Real-time agreement and fulfilment of SLAs in Cloud Computing environments

A Cloud Computing system must readjust its resources by taking into account the demand for its services. This raises the need for designing protocols that provide the individual components of the Cloud architecture with the ability to self-adapt and to reach agreements in order to deal with changes in the services demand. Furthermore, if the Cloud provider has signed a Service Level Agreement (SLA) with the clients of the services that it offers, the appropriate agreement mechanism has to ensure the provision of the service contracted within a specified time. This paper introduces real-time mechanisms for the agreement and fulfilment of SLAs in Cloud Computing environments. On the one hand, it presents a negotiation protocol inspired by the standard WSAgreement used in web services to manage the interactions between the client and the Cloud provider to agree the terms of the SLA of a service. On the other hand, it proposes the application of a real-time argumentation framework for redistributing resources and ensuring the fulfilment of these SLAs during peaks in the service demand.This work is supported by the Spanish government Grants CONSOLIDER-INGENIO 2010 CSD2007-00022, TIN2011-27652-C03-01, TIN2012-36586-C03-01 and TIN2012-36586-C03-03.De La Prieta, F.; Heras Barberá, SM.; Palanca Cámara, J.; Rodríguez, S.; Bajo, J.; Julian Inglada, VJ. (2014). Real-time agreement and fulfilment of SLAs in Cloud Computing environments. AI Communications. 1-24. doi:10.3233/AIC-140626S124[1]V. Aleven and K.D. Ashley, Teaching case-based argumentation through a model and examples, empirical evaluation of an intelligent learning environment, in: Artificial Intelligence in Education, AIED-97, Frontiers in Artificial Intelligence and Applications, Vol. 39, IOS Press, 1997, pp. 87–94.[2]M. Alhamad, W. Perth, T. Dillon and E. Chang, Conceptual SLA framework for cloud computing, in: 4th IEEE International Conference on Digital Ecosystems and Technologies (DEST), IEEE Press, 2010, pp. 606–610.Armbrust, M., Stoica, I., Zaharia, M., Fox, A., Griffith, R., Joseph, A. D., … Rabkin, A. (2010). A view of cloud computing. Communications of the ACM, 53(4), 50. doi:10.1145/1721654.1721672Ashley, K. D. (1991). Reasoning with cases and hypotheticals in HYPO. International Journal of Man-Machine Studies, 34(6), 753-796. doi:10.1016/0020-7373(91)90011-u[6]P. Barham, B. Dragovic, K. Fraser, S. Hand, T. Harris, A. Ho, R. Neugebauer, I. Pratt and A. Warfield, Xen and the art of virtualization, in: 9th ACM Symposium on Operating Systems Principles (SOSP-03), ACM Press, 2003, pp. 164–177.Beloglazov, A., Abawajy, J., & Buyya, R. (2012). Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing. Future Generation Computer Systems, 28(5), 755-768. doi:10.1016/j.future.2011.04.017[8]A. Beloglazov and R. Buyya, Energy efficient allocation of virtual machines in cloud data centers, in: 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing, IEEE Computer Society, 2010, pp. 577–578.[9]A. Beloglazov and R. Buyya, Energy efficient resource management in virtualized cloud data centers, in: 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing, IEEE Computer Society, 2010, pp. 826–831.Bench-Capon, T., & Sartor, G. (2003). A model of legal reasoning with cases incorporating theories and values. Artificial Intelligence, 150(1-2), 97-143. doi:10.1016/s0004-3702(03)00108-5[11]T.J. Bench-Capon, Specification and implementation of Toulmin dialogue game, in: International Conferences on Legal Knowledge and Information Systems, JURIX-98, Frontiers of Artificial Intelligence and Applications, IOS Press, 1998, pp. 5–20.