Abstract
With the recent advancements in distributed systems, Cloud computing has emerged as a model for enabling convenient, on-demand network access to a shared resource pool of configurable elements such as (networks, servers, storage, applications, and services). Various applications are developed and deployed into the Cloud following the layered architecture. The layered approach includes infrastructure, virtualization, application, platform and client tiers. Provenance (the meta-data), is the information that helps cloud providers and users to determine the derivation history of a data product, starting from its origin. Each layer in the Cloud has its own provenance data and generally, provenance data for each layer address different audience. For example, Cloud providers are interested in the infrastructure provenance data to verify the high utilization of resources through audit trials. Cloud users on the other hand are interested in the performance of the deployed application and the verification of experiments. In this paper, we present various queries regarding the provenance data for different layers of Cloud. Hereby, we integrate the provenance data from individual layers and highlight the importance of integrated provenance. We also outline the relationship between various layers of the Cloud by using the integrated provenance.
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References
Deelman, E., Singh, G., Livny, M., Berriman, B., Good, J.: The cost of doing science on the cloud: The montage example (2008)
Vöckler, J.S., Juve, G., Deelman, E., Rynge, M., Berriman, B.: Experiences using cloud computing for a scientific workflow application, pp. 15–24. ACM, USA (2011)
Barga, R.S., Simmhan, Y.L., Chinthaka, E., Sahoo, S.S.: Jackson: Provenance for scientific workflows towards reproducible research. IEEE Data Eng. Bull. (2010)
Bose, R., Frew, J.: Lineage retrieval for scientific data processing: a survey. ACM Comput. Surv. 37(1), 1–28 (2005)
Simmhan, Y.L., Plale, B., Gannon, D.: A Survey of Data Provenance Techniques. Technical report, Computer Science Department, Indiana University (2005)
Armbrust, M., Fox, A., Griffith, R., Joseph, A.D., Katz, R.H., Konwinski, A., Lee: Above the Clouds: A Berkeley View of Cloud Computing (2009)
Imran, M., Hlavacs, H.: Applications of provenance data for cloud infrastructure. In: Eighth International Conference on Semantics, Knowledge and Grids (SKG), pp. 16–23 (2012)
Crawl, D., Altintas, I.: A provenance-based fault tolerance mechanism for scientific workflows. In: Freire, J., Koop, D., Moreau, L. (eds.) IPAW 2008. LNCS, vol. 5272, pp. 152–159. Springer, Heidelberg (2008)
Miles, S., Groth, P., Branco, M., Moreau, L.: The requirements of recording and using provenance in e-Science experiments. Technical report (2005)
Muniswamy-Reddy, K.K., Seltzer, M.I.: Provenance as first class cloud data. Operating Systems Review 43(4), 11–16 (2009)
Muniswamy-Reddy, K.K., Macko, P., Seltzer, M.: Provenance for the cloud. In: FAST 2010, pp. 197–210. USENIX Association (2010)
Imran, M., Hlavacs, H.: Provenance in the cloud: Why and how? In: The Third International Conference on Cloud Computing, GRIDs, and Virtualization, pp. 106–112 (2012)
Margo, D.W., Seltzer, M.I.: The case for browser provenance. In: Workshop on the Theory and Practice of Provenance (2009)
Macko, P., Chiarini, M., Seltzer, M.: Collecting provenance via the xen hypervisor. In: Workshop on the Theory and Practice of Provenance (2011)
Muniswamy-Reddy, K.K., Braun, U., Holland, D.A., Macko, P.: Maclean: Layering in provenance systems. In: USENIX, USA (2009)
Muniswamy-Reddy, K.K., Holland, D.A., Braun, U., Seltzer, M.I.: Provenance-aware storage systems. In: USENIX, pp. 43–56 (2006)
Zhang, O.Q., Kirchberg, M., Ko, R.K.L., Lee, B.S.: How to track your data: The case for cloud computing provenance. In: CloudCom 2011, pp. 446–453 (2011)
Imran, M., Hlavacs, H.: Provenance framework for the cloud environment (iaas). In: The Third International Conference on Cloud Computing, GRIDs, and Virtualization (2012)
Youseff, L., Butrico, M., Da Silva, D.: Toward a Unified Ontology of Cloud Computing. In: Grid Computing Environments Workshop, GCE 2008, pp. 1–10 (2008)
Rochwerger, B., Breitgand, D., Levy, E., Galis, A., Nagin, K.: The reservoir model and architecture for open federated cloud computing (2009)
Wei, J., Zhang, X., Ammons, G., Bala, V., Ning, P.: Managing security of virtual machine images in a cloud environment. In: CCSW, pp. 91–96. ACM (2009)
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Imran, M., Hlavacs, H. (2013). Layering of the Provenance Data for Cloud Computing. In: Park, J.J.(.H., Arabnia, H.R., Kim, C., Shi, W., Gil, JM. (eds) Grid and Pervasive Computing. GPC 2013. Lecture Notes in Computer Science, vol 7861. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38027-3_6
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DOI: https://doi.org/10.1007/978-3-642-38027-3_6
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