Skip to main content

Advertisement

Log in

G-Route: an energy-aware service routing protocol for green cloud computing

  • Original Paper
  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

In this paper, we present the design and implementation of Green Route (G-Route), an autonomic service routing protocol for constructing energy-efficient provider paths in collaborative cloud architectures. The chief contribution of this work resides in autonomously selecting the optimal set of composite service components sustaining the most efficient energy consumption characteristics among a set of providers for executing a particular service request. For ensuring the accountability of the system, the routing decision engine is designed to operate by processing accountable energy measurements extracted securely from within the cloud data centers using trusted computing technologies and cryptographic mechanisms. By pushing green computing constraints into the service routing decision engine, we can leverage the collaborative cloud computing model to maximize the energy savings achieved. This is realized by focusing on a path of providers that execute the service requests instead of directing the green computing efforts towards a single provider site. To the best of our knowledge, G-Route is the first service routing protocol that utilizes the collaborative properties among cloud providers to select “green” service routes and thus, to enhance the energy savings in the overall cloud computing infrastructure. The devised G-Route design is developed and deployed in a real cloud computing environment using the Amazon EC2 cloud platform. The experimental results obtained analyze the protocol convergence characteristics, traffic overhead, and resilience under anomalous service configurations and conditions and demonstrate the capability of the proposed system to significantly reduce the overall energy requirements of collaborative cloud services.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Kaplan, J., Forrest, W., Kindler, N.: Revolutionizing data center energy efficiency. McKinsey & Company Tech, Report (2009)

  2. The Cloud Darkens: The New York Times. June 29, 2011. http://www.nytimes.com/2011/06/30/opinion/30thu1.html

  3. Amazon EC2 home page: http://aws.amazon.com/ec2/

  4. Daud, S., Ahmad, R.B., Murhty, N.S.: “The effects of compiler optimizations on embedded system power consumption. In: Proceedings international conference on electronic design, pp. 1–6 (2008)

  5. Tudor, D., Marcu, M.: Designing a power efficiency framework for battery powered systems. In: Proceedings of SYSTOR (2009)

  6. John, B.P., Agrawal, A., Steigerwald, B., John, E.B.: Impact of operating system behavior on battery life. J Low Power Electron 6, 10–17 (2010)

    Article  Google Scholar 

  7. Horvath, T., Abdelzaher, T., Skadron, K., Liu, X.: Dynamic voltage scaling in multi-tier web servers with end-to-end delay control. IEEE Trans Comput 56, 444–458 (2007)

    Article  MathSciNet  Google Scholar 

  8. Steigerwald, B., Chabukswar, R., Krishnan, K., Vega, J.D.:Creating energy-efficient software. Intel White Paper (2008)

  9. Liu, L., Wang, H., Liu, X., Jin, X., He, W., Wang, Q., Chen, Y.: GreenCloud: a new architecture for green data center. In: Proceedings international conference on autonomic computing and communications, New York (2009)

  10. Beloglazov, A., Buyya, R.: Optimal Online Deterministic Algorithms and Adaptive Heuristics for Energy and Performance Efficient Dynamic Consolidation of Virtual Machines in Cloud Data Centers. Concurrency and Computation: Practice and Experience (CCPE), pp. 1397–1420. Wiley, New York (2012)

    Google Scholar 

  11. Mazzucco, M., Dyachuk, D., Deters, R.: Maximizing cloud providers revenues via energy aware allocation policies. In: Proceedings of 3rd IEEE International Conference on Cloud Computing (IEEE Cloud) (2010)

  12. Jaeger, M.C., Roec-Goldmann, G., Muehl, G.: QoS aggregation for web service composition using workflow patterns. In: Proceedings of Eighth IEEE Int’l Enterprise Distributed Object Computing Conference (EDOC ’04), pp. 149–159 (2004)

  13. Menasce, D.: Composing web services: AQoS view. IEEE Internet Comput 6(8), 88–90 (2004)

    Article  Google Scholar 

  14. Zeng, L., Benatallah, A.N.B., Dumas, M., Kalagnanam, J., Chang, H.: QoS-aware middleware for web services composition. IEEE Trans. Softw. Eng. 30(5), 311–327 (2004)

    Article  Google Scholar 

  15. Zhang, W., Yang, Y., Tang, S., Fang, L.: QoS-driven service selection optimization model and algorithms for composite web services. In: Proceedings of 31st Annual International Computer Software and Applications Conference (COMPSAC ’07), 2, pp. 425–431 (2007)

  16. Srivastava, A., Sorenson, P.G.: Service selection based on customer rating of quality of service attributes. In: IEEE International Conference on Web Services (ICWS), pp. 1–8, 5–10 (2010)

  17. Tserpes, K., Aisopos, F., Kyriazis, D., Varvarigou, T.: Service selection decision support in the internet of services. Proc. GECON 2010, 16–33 (2010)

    Google Scholar 

  18. Menasce, D., Casalicchio, E., Dubey, V.: A heuristic approach to optimal service selection in service oriented architectures. In: Proceedings of WOSP’08, pp. 13–23, June 24–26 (2008)

  19. Ran, S.: A model for web services discovery with QoS. ACM SIGecom Exchanges, pp. 1–10 (2003)

  20. Gao, Z., Wu, G.: Combining Qos-based service selection with performance prediction. In: IEEE International Conference on e-Business Engineering (ICEBE), pp. 611–614 (2005)

  21. Deora, V., Shao, J., Shercliff, G., Stockreisser, P.J., Gray, W.A., Fiddian, N.J.: Incorporating QoS specifications in service discovery. In: Web Information Systems-WISE 2004 Workshops, pp. 252–263. Springer Berlin Heidelberg (2004)