[12]R. Buyya, R. Ranjan and R.N. Calheiros, Intercloud: Utility-oriented federation of cloud computing environments for scaling of application services, in: 10th International Conference on Algorithms and Architectures for Parallel Processing – Volume Part I, ICA3PP’10, Springer-Verlag, 2010, pp. 13–31.[13]R. Buyya, C.S. Yeo and S. Venugopal, Market-oriented cloud computing: Vision, hype, and reality for delivering it services as computing utilities, in: High Performance Computing and Communications, 2008. HPCC’08. 10th IEEE International Conference, September 2008, IEEE, 2008, pp. 5–13.Chen, C., Li, S. S., Chen, B., & Wen, D. (2011). Agent Recommendation for Agent-Based Urban-Transportation Systems. IEEE Intelligent Systems, 26(6), 77-81. doi:10.1109/mis.2011.94[15]Y.Y. Cheng, M. Low, S. Zhou, W. Cai and C.S. Choo, Evolving agent-based simulations in the clouds, in: 3rd International Workshop on Advanced Computational Intelligence (IWACI), 2010, pp. 244–249.[16]F. Dignum and H. Weigand, Communication and Deontic Logic, in: Information Systems – Correctness and Reusability. Selected Papers from the IS-CORE Workshop, R. Wieringa and R. Feenstra, eds, World Scientific Publishing Co., 1995, pp. 242–260.Erdogmus, H. (2009). Cloud Computing: Does Nirvana Hide behind the Nebula? IEEE Software, 26(2), 4-6. doi:10.1109/ms.2009.31[19]J.O. Fitó, I. Goiri and J. Guitart, SLA-driven elastic cloud hosting provider, in: 18th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP), IEEE Computer Society, 2010, pp. 111–118.Fuentes-Fernández, R., Hassan, S., Pavón, J., Galán, J. M., & López-Paredes, A. (2012). Metamodels for role-driven agent-based modelling. Computational and Mathematical Organization Theory, 18(1), 91-112. doi:10.1007/s10588-012-9110-5Heras, S., Botti, V., & Julián, V. (2009). Challenges for a CBR framework for argumentation in open MAS. The Knowledge Engineering Review, 24(4), 327-352. doi:10.1017/s0269888909990178Heras, S., Jordán, J., Botti, V., & Julián, V. (2013). Argue to agree: A case-based argumentation approach. International Journal of Approximate Reasoning, 54(1), 82-108. doi:10.1016/j.ijar.2012.06.005[24]M. Jensen, J. Schwenk, N. Gruschka and L. Iacono, On technical security issues in cloud computing, in: IEEE International Conference on Cloud Computing, IEEE Press, 2009, pp. 109–116.Kakas, A., Maudet, N., & Moraitis, P. (2005). Modular Representation of Agent Interaction Rules through Argumentation. Autonomous Agents and Multi-Agent Systems, 11(2), 189-206. doi:10.1007/s10458-005-2176-4[26]M.J. Kim, H.G. Yoon and H.K. Lee, MAV: An intelligent Multi-agent model based on Cloud computing for resource virtualization, in: Computers, Networks, Systems, and Industrial Engineering, Studies in Computational Intelligence, Vol. 365, Springer, 2011, pp. 99–111.Kraus, S., Sycara, K., & Evenchik, A. (1998). Reaching agreements through argumentation: a logical model and implementation. Artificial Intelligence, 104(1-2), 1-69. doi:10.1016/s0004-3702(98)00078-2[28]W.-Y. Lin, G.-Y. Lin and H.-Y. Wei, Dynamic auction mechanism for cloud resource allocation, in: 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing, CCGRID’10, IEEE Computer Society, Washington, DC, USA, 2010, pp. 591–592.