Advertisement

Soft Computing

, Volume 23, Issue 1, pp 323–341 | Cite as

Cross-layer collaboration handoff mechanism based on multi-attribute decision in mobile computation offloading

  • Ji-rui Li
  • Xiao-yong Li
  • Rui Zhang
Methodologies and Application
  • 117 Downloads

Abstract

In mobile cloud computing, employing computation offloading enables mobile devices to considerably augment their capability in emerging resource-hungry applications. However, studies on realistic offloading handoff mechanisms are still lacking. In the current work, a cross-layer collaboration handoff mechanism based on improved multi-attribute decision (CCHMD) is proposed to make reasonable, effective and efficient handoff decisions by considering the frequent movement of intelligent terminals and the heterogeneity of wireless networks. Cross-layer collaboration refers to the cooperation between communication handoff and computation handoff. The former mainly depends on received signal strength of mobile terminals, the minimum equality parameter and the minimum improvement parameter of all network attributes. By contrast, the latter hinges on several important attributes of candidate networks. To objectively evaluate the performance of each candidate network, we first apply the improved normalization and information entropy method (EM) to automatically calculate the weight value of each attribute, and employ the improved multi-attribute decision algorithm to assess all candidate networks. We then arrange these networks in descending order and select the first network as the optimal handoff network. Experimental results have proved that CCHMD exhibits better adaptability and performance than EM, simple additive weight and technique for order preference by similarity to ideal solution in terms of several indicators such as task execution time, handoff frequency, energy consumption and task execution efficiency.

Keywords

Mobile cloud computing Computation offloading The handoff Cross-layer collaboration Multi-attribute decision Information entropy 

Notes

Acknowledgements

The authors would like to appreciate the editors and the anonymous reviewers for their insightful suggestions to improve the quality of this paper. The work is supported by the National Key Research and Development Program of China (No. 2016QY03D0605), the National Nature Science Foundation of China (Nos. 61370069, 61672111) and Beijing Natural Science Foundation (No. 4162043).

Compliance with ethical standards

Conflict of interest

Xiaoyong Li declares that he has no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

