226,706 research outputs found
Little Boxes: A Dynamic Optimization Approach for Enhanced Cloud Infrastructures
The increasing demand for diverse, mobile applications with various degrees
of Quality of Service requirements meets the increasing elasticity of on-demand
resource provisioning in virtualized cloud computing infrastructures. This
paper provides a dynamic optimization approach for enhanced cloud
infrastructures, based on the concept of cloudlets, which are located at
hotspot areas throughout a metropolitan area. In conjunction, we consider
classical remote data centers that are rigid with respect to QoS but provide
nearly abundant computation resources. Given fluctuating user demands, we
optimize the cloudlet placement over a finite time horizon from a cloud
infrastructure provider's perspective. By the means of a custom tailed
heuristic approach, we are able to reduce the computational effort compared to
the exact approach by at least three orders of magnitude, while maintaining a
high solution quality with a moderate cost increase of 5.8% or less
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
An Algorithm for Network and Data-aware Placement of Multi-Tier Applications in Cloud Data Centers
Today's Cloud applications are dominated by composite applications comprising
multiple computing and data components with strong communication correlations
among them. Although Cloud providers are deploying large number of computing
and storage devices to address the ever increasing demand for computing and
storage resources, network resource demands are emerging as one of the key
areas of performance bottleneck. This paper addresses network-aware placement
of virtual components (computing and data) of multi-tier applications in data
centers and formally defines the placement as an optimization problem. The
simultaneous placement of Virtual Machines and data blocks aims at reducing the
network overhead of the data center network infrastructure. A greedy heuristic
is proposed for the on-demand application components placement that localizes
network traffic in the data center interconnect. Such optimization helps
reducing communication overhead in upper layer network switches that will
eventually reduce the overall traffic volume across the data center. This, in
turn, will help reducing packet transmission delay, increasing network
performance, and minimizing the energy consumption of network components.
Experimental results demonstrate performance superiority of the proposed
algorithm over other approaches where it outperforms the state-of-the-art
network-aware application placement algorithm across all performance metrics by
reducing the average network cost up to 67% and network usage at core switches
up to 84%, as well as increasing the average number of application deployments
up to 18%.Comment: Submitted for publication consideration for the Journal of Network
and Computer Applications (JNCA). Total page: 28. Number of figures: 15
figure
Computing server power modeling in a data center: survey,taxonomy and performance evaluation
Data centers are large scale, energy-hungry infrastructure serving the
increasing computational demands as the world is becoming more connected in
smart cities. The emergence of advanced technologies such as cloud-based
services, internet of things (IoT) and big data analytics has augmented the
growth of global data centers, leading to high energy consumption. This upsurge
in energy consumption of the data centers not only incurs the issue of surging
high cost (operational and maintenance) but also has an adverse effect on the
environment. Dynamic power management in a data center environment requires the
cognizance of the correlation between the system and hardware level performance
counters and the power consumption. Power consumption modeling exhibits this
correlation and is crucial in designing energy-efficient optimization
strategies based on resource utilization. Several works in power modeling are
proposed and used in the literature. However, these power models have been
evaluated using different benchmarking applications, power measurement
techniques and error calculation formula on different machines. In this work,
we present a taxonomy and evaluation of 24 software-based power models using a
unified environment, benchmarking applications, power measurement technique and
error formula, with the aim of achieving an objective comparison. We use
different servers architectures to assess the impact of heterogeneity on the
models' comparison. The performance analysis of these models is elaborated in
the paper
Single-Sweep Methods for Free Energy Calculations
A simple, efficient, and accurate method is proposed to map multi-dimensional
free energy landscapes. The method combines the temperature-accelerated
molecular dynamics (TAMD) proposed in [Maragliano & Vanden-Eijnden, Chem. Phys.
Lett. 426, 168 (2006)] with a variational reconstruction method using
radial-basis functions for the representation of the free energy. TAMD is used
to rapidly sweep through the important regions of the free energy landscape and
compute the gradient of the free energy locally at points in these regions. The
variational method is then used to reconstruct the free energy globally from
the mean force at these points. The algorithmic aspects of the single-sweep
method are explained in detail, and the method is tested on simple examples,
compared to metadynamics, and finally used to compute the free energy of the
solvated alanine dipeptide in two and four dihedral angles
Power Modelling for Heterogeneous Cloud-Edge Data Centers
Existing power modelling research focuses not on the method used for
developing models but rather on the model itself. This paper aims to develop a
method for deploying power models on emerging processors that will be used, for
example, in cloud-edge data centers. Our research first develops a hardware
counter selection method that appropriately selects counters most correlated to
power on ARM and Intel processors. Then, we propose a two stage power model
that works across multiple architectures. The key results are: (i) the
automated hardware performance counter selection method achieves comparable
selection to the manual selection methods reported in literature, and (ii) the
two stage power model can predict dynamic power more accurately on both ARM and
Intel processors when compared to classic power models.Comment: 10 pages,10 figures,conferenc
- …