Advertisement

Efficient resource allocation scheme for on-the-fly computing based mobile grids

  • Amit Sadanand SavyanavarEmail author
  • Vijay Ram Ghorpade
Original Research
  • 2 Downloads

Abstract

Mobile grid (MG) is emergi ng as a new computing paradigm due to the ubiquitous availability of mobile devices. With the advancement in the capability of these devices, computationally intensive tasks can be executed using a peer-to-peer grid of such devices. MG can provide an edifice to execute parallel computationally intensive tasks. Key challenges that crop up while computing on a MG are resource constrained environment, inefficient resource allocation, high failure probability, etc. As a result, selection of appropriate nodes for task execution becomes critical for successful execution of the application. In this paper, we propose an efficient resource allocation model (ERAM) which provides resource allocation with failure handling. We created a MG comprising of Wi-Fi Direct connected Android smartphones. Different scenarios are considered for the purpose of experimentation. Our approach performs well with respect to application completion time, % battery consumption and recovery time from failure in comparison with existing techniques.

Keywords

Checkpointing Mobile grid computing Peer-to-peer computing Resource allocation Rough set theory 

Abbreviations

MG

Mobile grid

ERAM

Efficient Resource Allocation Model

DRA

Distance based resource allocation scheme

NLRA

Next-location based resource allocation scheme

AMT

Application Metadata Template

References

  1. 1.
    Samad T, Bay JS, Godbole D (2007) Network-centric systems for military operations in urban terrain: the role of UAVs. Proc IEEE 95(1):92–107CrossRefGoogle Scholar
  2. 2.
    Falcou J, Serot J, Chateau T, Jurie F (2005) A parallel implementation of 3D reconstruction algorithm for real-time vision. Parallel Comput 33:663–670Google Scholar
  3. 3.
    Se S, Lowe DG, Little JJ (2005) Vision-based global localization and mapping for mobile robots. IEEE Trans Robot 21(3):364–375CrossRefGoogle Scholar
  4. 4.
    Viswanathan H, Chen B, Pompili D (2012) Research challenges in computation, communication, and context awareness for ubiquitous healthcare. IEEE Commun 50(5):92–99CrossRefGoogle Scholar
  5. 5.
    Olabarriaga SD, Glatard T, de Boer PT (2010) A virtual laboratory for medical image analysis. IEEE Trans Inf Technol Biomed 14(4):979–985CrossRefGoogle Scholar
  6. 6.
    Roy Nirmalya, Das Sajal K (2009) Enhancing availability of grid computational services to ubiquitous computing applications. IEEE Trans Parallel Distrib Syst 20(7):953–967CrossRefGoogle Scholar
  7. 7.
    Hanna P, Ross P, Blickman JG, Kamman R (2007) Pervasive access to images and data—the use of computing grids and mobile/wireless devices across healthcare enterprises. IEEE Trans Inf Technol Biomed 11(1):81–86CrossRefGoogle Scholar
  8. 8.
    Kim JK, Siegel HJ, Maciejewski AA, Fellow RE (2008) Dynamic resource management in energy constrained heterogeneous computing systems using voltage scaling. IEEE Trans Parallel Distrib Syst 19(11):1445–1457CrossRefGoogle Scholar
  9. 9.
    Hu L, Che XL, Zheng SQ (2012) Online system for Grid resource monitoring and machine learning-based prediction. IEEE Trans Parallel Distrib Syst 23(1):134–145CrossRefGoogle Scholar
  10. 10.
    McClatchey R, Anjum A, Stockinger H, Ali A, Willers I, Thomas M (2007) Data intensive and network aware (DIANA) grid scheduling. Springer J Grid Comput 5(1):43–64CrossRefGoogle Scholar
  11. 11.
    Villela D (2010) Minimizing the average completion time for concurrent grid applications. Springer J Grid Comput 8(1):47–59CrossRefGoogle Scholar
  12. 12.
    Zhang J, Hamalainen A, Porras J (2008) Addressing mobility issues in mobile environment. In: Proceedings 1st workshop on mobile middleware: embracing the personal communication device, Article No. 3, 2008Google Scholar