References

  1. Barbera MV et al (2013) To offload or not to offload? The bandwidth and energy costs of mobile cloud computing. In: INFOCOM, 2013 Proceedings IEEEGoogle Scholar
  2. Bari F, Leung V (2007) Application of ELECTRE to network selection in a heterogeneous wireless network environment. In: 2007 IEEE wireless communications and networking conference. IEEEGoogle Scholar
  3. Bircher E, Braun T (2004) An agent-based architecture for service discovery and negotiation in wireless networks. In: International conference on wired/wireless internet communicationsGoogle Scholar
  4. Bonino D, De Russis L, Corno F, Ferrero G (2014) JEERP: energy-aware enterprise resource planning. IT Prof 4:50–56CrossRefGoogle Scholar
  5. Çalhan A, Çeken C (2012) An optimum vertical handoff decision algorithm based on adaptive fuzzy logic and genetic algorithm. Wirel Pers Commun 64(4):647–664CrossRefGoogle Scholar
  6. Chen L-J et al (2004) A smart decision model for vertical handoff. In: ANWIRE international workshopGoogle Scholar
  7. Chen CT, Su WC, Chen CL (2015) Video sharing with seamless service handoff in mobile device-centric cloud computing environment. In: 2015 IEEE international conference on consumer electronics-Taiwan (ICCE-TW). IEEE, pp 362–363Google Scholar
  8. Cohen J (2008) Embedded speech recognition applications in mobile phones: status, trends, and challenges. In: 2008 IEEE international conference on acoustics, speech and signal processing. IEEE, pp 5352–5355Google Scholar
  9. Gallardo-Medina, JR, Pineda-Rico U, Stevens-Navarro E (2009) VIKOR method for vertical handoff decision in beyond 3G wireless networks. In: 2009 6th International conference on electrical engineering, computing science and automatic control, IEEEGoogle Scholar
  10. Garg V, Vijay K, Wilkes JE (1998) Principles and applications of GSM. Prentice Hall PTR, Englewood CliffsGoogle Scholar
  11. Hong CP, Kang TH, Kim SD (2005) An effective vertical handoff scheme supporting multiple applications in ubiquitous computing environment. In: 2005 Second international conference on embedded software and systems. IEEE, pp 1–6Google Scholar
  12. Hussain S, Hamid Z, Khattak NS (2006) Mobility management challenges and issues in 4G heterogeneous networks. In: Proceedings of the first international conference on integrated internet ad hoc and sensor networksGoogle Scholar
  13. Konka J, Andonovic I, Michie C, Atkinson R (2014) Auction-based network selection in a market-based framework for trading wireless communication services. IEEE Trans Veh Technol 63(3):1365–1377CrossRefGoogle Scholar
  14. Kovachev D, Yu T, Klamma R (2012) Adaptive computation offloading from mobile devices into the cloud. In: 2012 IEEE 10th international symposium on parallel and distributed processing with applications (ISPA). IEEE, pp 784–791Google Scholar
  15. Lal D, Manjeshwar A, Herrmann F et al (2003) Measurement and characterization of link quality metrics in energy constrained wireless sensor networks. In: IEEE 2003 global telecommunications conference (GLOBECOM’03), vol 1. IEEE, pp 446–452Google Scholar
  16. Lee YL, Chuah TC, Loo J, Vinel A (2014) Recent advances in radio resource management for heterogeneous LTE/LTE-A networks. IEEE Commun Surv Tutor 16(4):2142–2180CrossRefGoogle Scholar
  17. Lei L et al (2013) Challenges on wireless heterogeneous networks for mobile cloud computing. IEEE Wirel Commun 20(3):34–44CrossRefGoogle Scholar
  18. Li X, Ma H, Zhou F, Gui X (2015) Service operator-aware trust scheme for resource matchmaking across multiple clouds. IEEE Trans Parallel Distrib Syst 26(5):1419–1429CrossRefGoogle Scholar
  19. Liao J, Qi Q, Wang J et al (2016) A dual mode self-adaption handoff for multimedia services in mobile cloud computing environment. Multimed Tools Appl 75(8):4697–4722CrossRefGoogle Scholar
  20. Lin T, Wang C, Lin PC (2008) A neural-network-based context-aware handoff algorithm for multimedia computing. ACM Trans Multimed Comput Commun Appl (TOMM) 4(3):1–17CrossRefGoogle Scholar
  21. Liu F, Shu P, Jin H, Ding L, Yu J, Niu D, Li B (2013) Gearing resource-poor mobile devices with powerful clouds: architectures, challenges, and applications. IEEE Wirel Commun 20(3):14–22CrossRefGoogle Scholar
  22. Lohi M, Weerakoon D, Aghvami AH (2013) Key issues in handover design and multi. Multiaccess Mobil Teletraffic Wirel Commun 4:199Google Scholar
  23. Maheshwari S, Ho SD (2015) Determinative segmentation resegmentation and padding in radio link control (RLC) service data units (SDU). US Patent 9,055,473, 9 Jun 2015Google Scholar
  24. Marichamy P, Chakrabarti S, Maskara SL (2003) Performance evaluation of handoff detection schemes. In: Proceedings of IEEE TENCON 2003, Taj Residency, Bangalore, pp 643–646Google Scholar
  25. McNair J, Zhu F (2004) Vertical handoffs in fourth-generation multinetwork environments. IEEE Wirel Commun 11(3):8–15CrossRefGoogle Scholar
  26. Meskar E et al (2015) Energy efficient offloading for competing users on a shared communication channel. In: 2015 IEEE international conference on communications (ICC). IEEE, pp 3192–3197Google Scholar
