeida PS, Baquero C (2004) Bounded version vectors. In: 18th Intnl Conf Distrib Comput (DISC), Amsterdam, pp 102–116Almeida PS, Baquero C, Fonte V (2008) Interval tree clocks. In: 12th Intnl Conf Distrib Syst (OPODIS), Luxor, pp 259–274Almeida S, Leitão J, Rodrigues LET (2013) ChainReaction: a causal+ consistent datastore based on chain replication. In: 8th EuroSys Conf, Czech Republic, pp 85–98Álvarez A, Arévalo S, Cholvi V, Fernández A, Jiménez E (2008) On the interconnection of message passing systems. Inf Process Lett 105(6):249–254Amir Y, Stanton J (1998) The Spread wide area group communication system. Tech. rep., CDNS-98-4, The Center for Networking and Distributed Systems, The Johns Hopkins UnivAmir Y, Dolev D, Kramer S, Malki D (1992) Transis: a communication subsystem for high availability. In: 22nd Intnl Symp Fault-Tolerant Comp (FTCS), Boston, pp 76–84Anastasi G, Bartoli A, Spadoni F (2001) A reliable multicast protocol for distributed mobile systems: design and evaluation. IEEE Trans Parallel Distrib Syst 12(10):1009–1022Bailis P, Ghodsi A, Hellerstein JM, Stoica I (2013) Bolt-on causal consistency. In: Intnl Conf Mgmnt Data (SIGMOD), New York, pp 761–772Baldoni R, Raynal M, Prakash R, Singhal M (1996) Broadcast with time and causality constraints for multimedia applications. In: 22nd Intnl Euromicro Conf, Prague, pp 617–624Baldoni R, Friedman R, van Renesse R (1997) The hierarchical daisy architecture for causal delivery. In: 17th Intnl Conf Distrib Comput Syst (ICDCS), Maryland, pp 570–577Ban B (2002) JGroups—a toolkit for reliable multicast communication. http://www.jgroups.orgBaquero C, Almeida PS, Shoker A (2014) Making operation-based CRDTs operation-based. In: 14th Intnl Conf Distrib Appl Interop Syst (DAIS), Berlin, pp 126–140Benslimane A, Abouaissa A (2002) Dynamical grouping model for distributed real time causal ordering. Comput Commun 25:288–302Birman KP, Joseph TA (1987) Reliable communication in the presence of failures. ACM Trans Comput Syst 5(1):47–76Birman KP, Schiper A, Stephenson P (1991) Lightweigt causal and atomic group multicast. ACM Trans Comput Syst 9(3):272–314Cachin C, Guerraoui R, Rodrigues LET (2011) Introduction to reliable and secure distributed programming, 2nd edn. Springer, BerlinChandra P, Gambhire P, Kshemkalyani AD (2004) Performance of the optimal causal multicast algorithm: a statistical analysis. IEEE Trans Parall Distr 15(1):40–52Chandra TD, Toueg S (1996) Unreliable failure detectors for reliable distributed systems. J ACM 43(2):225–267de Juan-Marín R, Cholvi V, Jiménez E, Muñoz-Escoí FD (2009) Parallel interconnection of broadcast systems with multiple FIFO channels. In: 11th Intnl Symp on Distrib Obj, Middleware and Appl (DOA), Vilamoura, LNCS, vol 5870, pp 449–466Défago X, Schiper A, Urbán P (2004) Total order broadcast and multicast algorithms: taxonomy and survey. ACM Comput Surv 36(4):372–421Demers AJ, Greene DH, Hauser C, Irish W, Larson J, Shenker S, Sturgis HE, Swinehart DC, Terry DB (1987) Epidemic algorithms for replicated database maintenance. In: 6th ACM Symp on Princ of Distrib Comput (PODC), Canada, pp 1–12Du J, Elnikety S, Roy A, Zwaenepoel W (2013) Orbe: scalable causal consistency using dependency matrices and physical clocks. In: ACM Symp on Cloud Comput (SoCC), Santa Clara, pp 11:1–11:14Fernández A, Jiménez E, Cholvi V (2000) On the interconnection of causal memory systems. In: 19th Annual ACM Symp on Princ of Distrib Comput (PODC), Portland, pp 163–170Fidge CJ (1988) Timestamps in message-passing systems that preserve the partial ordering. In: 11th Australian Comput Conf, pp 56–66Friedman R, Vitenberg R, Chockler G (2003) On the composability of consistency conditions. Inf Process Lett 86(4):169–176Gilbert S, Lynch N (2002) Brewer’s conjecture and the feasibility of consistent, available, partition-tolerant web services. SIGACT News 33(2):51–59Gray J, Helland P, O’Neil PE, Shasha D (1996) The dangers of replication and a solution. In: SIGMOD Conf, pp 173–182Hadzilacos V, Toueg S (1993) Fault-tolerant broadcasts and related problems. In: Mullender S (ed) Distributed systems, chap 5, 2nd edn. ACM Press, pp 97–145Johnson S, Jahanian F, Shah J (1999) The inter-group router approach to scalable group composition. In: 19th Intnl Conf on Distrib Comput Syst (ICDCS), Austin, pp 4–14Kalantar MH, Birman KP (1999) Causally ordered multicast: the conservative approach. In: 19th Intnl Conf on Distrib Comput Syst (ICDCS), Austin, pp 36–44Kawanami S, Enokido T, Takizawa M (2004) A group communication