  22. Li, W.J., Ping, L.D.: Trust model to enhance security and interoperability of cloud environment. In: Proceedings of the 1st International Conference on Cloud Computing, ACM, Beijing, PRC, pp. 69–79 (2009)

  23. Bernstein, D., Ludvigson, E., Sankar, K., Diamond, S., Morrow, M.: Blueprint for the intercloud–protocols and formats for cloud computing interoperability. In: ICIW’09 Fourth International Conference on Internet and Web Applications and Services, pp. 328–336 (2009)

  24. Bernstein, D.: Keynote 2: the intercloud: cloud interoperability at Internet scale. In: NPC, 2009 6th IFIP International Conference on Network and Parallel Computing, p. xiii (2009)

  25. Bernstein, D., Vij, D.: Using XMPP as a transport in Intercloud Protocols. In: 2nd International Conference on Cloud Computing, CloudComp 2010 (2010)

  26. Extensible Messaging and Presence Protocol (XMPP): core, and other related RFCs at: http://xmorg/rfcs/rfc3920.html

  27. CCIF’s unified cloud interface project. Available at: http://code.google.com/p/unifiedcloud/

  28. Parameswaran, A.V., Chaddha, A.: Cloud interoperability and standardization. SETlabs briefings 7(7), 19–26 (2009)

    Google Scholar 

  29. Itani, W., Ghali, C., Bassil, R., Kayssi, A., Chehab, A.: BGP-inspired autonomic service routing for the cloud. In: Proceedings of ACM 27th Symposium on Applied Computing, ACM SAC 2012, Trento, Italy, 26–30 March 2012

  30. Bell, M.: SOA Modeling Patterns for Service Oriented Discovery and Analysis, p. 390. Wiley, New Jerssey (2010)

    Google Scholar 

  31. Bajikar, S.: Trusted platform module (TPM)-based security on notebook PCs–White paper. Mobile Platforms Group Intel Corporation (2002)

  32. Weingart, S.: Physical security for the mABYSS system. In: Proceedings of the IEEE Computer Society Conference on Security and Privacy, pp. 52–58 (1987)

  33. Coveillo, A., Elias, H., Gelsinger, P., Mcaniff, R.: Proof, not promises: creating the trusted cloud. RSA White paper. Retrieved from: http://www.rsa.com/innovation/docs/11319_TVISION_WP_0211.pdf (2011)

  34. Chen, H., Li, Y., Shi, W.: Fine-grained power management using process-level profiling. In: Sustainable Computing: Informatics and Systems, SUSCOM (2012)

  35. Do, T., Rawshdeh, S., Shi, W.: ptop: A process-level power profiling tool. In: Proceedings of the 2nd Workshop on Power Aware Computing and Systems (HotPower’09) (2009)

  36. Jacob, B., Ng, S.W., Wang, D.T.: Memory systems : Cache, DRAM, Disk. Denise E.M. Penrose, pp. 61–67 (2007)

  37. Feeney, L.M., Nilsson, M.: Investigating the energy consumption of an wireless network interface in an ad hoc networking environment. In: Proceedings of the Twentieth Annual Joint Conference of the IEEE Computer and Communications Societies (Infocom: Anchorage. Alaska, USA, April (2001). 2001

  38. Microsoft SkyDrive Homepage: https://skydrive.live.com/

  39. Dropbox Homepage: http://www.dropbox.com

  40. Google Drive Homepage: http://drive.google.com

  41. Hamady, F. Chehab, A., Kayssi, A.: Energy consumption breakdown of a modern mobile platform under various workloads. In: International Conference on Energy Aware Computing (ICEAC), November 30–December 2, 2011, Istanbul, Turkey

  42. http://aws.typepad.com/aws/2012/04/amazon-s3-905-billion-objects-and-650000-requestssecond.html

  43. The Pacific Gas and Electric Company homepage: http://www.pge.com/

  44. Gutmann, P.: An open-source cryptographic coprocessor. In: Proceedings of the 9\(^{\rm th}\) USENIX Security Symposium, pp. 97–112 (2000)

  45. Berger, S., C’aceres, R. et al.: vTPM: virtualizing the trusted platform module. In: USENIX-SS’06: Proceedings of the 15th conference on USENIX Security Symposium

  46. Lovász, G., Niedermeier, F., de Meer, H.: Performance tradeoffs of energy-aware virtual machine consolidation. Clust. Comput. 16(3), 481–496 (2013)

    Article  Google Scholar 

  47. Itani, Wassim, Ghali, Cesar, Bassil, Ramzi, Kayssi, Ayman, Chehab, Ali: ServBGP: BGP-inspired autonomic service routing for multi-provider collaborative architectures in the cloud. Elsevier Future Gener. Comput. Syst. 32, 99–117 (2014)

    Article  Google Scholar 

  48. Bilal, K., Malik, S.U.R., Khalid, O., Hameed, A., Alvarez, E., Wijaysekara, V., Irfan, R., Shrestha, S., Dwivedy, D., Ali, M., Khan, U.S., Abbas, A., Jalil, N., Khan, S.U.: A taxonomy and survey on Green Data Center Networks. Future Gener. Comput. Syst. 36, 189–208 (2014)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Wassim Itani.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Itani, W., Ghali, C., Kayssi, A. et al. G-Route: an energy-aware service routing protocol for green cloud computing. Cluster Comput 18, 889–908 (2015). https://doi.org/10.1007/s10586-015-0443-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-015-0443-y

Keywords

Navigation