Abstract
Cloud Computing is an emerging paradigm in Information Technologies that enables the delivery of infrastructure, software and platform resources as services. It is an environment with automatic service provisioning and management. In these last years autonomic management of Cloud services is receiving an increasing attention. Meanwhile, optimization of autonomic managers remains not well explored. In fact, almost all the existing solutions on autonomic computing have been interested in modeling and implementing of autonomic environments without paying attention on optimization. In this paper, we propose a new efficient algorithm to optimize autonomic managers for the management of service-based applications. Our algorithm allows to determine the minimum number of autonomic managers and to assign them to services that compose managed service-based applications. The realized experiments proves that our approach is efficient and adapted to service-based applications that can be not only described as architecture-based but also as behavior-based compositions of services.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
An architectural blueprint for autonomic computing. Tech. rep., IBM (2005)
Arya, L., Verma, A.: Workflow scheduling algorithms in cloud environment - a survey. In: Engineering and Computational Sciences (RAECS) (2014)
Babu, K.D., Kumar, D.G., Veluru, S.: Optimal allocation of virtual resources using genetic algorithm in cloud environments. In: Proceedings of the 12th ACM International Conference on Computing Frontiers (2015)
Booch, G., Rumbaugh, J., Jacobson, I.: Unified Modeling Language User Guide. Addison-Wesley Professional (2005)
Buyya, R., Calheiros, R.N., Li, X.: Autonomic cloud computing: open challenges and architectural elements. CoRR (2012)
Chaisiri, S., Lee, B.S., Niyato, D.: Optimization of resource provisioning cost in cloud computing. IEEE Transactions on Services Computing (2012)
Fahland, D., Favre, C., Koehler, J., Lohmann, N., Völzer, H., Wolf, K.: Analysis on demand: Instantaneous soundness checking of industrial business process models. Data Knowl. Eng. (2011)
Hoenisch, P., Schuller, D., Schulte, S., Hochreiner, C., Dustdar, S.: Optimization of complex elastic processes. IEEE Transactions on Services Computing (2015)
Huang, K.C., Shen, B.J.: Service deployment strategies for efficient execution of composite saas applications on cloud platform. Journal of Systems and Software (2015)
Juhnke, E., Dornemann, T., Bock, D., Freisleben, B.: Multi-objective scheduling of BPEL workflows in geographically distributed clouds. In: IEEE International Conference on Cloud Computing (CLOUD) (2011)
Juric, M.B.: Business Process Execution Language for Web Services BPEL and BPEL4WS, 2nd edn. Packt Publishing (2006)
Marino, J., Rowley, M.: Understanding SCA (Service Component Architecture). Addison-Wesley Professional (2009)
Mell, P.M., Grance, T.: The NIST definition of cloud computing. Tech. rep. (2011)
Mohamed, M., Amziani, M., Belaïd, D., Tata, S., Melliti, T.: An autonomic approach to manage elasticity of business processes in the Cloud. Future Generation Computer Systems (2014)
de Oliveira, F., Ledoux, T., Sharrock, R.: A framework for the coordination of multiple autonomic managers in cloud environments. In: IEEE 7th International Conference on Self-Adaptive and Self-Organizing Systems (SASO) (2013)
(OMG), O.M.G.: Business process model and notation (BPMN). Tech. rep. (2011)
Pandey, S., Wu, L., Guru, S., Buyya, R.: A particle swarm optimization-based heuristic for scheduling workflow applications in cloud computing environments. In: 24th IEEE International Conference on Advanced Information Networking and Applications (2010)
Redbooks, I., Organization, I.B.M.C.I.T.S.: A Practical Guide to the IBM Autonomic Computing Toolkit. IBM Corporation, International Technical Support Organization (2004)
Ruz, C., Baude, F., Sauvan, B.: Flexible adaptation loop for component-based SOA applications. In: 7th International Conference on Autonomic and Autonomous Systems ICAS (2011)
The Apache Software Foundation: Getting started with Tuscany. http://tuscany.apache.org/getting-started-with-tuscany.html
Tomás, L., Caminero, A.C., Rana, O., Carrión, C., Caminero, B.: A gridway-based autonomic network-aware metascheduler. Future Gener. Comput. Syst. (2012)
Wu, Z., Ni, Z., Gu, L., Liu, X.: A revised discrete particle swarm optimization for cloud workflow scheduling. In: International Conference on Computational Intelligence and Security (CIS) (2010)
Yangui, S., Tata, S.: The spd approach to deploy service-based applications in the cloud. Concurrency and Computation: Practice and Experience (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Hadded, L., Charrada, F.B., Tata, S. (2015). An Efficient Optimization Algorithm of Autonomic Managers in Service-Based Applications. In: Debruyne, C., et al. On the Move to Meaningful Internet Systems: OTM 2015 Conferences. OTM 2015. Lecture Notes in Computer Science(), vol 9415. Springer, Cham. https://doi.org/10.1007/978-3-319-26148-5_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-26148-5_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-26147-8
Online ISBN: 978-3-319-26148-5
eBook Packages: Computer ScienceComputer Science (R0)