protocol for scalable causal ordering. In: 18th Intnl Conf on Adv Inform Netw Appl (AINA), Fukuoka, pp 296–302Kawanami S, Nishimura T, Enokido T, Takizawa M (2005) A scalable group communication protocol with global clock. In: 19th Intnl Conf on Adv Inform Netw Appl (AINA), Taipei, pp 625–630Kshemkalyani AD, Singhal M (1998) Necessary and sufficient conditions on information for causal message ordering and their optimal implementation. Distrib Comput 11(2):91–111Kshemkalyani AD, Singhal M (2011) Distributed computing: principles, algorithms, and systems, 2nd edn. Cambridge University Press, New YorkLadin R, Liskov B, Shrira L, Ghemawat S (1992) Providing high availability using lazy replication. ACM Trans Comput Syst 10(4):360–391Lamport L (1978) Time, clocks, and the ordering of events in a distributed system. Commun ACM 21(7):558–565Laumay P, Bruneton E, de Palma N, Krakowiak S (2001) Preserving causality in a scalable message-oriented middleware. In: Intnl Conf on Distrib Syst Platf (Middleware), pp 311–328Liu N, Liu M, Cao J, Chen G, Lou W (2010) When transportation meets communication: V2P over VANETs. In: 30th Intnl Conf Distrib Comput Syst (ICDCS), GenovaLwin CH, Mohanty H, Ghosh RK (2004) Causal ordering in event notification service systems for mobile users. In: Intnl Conf Inform Tech: Coding Comput (ITCC), Las Vegas, pp 735–740Mahajan P, Alvisi L, Dahlin M (2011) Consistency, availability and covergence. Tech. rep., UTCS TR-11-22, The University of Texas at AustinMatos M, Sousa A, Pereira J, Oliveira R, Deliot E, Murray P (2009) CLON: overlay networks and gossip protocols for cloud environments. In: 11th Intnl Symp on Dist Obj, Middleware and Appl (DOA), Vilamoura, LNCS, vol 5870, pp 549–566Mattern F (1989) Virtual time and global states of distributed systems. In: Parallel and distributed algorithms, North-Holland, pp 215–226Mattern F, Fünfrocken S (1994) A non-blocking lightweight implementation of causal order message delivery. Lect Notes Comput Sci 938:197–213Meldal S, Sankar S, Vera J (1991) Exploiting locality in maintaining potential causality. In: 10th ACM Symp on Princ of Distrib Comp (PODC), Montreal, pp 231–239Meling H, Montresor A, Helvik BE, Babaoglu Ö (2008) Jgroup/ARM: a distributed object group platform with autonomous replication management. Softw Pract Exp 38(9):885–923Mosberger D (1993) Memory consistency models. Oper Syst Rev 27(1):18–26Mostéfaoui A, Raynal M (1993) Causal multicast in overlapping groups: towards a low cost approach. In: 4th Intnl Wshop on Future Trends of Distrib Comp Syst (FTDCS), Lisbon, pp 136–142Mostéfaoui A, Raynal M, Travers C, Patterson S, Agrawal D, El Abbadi A (2005) From static distributed systems to dynamic systems. In: 24th Symp on Rel Distrib Syst (SRDS), Orlando, pp 109–118Nishimura T, Hayashibara N, Takizawa M, Enokido T (2005) Causally ordered delivery with global clock in hierarchical group. In: ICPADS (2), Fukuoka, pp 560–564Parker DS Jr, Popek GJ, Rudisin G, Stoughton A, Walker BJ, Walton E, Chow JM, Edwards DA, Kiser S, Kline CS (1983) Detection of mutual inconsistency in distributed systems. IEEE Trans Softw Eng 9(3):240–247Pascual-Miret L (2014) Consistency models in modern distributed systems. An approach to eventual consistency. Master’s thesis, Depto. de Sistemas Informáticos y Computación, Univ. Politècnica de ValènciaPascual-Miret L, González de Mendívil JR, Bernabéu-Aubán JM, Muñoz-Escoí FD (2015) Widening CAP consistency. Tech. rep., IUMTI-SIDI-2015/003, Univ. Politècnica de València, ValenciaPeterson LL, Buchholz NC, Schlichting RD (1989) Preserving and using context information in interprocess communication. ACM Trans Comput Syst 7(3):217–246Pomares Hernández S, Fanchon J, Drira K, Diaz M (2001) Causal broadcast protocol for very large group communication systems. In: 5th Intnl Conf on Princ of Distrib Syst (OPODIS), Manzanillo, pp 175–188Prakash R, Baldoni R (2004) Causality and the spatial-temporal ordering in mobile systems. Mobile Netw Appl 9(5):507–516Prakash R, Raynal M, Singhal M (1997) An adaptive causal ordering algorithm suited to mobile computing environments. J Parallel Distrib Comput 41(2):190–204Raynal M, Schiper A, Toueg S (1991) The causal ordering abstraction and a simple way to implement it. Inf Process Lett 39(6):343–350Rodrigues L, Veríssimo P (1995a) Causal separators and topological timestamping: An approach to support causal multicast in large-scale systems. Tech. Rep. AR-05/95, Instituto de Engenharia de Sistemas e Computadores (INESC), LisbonRodrigues