A Conceptual Framework for Adapation

We present a white-box conceptual framework for adaptation. We called it CODA, for COntrol Data Adaptation, since it is based on the notion of control data. CODA promotes a neat separation between application and adaptation logic through a clear identification of the set of data that is relevant for the latter. The framework provides an original perspective from which we survey a representative set of approaches to adaptation ranging from programming languages and paradigms, to computational models and architectural solutions

A Conceptual Framework for Adapation

This paper presents a white-box conceptual framework for adaptation that promotes a neat separation of the adaptation logic from the application logic through a clear identification of control data and their role in the adaptation logic. The framework provides an original perspective from which we survey archetypal approaches to (self-)adaptation ranging from programming languages and paradigms, to computational models, to engineering solutions

A Conceptual Framework for Adapation

This paper presents a white-box conceptual framework for adaptation that promotes a neat separation of the adaptation logic from the application logic through a clear identification of control data and their role in the adaptation logic. The framework provides an original perspective from which we survey archetypal approaches to (self-)adaptation ranging from programming languages and paradigms, to computational models, to engineering solutions

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

Management of Cloud Infastructures: Policy-Based Revenue Optimization

Competition on global markets forces many enterprises to make use of new applications, reduce process times and at the same time cut the costs of their IT-infrastructure. To achieve this, it is necessary to maintain a high degree of flexibility with respect to the IT-infrastructure. Facing this challenge the idea of Cloud computing has been gaining interest lately. Cloud services can be accessed on demand without knowledge of the underlying infrastructure and have already succeeded in helping companies deploy products faster. Using Cloud services the New York Times managed to convert scanned images containing 11 million articles into PDF within 24 hours at a cost of merely 240 US-$. However Cloud providers will only offer their services, if they can realize sufficient benefit. To achieve this, the efficiency of Cloud infrastructure management must be increased. To this end we propose the use of concepts from revenue management and further enhancements