[29]S. Liu, G. Quan and S. Ren, On-line scheduling of real-time services for cloud computing, in: 6th World Congress on Services, SERVICES’10, IEEE Computer Society, 2010, pp. 459–464.Navarro, M., Heras, S., Botti, V., & Julián, V. (2013). Towards real-time agreements. Expert Systems with Applications, 40(10), 3906-3917. doi:10.1016/j.eswa.2012.12.087Ontañón, S., & Plaza, E. (2011). An argumentation framework for learning, information exchange, and joint-deliberation in multi-agent systems1. Multiagent and Grid Systems, 7(2-3), 95-108. doi:10.3233/mgs-2011-0169Palanca, J., Navarro, M., García-Fornes, A., & Julian, V. (2013). Deadline prediction scheduling based on benefits. Future Generation Computer Systems, 29(1), 61-73. doi:10.1016/j.future.2012.05.007[33]C. Pautasso, O. Zimmermann and F. Leymann, Restful web services vs. “big”’ web services: making the right architectural decision, in: Proceedings of the 17th International Conference on World Wide Web, WWW’08, ACM, New York, NY, USA, 2008, pp. 805–814.[34]J. Peng, X. Zhang, Z. Lei, B. Zhang, W. Zhang and Q. Li, Comparison of several cloud computing platforms, in: 2nd International Symposium on Information Science and Engineering, ISISE’09, IEEE Computer Society, 2009, pp. 23–27.Prakken, H., & Sartor, G. (1998). Artificial Intelligence and Law, 6(2/4), 231-287. doi:10.1023/a:1008278309945[36]I. Rahwan and G. Simari, eds, Argumentation in Artificial Intelligence, Springer, 2009.Ross, J. W., & Westerman, G. (2004). Preparing for utility computing: The role of IT architecture and relationship management. IBM Systems Journal, 43(1), 5-19. doi:10.1147/sj.431.0005Schaffer, H. E. (2009). X as a Service, Cloud Computing, and the Need for Good Judgment. IT Professional, 11(5), 4-5. doi:10.1109/mitp.2009.112[39]K.M. Sim, Agent-based cloud commerce, in: IEEE International Conference on Industrial Engineering and Engineering Management, IEEE Press, 2009, pp. 717–721.Soh, L.-K., & Tsatsoulis, C. (2005). A Real-Time Negotiation Model and A Multi-Agent Sensor Network Implementation. Autonomous Agents and Multi-Agent Systems, 11(3), 215-271. doi:10.1007/s10458-005-0539-5Talia, D. (2012). Clouds Meet Agents: Toward Intelligent Cloud Services. IEEE Internet Computing, 16(2), 78-81. doi:10.1109/mic.2012.28Tolchinsky, P., Modgil, S., Atkinson, K., McBurney, P., & Cortés, U. (2011). Deliberation dialogues for reasoning about safety critical actions. Autonomous Agents and Multi-Agent Systems, 25(2), 209-259. doi:10.1007/s10458-011-9174-5[44]A. Toniolo, T. Norman and K. Sycara, An empirical study of argumentation schemes in deliberative dialogue, in: 20th European Conference on Artificial Intelligence, ECAI-12, Frontiers in Artificial Intelligence and Applications, Vol. 242, IOS Press, 2012, pp. 756–761.[45]W.-T. Tsai, Q. Shao, X. Sun and J. Elston, Real-time service-oriented cloud computing, in: IEEE 6th World Congress on Services, SERVICES’10, IEEE Press, 2010, pp. 473–478.[46]D. Walton, C. Reed and F. Macagno, Argumentation Schemes, Cambridge University Press, 2008.[47]L. Wang, J. Tao, M. Kunze, A. Castellanos, D. Kramer and W. Karl, Scientific cloud computing: Early definition and experience, in: 10th IEEE International Conference on High Performance Computing and Communications (HPCC-08), IEEE Press, 2008, pp. 825–830.[48]Y.O. Yazir, C. Matthews, R. Farahbod, S. Neville, A. Guitouni, S. Ganti and Y. Coady, Dynamic resource allocation in computing clouds using distributed multiple criteria decision analysis, in: IEEE 3rd International Conference on Cloud Computing (CLOUD), IEEE Computer Society, 2010, pp. 91–98.[49]Y. Yu, S. Ren, N. Chen and X. Wang, Profit and penalty aware (pp-aware) scheduling for tasks with variable task execution time, in: ACM Symposium on Applied Computing, SAC’10, ACM, 2010, pp. 334–339

