Capturing and Scaling Up Concurrent Transactions in Uncertain Databases

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-42017-7_6[EN] This chapter provides a framework for capturing and scaling up concurrent transactions in uncertain databases. Models and methods proposed in the context of this framework for managing data uncertainty are innovative as previous studies have not considered the specific case of concurrent transactions, which may worsen the uncertainty of database management activities beyond the simplest case of isolated transactions. Indeed, as this chapter demonstrates, inconsistency tolerance of integrity management, constraint checking and repairing easily scale up to concurrent transactions in a natural way, and query answers in concurrent transactions over uncertain data remain certain in the presence of uncertainty. This analytical contribution is enriched by means of a reference architecture for uncertain database management under concurrent transactions that strictly adheres to models and methods that are the main contributions of this research.The second and the third author have been supported by FEDER and the Spanish grants TIN2009-14460-C03, TIN2010-17139.Cuzzocrea, A.; Decker, H.; Muñoz-Escoí, FD. (2013). Capturing and Scaling Up Concurrent Transactions in Uncertain Databases. Communications in Computer and Information Science. 246:70-85. https://doi.org/10.1007/978-3-642-42017-7_6S708524

A survey on elasticity management in PaaS systems

[EN] Elasticity is a goal of cloud computing. An elastic system should manage in an autonomic way its resources, being adaptive to dynamic workloads, allocating additional resources when workload is increased and deallocating resources when workload decreases. PaaS providers should manage resources of customer applications with the aim of converting those applications into elastic services. This survey identifies the requirements that such management imposes on a PaaS provider: autonomy, scalability, adaptivity, SLA awareness, composability and upgradeability. This document delves into the variety of mechanisms that have been proposed to deal with all those requirements. Although there are multiple approaches to address those concerns, providers main goal is maximisation of profits. This compels providers to look for balancing two opposed goals: maximising quality of service and minimising costs. Because of this, there are still several aspects that deserve additional research for finding optimal adaptability strategies. Those open issues are also discussed.This work has been partially supported by EU FEDER and Spanish MINECO under research Grant TIN2012-37719-C03-01.Muñoz-Escoí, FD.; Bernabeu Aubán, JM. (2017). A survey on elasticity management in PaaS systems. Computing. 99(7):617-656. https://doi.org/10.1007/s00607-016-0507-8S617656997Ajmani S (2004) Automatic software upgrades for distributed systems. PhD thesis, Department of Electrical and Computer Science, Massachusetts Institute of Technology, USAAjmani S, Liskov B, Shrira L (2006) Modular software upgrades for distributed systems. In: 20th European Conference on Object-Oriented Programming (ECOOP), Nantes, France, pp 452–476Alhamad M, Dillon TS, Chang E (2010) Conceptual SLA framework for cloud computing. 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On synchrony in dynamic distributed systems

[EN] Many modern distributed services are deployed in dynamic systems. Cloud services are an example. They are expected to provide service to a potentially huge amount of users and may require a wide geographical deployment in multiple data centres. Their service processes vary in volume in accordance with workload variations, showing an adaptive behaviour in order to minimise economical costs. Dynamic distributed systems may be classifed considering two axes: (a) the number of processes that compose the system, and (b) the diameter of the networking graph that interconnects those processes. Other important features of dynamic systems can be derived from these two characteristics, e.g., their attainable synchrony. We analyse the level of synchrony that may be achieved in each dynamic system class and revise the existing techniques for transforming an initially asynchronous large dynamic system into another one with a higher synchrony level. With this, a larger set of problems may be handled in dynamic distributed systems. This facilitates the implementation and provision of additional services in those systems.Muñoz-Escoí, FD.; Juan Marín, RD. (2018). On synchrony in dynamic distributed systems. Open Computer Science. 8(1):154-164. https://doi.org/10.1515/comp-2018-0014S1541648