L, Veríssimo P (1995b) Causal separators for large-scale multicast communication. In: 15th Intnl Conf on Distrib Comput Syst (ICDCS), Vancouver, pp 83–91Schiper A, Eggli J, Sandoz A (1989) A new algorithm to implement causal ordering. In: 3rd Intnl Wshop on Distrib Alg (WDAG), Nice, pp 219–232Schiper N, Pedone F (2010) Fast, flexible and highly resilient genuine FIFO and causal multicast algorithms. In: 25th ACM Symp on Applied Comp (SAC), Sierre, pp 418–422Shapiro M, Preguiça NM, Baquero C, Zawirski M (2011) Convergent and commutative replicated data types. Bull EATCS 104:67–88Shen M, Kshemkalyani AD, Hsu TY (2015) Causal consistency for geo-replicated cloud storage under partial replication. In: Intnl Paral Distrib Proces Symp (IPDPS) Wshop, Hyderabad, pp 509–518Singhal M, Kshemkalyani AD (1992) An efficient implementation of vector clocks. Inf Process Lett 43(1):47–52Sotomayor B, Montero RS, Llorente IM, Foster IT (2009) Virtual infrastructure management in private and hybrid clouds. IEEE Internet Comput 13(5):14–22Stephenson P (1991) Fast ordered multicasts. PhD thesis, Dept. of Comp. Sc., Cornell Univ., IthacaStonebraker M (1986) The case for shared nothing. IEEE Database Eng Bull 9(1):4–9Vogels W (2009) Eventually consistent. Commun ACM 52(1):40–44Wischhof L, Ebner A, Rohling H (2005) Information dissemination in self-organizing intervehicle networks. IEEE Trans Intell Transp 6(1):90–101Yavatkar R (1992) MCP: a protocol for coordination and temporal synchronization in multimedia collaborative applications. In: 12th Intnl Conf on Distrib Comput Syst (ICDCS), Yokohama, pp 606–613Yen LH, Huang TL, Hwang SY (1997) A protocol for causally ordered message delivery in mobile computing systems. Mobile Netw Appl 2(4):365–372Zawirski M, Preguiça N, Duarte S, Bieniusa A, Balegas V, Shapiro M (2015) Write fast, read in the past: causal consistency for client-side applications. In: 16th Intnl Middleware Conf, VancouverZhou S, Cai W, Turner SJ, Lee BS, Wei J (2007) Critical causal order of events in distributed virtual environments. ACM Trans Mult Comp Commun Appl 3(3):1

Supervision de contenus multimédia : adaptation de contenu, politiques optimales de préchargement et coordination causale de flux

La qualité des systèmes d'informations distribués dépend de la pertinence du contenu mis à disposition, de la réactivité du service ainsi que de la cohérence des informations présentées. Nos travaux visent à améliorer ces trois critères de performance et passent par la prise en compte des caractéristiques de l'utilisateur, des ressources disponibles ou plus généralement du contexte d'exécution. Par conséquent, cette thèse comporte trois volets. Le premier volet se place dans le cadre de l'adaptation de systèmes d’information déployés dans des contextes dynamiques et stochastiques. Nous présentons une approche où des agents d’adaptation appliquent des politiques de décision séquentielle dans l'incertain. Nous modélisons ces agents par des Processus Décisionnels de Markov (PDM) selon que le contexte soit observable ou seulement partiellement observable (PDM Partiellement Observables). Dans le cas d’un service mobile de consultation de films, nous montrons en particulier qu’une politique d'adaptation de ce service à des ressources limitées peut être nuancée selon l'intérêt de l'utilisateur, estimé grâce à l’évaluation des signaux de retour implicite. Dans le deuxième volet, nous nous intéressons à l'optimisation de la réactivité d'un système qui propose des contenus hypermédia. Nous nous appuyons sur des techniques de préchargement pour réduire les latences. Comme précédemment, un PDM modélise les habitudes des utilisateurs et les ressources disponibles. La force de ce modèle réside dans sa capacité à fournir des politiques optimales de préchargement. Les premières politiques que nous obtenons sont simples. Nous enrichissons alors le modèle pour dériver des politiques de préchargement plus complexes et plus agressives et montrons leurs performances par simulation. Afin de personnaliser nos stratégies optimales nous proposons finalement un modèle PDMPO dont les politiques s'adaptent aux profils des utilisateurs. Le troisième volet se place dans le contexte des applications multimédia interactives distribuées et concerne le contrôle de la cohérence des flux multimédia répartis. Dans un tel contexte, plusieurs mécanismes de synchronisation sont nécessaires et plusieurs ordres logiques (fifo, causal, total) s'avèrent utiles. Nous proposons une boîte à outils capable de gérer