Optimised auto-scaling for cloud-based web service

University of Technology Sydney. Faculty of Engineering and Information Technology.Elasticity and cost-effectiveness are two key features for ensuring that cloud-based web services appeal to more businesses. However, true elasticity and cost-effectiveness in the pay-per-use cloud business model has not yet been fully achieved. The explosion of cloud-based web services brings new challenges to enable the automatic scaling up and down of service provision when the workload is time-varying. This research studies the problems associated with these challenges. It proposes a novel scheme to achieve optimised auto-scaling for cloud-based web services from three levels of cloud structure: Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). At the various levels, auto-scaling for cloud-based web services has different problems and requires different solutions. At the SaaS level, this study investigates how to design and develop scalable web services, especially for time-consuming applications. To achieve the greatest efficiency, the optimisation of service provision problem is studied by providing the minimum functionality and fastest scalability performance concerning the speed-up curve and QoS (Quality of Service) of the SLA (Service-Level Agreement). At the PaaS level, this work studies how to support dynamic re-configuration when workloads change and the effective deployment of various kinds of web services to the cloud. To achieve optimised auto-scaling of this deployment, a platform is designed to deploy all web services automatically with the minimal number of cloud resources by satisfying the QoS of SLAs. At the IaaS level for two infrastructure resources of virtual machine (VM) and virtual network (VN), this research focuses on studying two types of cloud-based web service: computation-intensive and bandwidth-intensive. To address the optimised auto-scaling problem for computation-intensive cloud-based web service, data-driven VM auto-scaling approaches are proposed to handle the workload in both stable and dynamic environments. To address the optimised auto-scaling problem for bandwidth-intensive cloud-based web service, this study proposes a novel approach to predict the volume of requests and dynamically adjust the software defined network (SDN)-based network configuration in the cloud to auto-scale the service with minimal cost. This research proposes comprehensive and profound perspectives to solve the auto-scaling optimisation problems for cloud-based web services. The proposed approaches not only enable cloud-based web services to minimise resource consumption while auto-scaling service provision to achieve satisfying performance, but also save energy consumption for the global realisation of green computing. The performance of the proposed approaches has been evaluated on a public platform (e.g. Amazon EC2) with the real dataset workload of web services. The experiment results demonstrate that the proposed approaches are practicable and achieve superior performance to other benchmark methods

Proneo: Large File Transfer using Webworkers and Cloud Services

Cloud computing is a term, which involves virtualization, distributed computing, networking, and web services. Efficient data transfer among the cloud server and client. Cloud storage enables users to remotely store and retrieve their data. In previous work, the data are stored in the cloud using dynamic data operation with computation which makes the user need to make a copy for further updating and verification of the data loss. The objective of our project is to propose the partitioning method & web workers for the data storage which avoids the local copy at the user side. The cryptography technologies offer encryption and decryption of the data and user authentication information to protect it from the unauthorized user or attacker. MD5 based file encryption system for exchanging information or data is included in this model. This ensures secure authentication system and hiding information from others. The Cloud server allows user to store their data on a cloud without worrying about correctness & integrity of data. DOI: 10.17762/ijritcc2321-8169.15052

Dynamic Congestion Control in Network Layer for Advanced Cloud Computing

Cloud computing becoming attractive tool for delivering web-based services. It can enable rapid development and dynamic scaling and it offers flexible powerful but low cost distribution infrastructure. In paper we proposed new infrastructure capabilities to support dynamic networks. In the network layer Allocation of resource at specific locations and those sites are connects by backbone supporting provisional virtual links. Each location constructs one data center for processing of resource specified by function. Application controller updates the distribution information and multicast to access nodes for load balancing of flow of packets and regulating the traffic flow within application cluster to avoid congestion. The processing elements create the virtual output queues to adjust to prevent output congestion

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

Modelling Cloud Computing Infrastructure

Abstract. We present a modelling approach for an adaptive cloud infrastructure consisting of secure pools of virtualized resources, in order to facilitate automated management tasks and interaction with the system by a human administrator, or programmatically by a higher level service. The topology of such a system is rapidly changing as, for example, it has the abilities to create, modify or destroy pools of virtual resources according to customer demand, as well as dynamically modify the mapping of virtual to physical resources. It is also highly distributed and management data needs to be compiled from disparate sources. Our modelling approach, based on the semantic web, allows us to represent complex topologies, model incomplete or erroneous systems and perform operations such as query, while still allowing validation of the models against system invariants and policies. It also supports distributed modelling, allowing sub-models to be combined, data merging, and shared vocabularies. Introduction There is currently a shift towards cloud computing, which changes the model of provision and consumption of information technology (IT) services, and separates the IT consumer or customer organisation from much of the direct cost and management of IT provision. Rather than an organization managing IT services on their own computing infrastructure, cloud computing takes the approach of meeting an organisation's IT needs, partly or wholly, by IT services available on the internet. The infrastructure to support cloud computing needs to be highly adaptive and distributed. The topology is rapidly changing, as the platform on which applications run will have the abilities to create and destroy pools of virtualized resources, to manage dynamic resource allocation across the population of resources, and to detect and recover from failures. Information about the infrastructure needs to be assimilated from, and integrated into, a variety of management sources. Throughout all this change, topological constraints and management policies will need to be applied. Our aim is to model a platform for cloud computing, to enable interaction programmatically by other management systems and higher level services, as well as by human administrators. This paper discusses the requirements of modelling a cloud computing infrastructure (Section 2) and presents a solution based on semantic web [1] technologies (Section 3). Sections 4 and 5 discuss related work and conclusions