  27. Miettinen AP, Nurminen JK (2010) Energy efficiency of mobile clients in cloud computing. HotCloud 10:4–4Google Scholar
  28. Mohanty S, Akyildiz IF (2006) A cross-layer (layer 2 + 3) handoff management protocol for next-generation wireless systems. IEEE Trans Mob Comput 5(10):1347–1360CrossRefGoogle Scholar
  29. Mtibaa A et al (2015) Towards mobile opportunistic computing. In: 2015 IEEE 8th international conference on cloud computing. IEEE, pp 1111–1114Google Scholar
  30. Ong EH, Khan JY (2008) Dynamic access network selection with QoS parameters estimation: a step closer to ABC. In: Proceedings of IEEE VTC 2008-Spring, Marina Bay, Singapore, pp 2671–2676Google Scholar
  31. Othman M, Madani SA, Khan SU (2014) A survey of mobile cloud computing application models. IEEE Commun Surv Tutor 16(1):393–413CrossRefGoogle Scholar
  32. Qi Q, Liao J, Wang J et al (2016) Integrated multi-service handoff mechanism with QoS-support strategy in mobile cloud computing. Wirel Pers Commun 87(2):593–614CrossRefGoogle Scholar
  33. Ryu S, Lee K, Mun Y (2012) Optimized fast handover scheme in Mobile IPv6 networks to support mobile users for cloud computing. J Supercomput 59(2):658–675CrossRefGoogle Scholar
  34. Sabharwal M, Agrawal A, Metri G (2013) Enabling green it through energy-aware software. IT Prof 15(1):19–27CrossRefGoogle Scholar
  35. Saraydar CU, Mandayam NB, Goodman DJ (2002) Efficient power control via pricing in wireless data networks. IEEE Trans Commun 50(2):291–303Google Scholar
  36. Song Q, Jamalipour A (2005) Network selection in an integrated wireless LAN and UMTS environment using mathematical modeling and computing techniques. IEEE Wirel Commun 12(3):42–48Google Scholar
  37. Soyata T, Muraleedharan R, Funai C, Kwon M, Heinzelman W (2012) Cloud-vision: real-time face recognition using a mobile-cloudlet-cloud acceleration architecture. In: 2012 IEEE symposium on computers and communications (ISCC). IEEE, pp 000059–000066Google Scholar
  38. Sun Z, Yang Y, Zhou Y, Cruickshank H (2016) Agent-based resource management for mobile cloud. In: Web-based services: concepts, methodologies, tools, and applications, IGI Global, pp 290–305Google Scholar
  39. Wang C, Li Z (2004) A computation offloading scheme on handheld devices. J Parallel Distrib Comput 64(6):740–746CrossRefzbMATHGoogle Scholar
  40. Wang K, Zeng ZM, Feng CY (2009) A heterogeneous network selection algorithm based on multiple attribute decision making. Radio Eng 39(1):1–3Google Scholar
  41. Wei G (2012) Hesitant fuzzy prioritized operators and their application to multiple attribute decision making. Knowl Based Syst 31:176–182CrossRefGoogle Scholar
  42. Wen Y, Zhang W, Luo H (2012) Energy-optimal mobile application execution: taming resource-poor mobile devices with cloud clones. In: INFOCOM, 2012 Proceedings IEEE. IEEEGoogle Scholar
  43. Wu, H, Huang D, Bouzefrane S (2013) Making offloading decisions resistant to network unavailability for mobile cloud collaboration. In: 2013 9th International conference on collaborative computing: networking, applications and worksharing (Collaboratecom), IEEEGoogle Scholar
  44. Xiao M, Shroff NB, Chong EKP (2003) A utility-based power-control scheme in wireless cellular systems. IEEE/ACM Trans Netw 11(2):210–221CrossRefGoogle Scholar
  45. Yang L (2013) A framework for partitioning and execution of data stream applications in mobile cloud computing. ACM SIGMETRICS Perform Eval Rev 40(4):23–32CrossRefGoogle Scholar
  46. Yang Z, Liu X, Hu Z et al (2017) Seamless service handoff based on delaunay triangulation for mobile cloud computing. Wirel Pers Commun 93(3):795–809CrossRefGoogle Scholar
  47. Ylianttila M, Makela J, Mahonen P (2002) Supporting resource allocation with vertical handoffs in multiple radio network environment. In: Proceedings of IEEE PIMRC’02, Lisbon, Portugal, pp 64–68Google Scholar
  48. Ylitalo J et al (2003) Dynamic network interface selection in multihomed mobile hosts. In: 2003 Proceedings of the 36th annual Hawaii international conference on system sciences. IEEEGoogle Scholar
  49. Zhang W (2004) Handover decision using fuzzy MADM in heterogeneous networks. In: 2004 IEEE wireless communications and networking conference (WCNC), vol 2. IEEEGoogle Scholar
  50. Zhang N, Holtzman JM (1998) Analysis of CDMA soft-handover algorithm. IEEE Trans Veh Technol 47(2):710–714CrossRefGoogle Scholar
  51. Zhou H, Zhang G, Wang G (2007) Multi-objective decision making approach based on entropy weights for reservoir flood control operation. J Hydraul Eng 1:014Google Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.Cyberspace Safety InstituteBeijing University of Posts and TelecommunicationsBeijingPeople’s Republic of China
  2. 2.Key Laboratory of Trustworthy Distributed Computing and Service, Ministry of EducationBeijing University of Posts and TelecommunicationsBeijingPeople’s Republic of China
  3. 3.School of Computing, Informatics, and Decision Systems EngineeringArizona State UniversityTempeUSA

Personalised recommendations