Scalable Uncertainty-tolerant Business Rules

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-07617-1_16Business rules are of key importance for maintaining the correctness of business processes and the reliability of business data. When they take the form of integrity constraints, business rules also can help to contain the amount of uncertainty associated to business data and decisions based on those data. However, business rule enforcement may not scale up easily to systems with concurrent transactions. To a large extent, the problem is due to two common exigencies: the postulates of total and of isolated business rule satisfaction. In order to limit the accumulation of business rule violations, and thus of uncertainty, we are going to outline how a measure-based uncertainty-tolerant approach to business rules maintenance scales up to concurrent transactions. 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Improving the benefits of multicast prioritization algorithms

The final publication is available at Springer via http://dx.doi.org/10.1007/s11227-014-1087-zPrioritized atomic multicast consists in delivering messages in total order while ensuring that the priorities of the messages are considered; i.e., messages with higher priorities are delivered first. That service can be used in multiple applications. An example is the usage of prioritization algorithms for reducing the transaction abort rates in applications that use a replicated database system. To this end, transaction messages get priorities according to their probability of violating the existing integrity constraints. This paper evaluates how that abort reduction may be improved varying the message sending rate and the bounds set on the length of the priority reordering queue being used by those multicast algorithms.This work has been partially supported by EU FEDER and Spanish MICINN under research Grants TIN2009-14460-C03-01 and TIN2010-17193.Miedes De Elías, EP.; Muñoz Escoí, FD. (2014). Improving the benefits of multicast prioritization algorithms. Journal of Supercomputing. 68(3):1280-1301. doi:10.1007/s11227-014-1087-zS12801301683Amir Y, Danilov C, Stanton JR (2000) A low latency, loss tolerant architecture and protocol for wide area group communication. In: International Conference on Dependable Systems and Networks (DSN), IEEE-CS, Washington, DC, USA, pp 327–336Chockler G, Keidar I, Vitenberg R (2001) Group communication specifications: a comprehensive study. ACM Comput Surv 33(4):427–469CiA (2001) About CAN in Automation (CiA). http://www.can-cia.org/index.php?id=aboutciaDéfago X, Schiper A, Urbán P (2004) Total order broadcast and multicast algorithms: taxonomy and survey. ACM Comput Surv 36(4):372–421Dolev D, Dwork C, Stockmeyer L (1987) On the minimal synchronism needed for distributed consensus. J ACM 34(1):77–97International Organization for Standardization (ISO) (1993) Road vehicles—interchange of digital information—controller area network (CAN) for high-speed communication. Revised by ISO 11898-1:2003JBoss (2011) The Netty project 3.2 user guide. http://docs.jboss.org/netty/3.2/guide/html/Kaashoek MF, Tanenbaum AS (1996) An evaluation of the Amoeba group communication system. In: International conference on distributed computing system (ICDCS), IEEE-CS, Washington, DC, USA, pp 436–448Miedes E, Muñoz-Escoí FD (2008) Managing priorities in atomic multicast protocols. In: International conference on availability, reliability and security (ARES), Barcelona, Spain, pp 514–519Miedes E, Muñoz-Escoí FD (2010) Dynamic switching of total-order broadcast protocols. In: International conference on parallel and distributed processing techniques and applications (PDPTA), CSREA Press, Las Vegas, Nevada, USA, pp 457–463Miedes E, Muñoz-Escoí FD, Decker H (2008) Reducing transaction abort rates with prioritized atomic multicast protocols. In: International European conference on parallel and distributed computing (Euro-Par), Springer, Las Palmas de Gran Canaria, Spain, Lecture notes in computer science, vol 5168, pp 394–403Mocito J, Rodrigues L (2006) Run-time switching between total order algorithms. In: International European conference on parallel and distributed computing (Euro-Par), Springer, Dresden, Germany, Lecture Notes in Computer Science, vol 4128, pp 582–591Moser LE, Melliar-Smith PM, Agarwal DA, Budhia R, Lingley-Papadopoulos C (1996) Totem: a fault-tolerant multicast group communication system. Commun ACM 39(4):54–63Nakamura A, Takizawa M (1992) Priority-based total and semi-total ordering broadcast protocols. In: International conference on distributed computing systems (ICDCS), Yokohama, Japan, pp 178–185Nakamura A, Takizawa M (1993) Starvation-prevented priority based total ordering broadcast protocol on high-speed single channel network. In: 2nd International symposium on high performance distributed computing (HPDC), pp 281–288Rodrigues L, Veríssimo P, Casimiro A (1995) Priority-based totally ordered multicast. In: Workshop on algorithms and architectures for real-time control (AARTC), Ostend, BelgiumRütti O, Wojciechowski P, Schiper A (2006) Structural and algorithmic issues of dynamic protocol update. In: 20th International parallel and distributed processing symposium (IPDPS), IEEE-CS Press, Rhodes Island, GreeceTindell K, Clark J (1994) Holistic schedulability analysis for distributed hard real-time systems. Microprocess Microprogr 40(2–3):117–134Tully A, Shrivastava SK (1990) Preventing state divergence in replicated distributed programs. In: International symposium on reliable distributed systems (SRDS), Huntsville, Alabama, USA, pp 104–113Wiesmann M, Schiper A (2005) Comparison of database replication techniques based on total order broadcast. IEEE Trans Knowl Data Eng 17(4):551–56