MorphoSys: efficient colocation of QoS-constrained workloads in the cloud

In hosting environments such as IaaS clouds, desirable application performance is usually guaranteed through the use of Service Level Agreements (SLAs), which specify minimal fractions of resource capacities that must be allocated for unencumbered use for proper operation. Arbitrary colocation of applications with different SLAs on a single host may result in inefficient utilization of the host’s resources. In this paper, we propose that periodic resource allocation and consumption models -- often used to characterize real-time workloads -- be used for a more granular expression of SLAs. Our proposed SLA model has the salient feature that it exposes flexibilities that enable the infrastructure provider to safely transform SLAs from one form to another for the purpose of achieving more efficient colocation. Towards that goal, we present MORPHOSYS: a framework for a service that allows the manipulation of SLAs to enable efficient colocation of arbitrary workloads in a dynamic setting. We present results from extensive trace-driven simulations of colocated Video-on-Demand servers in a cloud setting. These results show that potentially-significant reduction in wasted resources (by as much as 60%) are possible using MORPHOSYS.National Science Foundation (0720604, 0735974, 0820138, 0952145, 1012798

InterCloud: Utility-Oriented Federation of Cloud Computing Environments for Scaling of Application Services

Cloud computing providers have setup several data centers at different geographical locations over the Internet in order to optimally serve needs of their customers around the world. However, existing systems do not support mechanisms and policies for dynamically coordinating load distribution among different Cloud-based data centers in order to determine optimal location for hosting application services to achieve reasonable QoS levels. Further, the Cloud computing providers are unable to predict geographic distribution of users consuming their services, hence the load coordination must happen automatically, and distribution of services must change in response to changes in the load. To counter this problem, we advocate creation of federated Cloud computing environment (InterCloud) that facilitates just-in-time, opportunistic, and scalable provisioning of application services, consistently achieving QoS targets under variable workload, resource and network conditions. The overall goal is to create a computing environment that supports dynamic expansion or contraction of capabilities (VMs, services, storage, and database) for handling sudden variations in service demands. This paper presents vision, challenges, and architectural elements of InterCloud for utility-oriented federation of Cloud computing environments. The proposed InterCloud environment supports scaling of applications across multiple vendor clouds. We have validated our approach by conducting a set of rigorous performance evaluation study using the CloudSim toolkit. The results demonstrate that federated Cloud computing model has immense potential as it offers significant performance gains as regards to response time and cost saving under dynamic workload scenarios.Comment: 20 pages, 4 figures, 3 tables, conference pape

A Bag-of-Tasks Scheduler Tolerant to Temporal Failures in Clouds

Cloud platforms have emerged as a prominent environment to execute high performance computing (HPC) applications providing on-demand resources as well as scalability. They usually offer different classes of Virtual Machines (VMs) which ensure different guarantees in terms of availability and volatility, provisioning the same resource through multiple pricing models. For instance, in Amazon EC2 cloud, the user pays per hour for on-demand VMs while spot VMs are unused instances available for lower price. Despite the monetary advantages, a spot VM can be terminated, stopped, or hibernated by EC2 at any moment. Using both hibernation-prone spot VMs (for cost sake) and on-demand VMs, we propose in this paper a static scheduling for HPC applications which are composed by independent tasks (bag-of-task) with deadline constraints. However, if a spot VM hibernates and it does not resume within a time which guarantees the application's deadline, a temporal failure takes place. Our scheduling, thus, aims at minimizing monetary costs of bag-of-tasks applications in EC2 cloud, respecting its deadline and avoiding temporal failures. To this end, our algorithm statically creates two scheduling maps: (i) the first one contains, for each task, its starting time and on which VM (i.e., an available spot or on-demand VM with the current lowest price) the task should execute; (ii) the second one contains, for each task allocated on a VM spot in the first map, its starting time and on which on-demand VM it should be executed to meet the application deadline in order to avoid temporal failures. The latter will be used whenever the hibernation period of a spot VM exceeds a time limit. Performance results from simulation with task execution traces, configuration of Amazon EC2 VM classes, and VMs market history confirms the effectiveness of our scheduling and that it tolerates temporal failures