  13. 13.
    Fox G, Ho A, Wang R, Chu E, Kwan I (2008) A collaborative sensorGrids framework. IEEE international symposium on collaborative technologies & systems, pp 29–38Google Scholar
  14. 14.
    Tadeu A, Gomes A (2007) DICHOTOMY: a resource discovery and scheduling protocol for multihop adhoc mobile Grids. Seventh IEEE international symposium on cluster computing and the grid (CCGrid’07), pp 719–724Google Scholar
  15. 15.
    Preetam G, Nirmalya R, Das SK (2007) Mobility-aware efficient job scheduling in mobile grids. Seventh IEEE international symposium on cluster computing and the grid (CCGrid’07), pp 701–706Google Scholar
  16. 16.
    Shilve S, Siegel HJ, Maciejewski AA, Sugavanam P, Banka T, Castain R, Chindam K, Dussinger S, Pichumani P, Satyasekaran P, Saylor W, Sendek D, Sousa J, Sridharan J, Velazco J (2006) Static allocation of resources to communicating subtasks in a heterogeneous adhoc Grid environment. Elsevier J Parallel Distrib Comput 66(4):600–611CrossRefGoogle Scholar
  17. 17.
    Balasubramanian N, Balasubramanian A, Venkataramani A (2009) Energy consumption in mobile phones: a measurement study and implications for network applications. ACM Internet Measurement Conference, pp 280–293Google Scholar
  18. 18.
    Shah SC, Chauhdary SH, Bashir AK, Park MS (2010) A centralized location-based job scheduling algorithm for interdependent jobs in mobile adhoc computational Grids. Springer J Appl Sci 10(3):174–181Google Scholar
  19. 19.
    Shah K, Di Francesco M, Kumar M (2012) Distributed resource management in wireless sensor networks using reinforcement learning. Springer Wirel Netw 19:496–515Google Scholar
  20. 20.
    Jaggi PK, Singh AK (2015) Rollback recovery with low overhead for fault tolerance in mobile adhoc networks. Elsevier J King Saud Univ Comput Inform Sci 27:402–415Google Scholar
  21. 21.
    Darby PJ III, Tzeng NF (2010) Decentralized QoS aware checkpointing arrangements in mobile grid computing. IEEE Trans Mobile Comput 9(8):1173–1186CrossRefGoogle Scholar
  22. 22.
    Kaur P, Parwekar P (2014) Fuzzy rule based checkpointing arrangement for fault tolerance in mobile grids. Seventh International Conference on Contemporary Computing.  https://doi.org/10.1109/ic3.2014.6897188
  23. 23.
    Dongarra J, Herault T, Robert Y (2013) Revisiting the double checkpointing algorithm. In: IEEE 27th International symposium on parallel and distributed processing workshops and PhD forum,  https://doi.org/10.1109/ipdpsw2013.11
  24. 24.
    Gil-Herrera E, Aden-Buie G, Yalcin A, Tsalatsanis A, Barnes LE, Djulbegovic B (2015) Rough set theory based prognostic classification models for hospice referral. BMC Med Inf Decis Making 15:98.  https://doi.org/10.1186/s12911-015-0216-9 CrossRefGoogle Scholar
  25. 25.
    Nahato KB, Harichandran KN, Arputharaj K (2015) Knowledge mining from clinical datasets using rough sets and backpropagation neural network. Hindawi Publishing Corporation, Computational and mathematical methods in medicine, Article ID 460189Google Scholar
  26. 26.
    Savyanavar A, Ghorpade VR (2015) Node mobility prediction in mobile grid. Int J Emerg Trends Technol 2(2):1–6Google Scholar
  27. 27.
    Li L, Li T (2014) An empirical study of ontology-based multi-document summarization in disaster management. IEEE Trans Syst Man Cybern Syst 44(2):162–171CrossRefGoogle Scholar

Copyright information

© Bharati Vidyapeeth's Institute of Computer Applications and Management 2018

Authors and Affiliations

  • Amit Sadanand Savyanavar
    • 1
    • 2
    Email author
  • Vijay Ram Ghorpade
    • 3
  1. 1.Department of Computer Science and EngineeringShivaji UniversityKolhapurIndia
  2. 2.Department of Computer EngineeringMIT College of EngineeringPuneIndia
  3. 3.Department of Computer Science and EngineeringBharati Vidyapeeth’s College of EngineeringKolhapurIndia

Personalised recommendations