Eventual Consistency: Origin and Support

Eventual consistency is demanded nowadays in geo-replicated services that need to be highly scalable and available. According to the CAP constraints, when network partitions may arise, a distributed service should choose between being strongly consistent or being highly available. Since scalable services should be available, a relaxed consistency (while the network is partitioned) is the preferred choice. Eventual consistency is not a common data-centric consistency model, but only a state convergence condition to be added to a relaxed consistency model. There are still several aspects of eventual consistency that have not been analysed in depth in previous works: 1. which are the oldest replication proposals providing eventual consistency, 2. which replica consistency models provide the best basis for building eventually consistent services, 3. which mechanisms should be considered for implementing an eventually consistent service, and 4. which are the best combinations of those mechanisms for achieving different concrete goals. This paper provides some notes on these important topics

CAP Theorem: Revision of its related consistency models

[EN] The CAP theorem states that only two of these properties can be simultaneously guaranteed in a distributed service: (i) consistency, (ii) availability, and (iii) network partition tolerance. This theorem was stated and proved assuming that "consistency" refers to atomic consistency. However, multiple consistency models exist and atomic consistency is located at the strongest edge of that spectrum. Many distributed services deployed in cloud platforms should be highly available and scalable. Network partitions may arise in those deployments and should be tolerated. One way of dealing with CAP constraints consists in relaxing consistency. Therefore, it is interesting to explore the set of consistency models not supported in an available and partition-tolerant service (CAP-constrained models). Other weaker consistency models could be maintained when scalable services are deployed in partitionable systems (CAP-free models). Three contributions arise: (1) multiple other CAP-constrained models are identified, (2) a borderline between CAP-constrained and CAP-free models is set, and (3) a hierarchy of consistency models depending on their strength and convergence is built.Muñoz-Escoí, FD.; Juan Marín, RD.; García Escriva, JR.; González De Mendívil Moreno, JR.; Bernabeu Aubán, JM. (2019). CAP Theorem: Revision of its related consistency models. The Computer Journal. 62(6):943-960. https://doi.org/10.1093/comjnl/bxy142S943960626Davidson, S. B., Garcia-Molina, H., & Skeen, D. (1985). Consistency in a partitioned network: a survey. ACM Computing Surveys, 17(3), 341-370. doi:10.1145/5505.5508Gilbert, S., & Lynch, N. (2002). Brewer’s conjecture and the feasibility of consistent, available, partition-tolerant web services. ACM SIGACT News, 33(2), 51-59. doi:10.1145/564585.564601Muñoz-Escoí, F. D., & Bernabéu-Aubán, J. M. (2016). A survey on elasticity management in PaaS systems. Computing, 99(7), 617-656. doi:10.1007/s00607-016-0507-8Brewer, E. (2012). CAP twelve years later: How the «rules» have changed. Computer, 45(2), 23-29. doi:10.1109/mc.2012.37Attiya, H., Ellen, F., & Morrison, A. (2017). Limitations of Highly-Available Eventually-Consistent Data Stores. IEEE Transactions on Parallel and Distributed Systems, 28(1), 141-155. doi:10.1109/tpds.2016.2556669Viotti, P., & Vukolić, M. (2016). Consistency in Non-Transactional Distributed Storage Systems. ACM Computing Surveys, 49(1), 1-34. doi:10.1145/2926965Burckhardt, S. (2014). Principles of Eventual Consistency. Foundations and Trends® in Programming Languages, 1(1-2), 1-150. doi:10.1561/2500000011Herlihy, M. P., & Wing, J. M. (1990). Linearizability: a correctness condition for concurrent objects. ACM Transactions on Programming Languages and Systems, 12(3), 463-492. doi:10.1145/78969.78972Lamport. (1979). How to Make a Multiprocessor Computer That Correctly Executes Multiprocess Programs. IEEE Transactions on Computers, C-28(9), 690-691. doi:10.1109/tc.1979.1675439Ladin, R., Liskov, B., Shrira, L., & Ghemawat, S. (1992). Providing high availability using lazy replication. ACM Transactions on Computer Systems, 10(4), 360-391. doi:10.1145/138873.138877Yu, H., & Vahdat, A. (2002). Design and evaluation of a conit-based continuous consistency model for replicated services. ACM Transactions on Computer Systems, 20(3), 239-282. doi:10.1145/566340.566342Curino, C., Jones, E., Zhang, Y., & Madden, S. (2010). Schism. Proceedings of the VLDB Endowment, 3(1-2), 48-57. doi:10.14778/1920841.1920853Das, S., Agrawal, D., & El Abbadi, A. (2013). ElasTraS. ACM Transactions on Database Systems, 38(1), 1-45. doi:10.1145/2445583.2445588Chen, Z., Yang, S., Tan, S., He, L., Yin, H., & Zhang, G. (2014). A new fragment re-allocation strategy for NoSQL database systems. Frontiers of Computer Science, 9(1), 111-127. doi:10.1007/s11704-014-3480-4Kamal, J., Murshed, M., & Buyya, R. (2016). Workload-aware incremental repartitioning of shared-nothing distributed databases for scalable OLTP applications. Future Generation Computer Systems, 56, 421-435. doi:10.1016/j.future.2015.09.024Elghamrawy, S. M., & Hassanien, A. E. (2017). A partitioning framework for Cassandra NoSQL database using Rendezvous hashing. The Journal of Supercomputing, 73(10), 4444-4465. doi:10.1007/s11227-017-2027-5Muñoz-Escoí, F. D., García-Escrivá, J.-R., Sendra-Roig, J. S., Bernabéu-Aubán, J. M., & González de Mendívil, J. R. (2018). Eventual Consistency: Origin and Support. Computing and Informatics, 37(5), 1037-1072. doi:10.4149/cai_2018_5_1037Fischer, M. J., Lynch, N. A., & Paterson, M. S. (1985). Impossibility of distributed consensus with one faulty process. Journal of the ACM, 32(2), 374-382. doi:10.1145/3149.21412

Supporting multiple isolation levels in replicated environments

Replication is used by databases to implement reliability and provide scalability. However, achieving transparent replication is not an easy task. A replicated database is transparent if it can seamlessly replace a standard stand-alone database without requiring any changes to the components of the system. Database replication transparency can be achieved if: (a) replication protocols remain hidden for all other components of the system; and (b) the functionality of a stand-alone database is provided. The ability to simultaneously execute transactions under different isolation levels is a functionality offered by all stand-alone databases but not by their replicated counterparts. Allowing different isolation levels may improve overall system performance. For example, the TPC-C benchmark specification tolerates execution of some transactions at weaker isolation levels in order to increase throughput of committed transactions. In this paper, we show how replication protocols can be extended to enable transactions to be executed under different isolation levels. © 2012 Elsevier B.V. All rights reserved.This work has been supported by the Spanish Ministerio de Ciencia e Innovation (MICINN) and the European Regional Development Fund (ERDF/FEDER) under research grants TIN2009-14460-C03-01 and TIN2010-17193. The translation of this paper was funded by the Universitat Politecnica de Valencia, Spain.Bernabe Gisbert, JM.; Muñoz Escoí, FD. (2012). Supporting multiple isolation levels in replicated environments. Data and Knowledge Engineering. 79-80:1-16. doi:10.1016/j.datak.2012.05.001S11679-8

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. 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