plusieurs protocoles d’ordre partiel et d'assurer une délivrance correcte de chaque message, en respectant tous les ordres qui lui ont été imposés. Nous décrivons ensuite l’intégration des tolérances humaines vis-à-vis des courtes incohérences causales dans notre boîte à outils. Nos simulations montrent que de meilleures performances sont obtenues par cette méthode comparativement à d’autres approches, comme la causalité classique ou la Δ-causalité. ABSTRACT : Distributed systems information quality depends on service responsiveness, data consistency and its relevance according to user interests. The thesis aims to improve these three performance criteria by taking into account user characteristics, available ressources or more generally execution context. Naturally, the document is organized in three main parts. The first part discusses adaptation policies for information systems that are subject to dynamic and stochastic contexts. In our approach adaptation agents apply sequential decisional policies under uncertainty. We focus on the modeling of such decisional processes depending on whether the context is fully or partially observable. We use Markov Decision Processes (MDP) and Partially Observable MDP (POMDP) for modeling a movie browsing service in a mobile environment. Our model derives adaptation policies for this service that take into account the limited (and observable) resources. These policies are further refined according to the (partially observable) users’ interest level estimated from implicit feedback. Our theoretical models are validated through numerous simulations. The second part deals with hypermedia content delivery aiming to reduce navigation latencies by means of prefetching. As previously, we build upon an MDP model able to derive optimal prefetching policies integrating both user behaviour and ressource availability. First, we extend this model and propose more complex and aggressive policies. Second, the extended model is enriched by taking into account user's profile and therefore provides finer prefetching policies. It is worth noting that this model issues personnalized policies without explicily manipulating user profiles. The proposed extensions and the associated policies are validated through comparison with the original model and some heuristic approches. Finally, the third part considers multimedia applications in distributed contexts. In these contexts, highly interactive collaborative applications need to offer each user a consistent view of the interactions represented by the streams exchanged between dispersed groups of users. At the coordination level, strong ordering protocols for capturing and delivering streams' interactions (e.g. CAUSAL, TOTAL order) may be too expensive due to the variability of network conditions. We build upon previous work on expressing streams causality and propose a flexible coordination middleware for integrating different delivery modes (e.g. FIFO, CAUSAL, TOTAL) into a single channel (with respect to each of these protocols). Moreover, the proposed abstract channel can handle the mix of any partial or total order protocols. Integrating perceptual tolerance in our middleware, provides us with a coordination toolkit that performs better than Δ-causality, usually considered the best solutio

The hierarchical daisy architecture for causal delivery

In this paper, we propose the hierarchical daisy architecture, which provides causal delivery of messages sent to any subset of processes. The architecture provides fault tolerance and maintains the amount of control information within a reasonable size. It divides processes into logical groups. Messages inside a logical group are sent directly, while messages that need to cross logical groups' boundaries are forwarded by servers. We prove the correctness of the daisy architecture, discuss possible optimizations, and present simulation results. © 1999 The British Computer Society

The hierarchical daisy architecture for causal delivery

In this paper, we propose the hierarchical daisy architecture, which provides causal delivery of messages sent to any subset of processes. The architecture provides fault tolerance and maintains the amount of control information within a reasonable size. It divides processes into logical groups. Messages inside a logical group are sent directly, while messages that need to cross logical groups ' bounderies are forwarded by servers. We proof the correctness of the daisy architecture and discuss possible optimizations