Novel optimization schemes for service composition in the cloud using learning automata-based matrix factorization

A thesis submitted to the University of Bedfordshire, in partial fulfilment of the requirements for the degree of Doctor of PhilosophyService Oriented Computing (SOC) provides a framework for the realization of loosely couple service oriented applications (SOA). Web services are central to the concept of SOC. They possess several benefits which are useful to SOA e.g. encapsulation, loose coupling and reusability. Using web services, an application can embed its functionalities within the business process of other applications. This is made possible through web service composition. Web services are composed to provide more complex functions for a service consumer in the form of a value added composite service. Currently, research into how web services can be composed to yield QoS (Quality of Service) optimal composite service has gathered significant attention. However, the number and services has risen thereby increasing the number of possible service combinations and also amplifying the impact of network on composite service performance. QoS-based service composition in the cloud addresses two important sub-problems; Prediction of network performance between web service nodes in the cloud, and QoS-based web service composition. We model the former problem as a prediction problem while the later problem is modelled as an NP-Hard optimization problem due to its complex, constrained and multi-objective nature. This thesis contributed to the prediction problem by presenting a novel learning automata-based non-negative matrix factorization algorithm (LANMF) for estimating end-to-end network latency of a composition in the cloud. LANMF encodes each web service node as an automaton which allows v it to estimate its network coordinate in such a way that prediction error is minimized. Experiments indicate that LANMF is more accurate than current approaches. The thesis also contributed to the QoS-based service composition problem by proposing four evolutionary algorithms; a network-aware genetic algorithm (INSGA), a K-mean based genetic algorithm (KNSGA), a multi-population particle swarm optimization algorithm (NMPSO), and a non-dominated sort fruit fly algorithm (NFOA). The algorithms adopt different evolutionary strategies coupled with LANMF method to search for low latency and QoSoptimal solutions. They also employ a unique constraint handling method used to penalize solutions that violate user specified QoS constraints. Experiments demonstrate the efficiency and scalability of the algorithms in a large scale environment. Also the algorithms outperform other evolutionary algorithms in terms of optimality and calability. In addition, the thesis contributed to QoS-based web service composition in a dynamic environment. This is motivated by the ineffectiveness of the four proposed algorithms in a dynamically hanging QoS environment such as a real world scenario. Hence, we propose a new cellular automata-based genetic algorithm (CellGA) to address the issue. Experimental results show the effectiveness of CellGA in solving QoS-based service composition in dynamic QoS environment

