Cirrus: A Disruption-Tolerant Cloud

  • Eleftheria Katsiri
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7277)


Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, virtual machines, applications, and services) that can be rapidly and elastically provisioned, to quickly scale out, and rapidly released to quickly scale in. However, commercially available cloud services such as public grids target the needs for the broader customer base and do not meet the specialized requirements of real-time, data-centric applications, such as sensor data aggregation, messaging, media streaming and commodity exchange, that need to process very large volumes of diverse, streaming data in near real time. To make matters worse, end-to-end communication paths between real-time data providers and consumers are no longer guaranteed, due to either node unavailability or service unavailability. The DTN paradigm has shown to promote interoperable and reliable communications in the presence of disruptions, however, is not directly applicable to cloud computing. A new cloud computing model is therefore needed for the above scenarios.

This paper proposes a novel concept, that of a generalized cloud, Cirrus, defined as a computing cloud with the following characteristics: (i) abiding by the NIST Cloud Definition, (ii) providing specialized, core Cloud services targeted to real-time, data centric applications, (iii) allowing for the elastic use of Cirrus cloud resources by ad-hoc networks and (iv) allowing for the elastic incorporation of nomadic and/or severely resource constrained devices, in Cirrus. Cirrus is built on top of DTN application-layer extensions, such as the Bundle Protocol (BP). As a result, Cirrus behaves as an ”overlay Cloud”, elastically forming, expanding and shrinking over networks of dynamic topology that may contain both fixed and ad-hoc infrastructure, thus providing a more fair and de-centralized Cloud Computing solution that is not exclusive to ”big players” in the field.


Sensor Network Cloud Computing Virtual Machine Cloud Service Mobile Cloud 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    IRODS:Data Grids, Digital Libraries, Persistent Archives, and Real-time Data Systems,,_Digital_Libraries,_Persistent_Archives,_and_Real-time_Data_Systems
  2. 2.
    Mobile Cloud Computing: Devices, trends, issues, and the enabling technologies,
  3. 3.
    The NIST Definition of Cloud Computing. National Institute of Standards and Technology 53(6), 50 (2009)Google Scholar
  4. 4.
  5. 5.
    Amazon Elastic Compute Cloud,
  6. 6.
    Booth, D., Haas, H., McCabe, F., Newcomer, E., Champion, M., Ferris, C., Orchard, D.: Web Services Architecture. Technical report, W3C (2004)Google Scholar
  7. 7.
    Eugster, P.T., Felber, P.A., Guerraoui, R., Kermarrec, A.M.: The Many Faces of Publish/Subscribe. ACM Computing Surveys 35(2), 114–131 (2003)CrossRefGoogle Scholar
  8. 8.
  9. 9.
    Delay Tolerant Networking Research Group,
  10. 10.
  11. 11.
    NASA Nebula in Action: Cloud Computing Case Examples,
  12. 12.
    Nichols, K., Holbrook, M., Pitts, R.L., Gifford, K., Jenkins, A., Kumzinsky, S.: Dtn implementation and utilization options on the international space station. In: SpaceOps 2010 Conference ”Delivering on the dream”, Huntsville, Alabama, Springer (April 2010)Google Scholar
  13. 13.
    Leguay, J., Lopez-Ramos, M., Jean-Marie, K., Conan, V.: An efficient service oriented architecture for heterogeneous and dynamic wireless sensor networks. In: 33rd IEEE Conference on Local Computer Networks, LCN 2008, pp. 740–747 (October 2008)Google Scholar
  14. 14.
    The Internet of Things. Executive Summary. Itu internet reports (2005),
  15. 15.
    MapReduce: Simplified Data Processing on Large Clusters,
  16. 16.
  17. 17.
  18. 18.
    Warner, S.A., Karman, A.F.: Defining the mobile cloud. NASA IT Summit 2010 (August 2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Eleftheria Katsiri
    • 1
    • 2
  1. 1.Department of Electrical Engineering and Computer EngineeringDemocritus University of ThraceXanthiGreece
  2. 2.Institute for the Management of Information SystemsResearch and Innovation Centre in Information, Communication and Knowledge Technologies, “Athena”MaroussiGreece

Personalised recommendations