Parallel and Distributed Simulation from Many Cores to the Public Cloud (Extended Version)

In this tutorial paper, we will firstly review some basic simulation concepts and then introduce the parallel and distributed simulation techniques in view of some new challenges of today and tomorrow. More in particular, in the last years there has been a wide diffusion of many cores architectures and we can expect this trend to continue. On the other hand, the success of cloud computing is strongly promoting the everything as a service paradigm. Is parallel and distributed simulation ready for these new challenges? The current approaches present many limitations in terms of usability and adaptivity: there is a strong need for new evaluation metrics and for revising the currently implemented mechanisms. In the last part of the paper, we propose a new approach based on multi-agent systems for the simulation of complex systems. It is possible to implement advanced techniques such as the migration of simulated entities in order to build mechanisms that are both adaptive and very easy to use. Adaptive mechanisms are able to significantly reduce the communication cost in the parallel/distributed architectures, to implement load-balance techniques and to cope with execution environments that are both variable and dynamic. Finally, such mechanisms will be used to build simulations on top of unreliable cloud services.Comment: Tutorial paper published in the Proceedings of the International Conference on High Performance Computing and Simulation (HPCS 2011). Istanbul (Turkey), IEEE, July 2011. ISBN 978-1-61284-382-

Total order in opportunistic networks

Opportunistic network applications are usually assumed to work only with unordered immutable messages, like photos, videos, or music files, while applications that depend on ordered or mutable messages, like chat or shared contents editing applications, are ignored. In this paper, we examine how total ordering can be achieved in an opportunistic network. By leveraging on existing dissemination and causal order algorithms, we propose a commutative replicated data type algorithm on the basis of Logoot for achieving total order without using tombstones in opportunistic networks where message delivery is not guaranteed by the routing layer. Our algorithm is designed to use the nature of the opportunistic network to reduce the metadata size compared to the original Logoot, and even to achieve in some cases higher hit rates compared to the dissemination algorithms when no order is enforced. Finally, we present the results of the experiments for the new algorithm by using an opportunistic network emulator, mobility traces, and Wikipedia pages.Peer ReviewedPostprint (author's final draft

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

A Survey of Checkpointing Algorithms in Mobile Ad Hoc Network

Checkpoint is defined as a fault tolerant technique that is a designated place in a program at which normal processing is interrupted specifically to preserve the status information necessary to allow resumption of processing at a later time. If there is a failure, computation may be restarted from the current checkpoint instead of repeating the computation from beginning. Checkpoint based rollback recovery is one of the widely used technique used in various areas like scientific computing, database, telecommunication and critical applications in distributed and mobile ad hoc network. The mobile ad hoc network architecture is one consisting of a set of self configure mobile hosts capable of communicating with each other without the assistance of base stations. The main problems of this environment are insufficient power and limited storage capacity, so the checkpointing is major challenge in mobile ad hoc network. This paper presents the review of the algorithms, which have been reported for checkpointing approaches in mobile ad hoc network

Autonomic State Management for Optimistic Simulation Platforms

We present the design and implementation of an autonomic state manager (ASM) tailored for integration within optimistic parallel discrete event simulation (PDES) environments based on the C programming language and the executable and linkable format (ELF), and developed for execution on x8664 architectures. With ASM, the state of any logical process (LP), namely the individual (concurrent) simulation unit being part of the simulation model, is allowed to be scattered on dynamically allocated memory chunks managed via standard API (e.g., malloc/free). Also, the application programmer is not required to provide any serialization/deserialization module in order to take a checkpoint of the LP state, or to restore it in case a causality error occurs during the optimistic run, or to provide indications on which portions of the state are updated by event processing, so to allow incremental checkpointing. All these tasks are handled by ASM in a fully transparent manner via (A) runtime identification (with chunk-level granularity) of the memory map associated with the LP state, and (B) runtime tracking of the memory updates occurring within chunks belonging to the dynamic memory map. The co-existence of the incremental and non-incremental log/restore modes is achieved via dual versions of the same application code, transparently generated by ASM via compile/link time facilities. Also, the dynamic selection of the best suited log/restore mode is actuated by ASM on the basis of an innovative modeling/optimization approach which takes into account stability of each operating mode with respect to variations of the model/environmental execution parameters

Learning cognitive maps: Finding useful structure in an uncertain world

In this chapter we will describe the central mechanisms that influence how people learn about large-scale space. We will focus particularly on how these mechanisms enable people to effectively cope with both the uncertainty inherent in a constantly changing world and also with the high information content of natural environments. The major lessons are that humans get by with a less is more approach to building structure, and that they are able to quickly adapt to environmental changes thanks to a range of general purpose mechanisms. By looking at abstract principles, instead of concrete implementation details, it is shown that the study of human learning can provide valuable lessons for robotics. Finally, these issues are discussed in the context of an implementation on a mobile robot. © 2007 Springer-Verlag Berlin Heidelberg

Towards Thompson Sampling for Complex Bayesian Reasoning

Paper III, IV, and VI are not available as a part of the dissertation due to the copyright.Thompson Sampling (TS) is a state-of-art algorithm for bandit problems set in a Bayesian framework. Both the theoretical foundation and the empirical efficiency of TS is wellexplored for plain bandit problems. However, the Bayesian underpinning of TS means that TS could potentially be applied to other, more complex, problems as well, beyond the bandit problem, if suitable Bayesian structures can be found. The objective of this thesis is the development and analysis of TS-based schemes for more complex optimization problems, founded on Bayesian reasoning. We address several complex optimization problems where the previous state-of-art relies on a relatively myopic perspective on the problem. These includes stochastic searching on the line, the Goore game, the knapsack problem, travel time estimation, and equipartitioning. Instead of employing Bayesian reasoning to obtain a solution, they rely on carefully engineered rules. In all brevity, we recast each of these optimization problems in a Bayesian framework, introducing dedicated TS based solution schemes. For all of the addressed problems, the results show that besides being more effective, the TS based approaches we introduce are also capable of solving more adverse versions of the problems, such as dealing with stochastic liars.publishedVersio