A platform to deploy customized scientific virtual infrastructures on the cloud

This paper presents a software platform to dynamically deploy complex scientific virtual computing infrastructures, on top of Infrastructure as a Service (IaaS) Clouds. The platform orchestrates different services to provision the virtual computing resources. It dynamically installs the appropriate software to satisfy the requirements of a researcher, both on public and on-premise Clouds. The platform provides a web interface to enable the users to easily management of the lifecycle of virtual infrastructures. It enables users to define infrastructures, share them with other users, deploy and relinquish them, add or remove resources dynamically, create and share application recipes, etc. The paper also describes three case studies to deploy complex infrastructures, namely a Hadoop cluster, a single-node to perform NGS sequencing and a gateway for users to access the European Grid Infrastructure (EGI). This platform promotes a better use of on-premise hardware resources of a research center by allocating the computing resources just-in-time to the specific life time of the virtual infrastructures as well as the deployment of the very same infrastructures on a public Cloud.The authors would to thank the Spanish "Ministerio de Economia y Competitividad" for the project "Clusters Virtuales Elasticos y Migrables sobre Infraestructuras Cloud Hibridas" with reference TIN2013-44390-R.Caballer Fernández, M.; Segrelles Quilis, JD.; Moltó, G.; Blanquer Espert, I. (2015). A platform to deploy customized scientific virtual infrastructures on the cloud. Concurrency and Computation: Practice and Experience. 27(16):4318-4329. https://doi.org/10.1002/cpe.3518S431843292716Mell P Grance T The NIST definition of Cloud computing. NIST Special Publication 800-145 (Final) Technical Report 2011 http://csrc.nist.gov/publications/nistpubs/800-145/SP800-145.pdfBuyya, R., Broberg, J., & Goscinski, A. (Eds.). (2011). Cloud Computing. doi:10.1002/9780470940105Sahoo J Mohapatra S Lath R Virtualization: a survey on concepts, taxonomy and associated security issues 2010 Second International Conference on Computer and Network Technology Bangkok, Thailand 2010 222 226OpenStack OpenStack 2013 http://openstack.orgNurmi D Wolski R Grzegorczyk C Obertelli G Soman S Youseff L Zagorodnov D The Eucalyptus open-source Cloud-computing system Proceedings of 9th IEEE International Symposium on Cluster Computing and the Grid Shanghai, China 2009 124 131Amazon Web Services AWS CloudFormation http://aws.amazon.com/cloudformation/Amazon Web Services AWS OpsWorks http://aws.amazon.com/opsworks/Keahey K Freeman T Contextualization: providing one-click virtual clusters Fourth IEEE International Conference on eScience Indianapolis, Indiana, USA 2008 301 308Keahey K Freeman T Architecting a large-scale elastic environment: recontextualization and adaptive Cloud services for scientific computing 2012Marshall P Keahey K Freeman T Elastic site: using Clouds to elastically extend site resources Proceedings of the 2010 IEEE/ACM 10th International Conference on Cluster, Cloud and Grid Computing CCGRID '10 IEEE Computer Society, Washington, DC, USA 2010 43 52Bresnahan J Freeman T LaBissoniere D Keahey K Managing appliance launches in infrastructure Clouds Proceedings of the 2011 TeraGrid Conference: Extreme Digital Discovery TG '11 ACM, New York, NY, USA 2011 12:1 12:7Apache Whirr 2013 from:http://whirr.apache.org/Juve G Deelman E Automating application deployment in infrastructure clouds Proceedings of the 2011 IEEE Third International Conference on Cloud Computing Technology and Science CLOUDCOM '11 IEEE Computer Society, Washington, DC, USA 2011 658 665OASIS Topology and orchestration specification for cloud applications version 1.0 2013 http://docs.oasis-open.org/tosca/TOSCA/v1.0/TOSCA-v1.0.htmlBinz T Breitenbcher U Haupt F Kopp O Leymann F Nowak A Wagner S OpenTOSCA - a runtime for TOSCA-based cloud applications ICSOC, Lecture Notes in Computer Science 8274 Springer 2013 692 695Puppet Labs IT automation software for system administrators 2013 http://www.puppetlabs.com/Opscode Chef 2013 http://www.opscode.com/chef/DeHaan M Ansible 2013 http://ansible.cc/Vogels, W. (2008). Beyond server consolidation. Queue, 6(1), 20. doi:10.1145/1348583.1348590Carrión JV Moltó G De Alfonso C Caballer M Hernández V A generic catalog and repository service for virtual machine images 2nd International ICST Conference on Cloud Computing (CloudComp 2010) Barcelona, Spain 2010 1 15de Alfonso C Caballer M Alvarruiz F Molto G Hernández V Infrastructure deployment over the Cloud 2011 IEEE Third International Conference on Cloud Computing Technology and Science Athens, Greece 2011 517 521Caballer, M., Blanquer, I., Moltó, G., & de Alfonso, C. (2014). Dynamic Management of Virtual Infrastructures. Journal of Grid Computing, 13(1), 53-70. doi:10.1007/s10723-014-9296-5Dean, J., & Ghemawat, S. (2008). MapReduce. Communications of the ACM, 51(1), 107. doi:10.1145/1327452.1327492Shvachko K Kuang H Radia S Chansler R The Hadoop distributed file system 2010 IEEE 26th Symposium on Mass Storage Systems and Technologies (MSST) Incline Village, NV, USA 2010 1 10Altschul, S. F., Gish, W., Miller, W., Myers, E. W., & Lipman, D. J. (1990). Basic local alignment search tool. Journal of Molecular Biology, 215(3), 403-410. doi:10.1016/s0022-2836(05)80360-

A cluster-based decentralized job dispatching for the large-scale cloud.

The remarkable development of cloud computing in the past few years, and its proven ability to handle web hosting workloads, is prompting researchers to investigate whether clouds are suitable to run large-scale computations. Cloud load balancing is one of the solution to provide reliable and scalable cloud services. Especially, load balancing for the multimedia streaming requires dynamic and real-time load balancing strategies. With this context, this paper aims to propose an Inter Cloud Manager (ICM) job dispatching algorithm for the large-scale cloud environment. ICM mainly performs two tasks: clustering (neighboring) and decision-making. For clustering, ICM uses Hello packets that observe and collect data from its neighbor nodes, and decision-making is based on both the measured execution time and network delay in forwarding the jobs and receiving the result of the execution. We then run experiments on a large-scale laboratory test-bed to evaluate the performance of ICM, and compare it with well-known decentralized algorithms such as Ant Colony, Workload and Client Aware Policy (WCAP), and the Honey-Bee Foraging Algorithm (HFA). Measurements focus in particular on the observed total average response time including network delay in congested environments. The experimental results show that for most cases, ICM is better at avoiding system saturation under the heavy load.N/