Search WWW Search inass.org
»Journal Description
»Topics
»Call for Papers and Reviewers
»Author Guidelines
»Contents & Papers
»Call for Special Issues
»SCOPUS
 
»IEEE CIS
»INNS
»IEEE IS
DOI: http://dx.doi.org/10.22266/ijies2017.0228.15

Towards Maximum Resource Utilization and Optimal Task Execution for Gaming IoT Workflow in Mobile Cloud

Author(s):

Shakkeera L1*,Latha Tamilselvan1


Affiliations:

1B.S. Abdur Rahman University, Chennai-600048, Tamilnadu, India







Abstract:

With the rapid and tremendous growth of real-time resource discovery process ubiquitously, Internet of Things (IoT) and mobile cloud technologies have become an essential part of the future internet. The adaptive and energy-efficient mobile application execution is still in its infancy stage due to the diversity of IoT applications, manipulating workflow of an application and handling a large volume of streaming data in the heterogeneous resource-rich environment. To overcome these constraints, this paper introduces a GAming IoT workflow Fuzzifiers and Supporting MAximum Resource utilization and optimal Task execution on mobile cloud (GAF-SMART) paradigm offering an intelligent way of computing the mobile applications on the remote server. The GAF-SMART employs Game-theory and Fuzzy logic assisted adaptive task scheduler to attain optimal execution for maintaining a workflow of an application. Thus, the proposed mobile cloud IoT paradigm offers high Quality of Service (QoS), increases resource utilization and minimizes the mobile device energy.


Keywords:

Mobile cloud, IoT, Game-theory, Fuzzy logic, Adaptive task scheduler, Workflow model, Non-dominant.


Full Text:




References:
  1. N. Fernando, S.W. Loke, and W. Rahayu, “Mobile cloud computing: A survey”, Elsevier transaction on Future Generation Computer Systems, Vol.29, No.1, pp. 84-106, 2013.
  2. H.T. Dinh, C. Lee, D. Niyato, and P. Wang, “A survey of mobile cloud computing: architecture, applications, and approaches”, Wireless communications and Mobile Computing, Vol.13, No.18, pp. 1587-1611, 2013.
  3. K. Kumar, J. Liu, Y.H Lu, and B. Bhargava, “A survey of computation offloading for mobile systems”, Springer Transaction on Mobile Networks and Applications, Vol.18, No.1, pp. 129-140, 2013.
  4. D. Singh, G. Tripathi, and A.J Jara, “A survey of Internet-of-Things: Future vision, architecture, challenges and services”, IEEE world forum on Internet of things (WF-IoT), pp. 287-292, 2014.
  5. A. Botta, W. De Donato, V.Persico, and A. Pescapé, “On the integration of cloud computing and internet of things”, IEEE International Conference on Future Internet of Things and Cloud (FiCloud), pp. 23-30, 2014.
  6. T. Shon, J. Cho, K. Han, and H. Choi, “Towards advanced mobile cloud computing for the internet of things: current issues and future direction”, Springer transaction on Mobile Networks and Applications, Vol.19, No.3, pp. 404-413, 2014.
  7. L. Yang, J. Cao, Y. Yuan, T. Li, A. Han, and A. Chan, “A framework for partitioning and execution of data stream applications in mobile cloud computing”, ACM SIGMETRICS Performance Evaluation Review, Vol.40, No.4, pp. 23-32, 2013.
  8. Liu, Yanchen, and Myung J.Lee, “An effective dynamic programming offloading algorithm in Mobile cloud computing system”, IEEE Transaction on Wireless Communications and Networking Conference (WCNC), pp. 1868-1873, 2014.
  9. H. Flores, and S. Srirama, “Adaptive code offloading for mobile cloud applications: Exploiting fuzzy sets and evidence-based learning”, ACM Proceeding of the fourth workshop on Mobile cloud computing and services, pp. 9-16, 2013.
  10. Yibin Li, Min Chen, Wenyun Dai, and Meikang Qiu, “Energy optimization with dynamic task scheduling mobile cloud computing”, IEEE Transaction on System Journal, No.99, pp. 1-10, 2015.
  11. L. Shakkeera, and L. Tamilselvan, “Energy-Aware Application Scheduling and Consolidation in Mobile Cloud Computing with Load Balancing”, Springer transaction on Emerging Research in Computing, Information, Communication and Applications, pp. 253-264, 2016.
  12. M.H. Zarei, M.A. Shirsavar, and N. Yazdani, “A QoS-aware task allocation model for mobile cloud computing”, IEEE Second International Conference on Web Research (ICWR), pp. 43-47, 2016.
  13. G. Yang, Z. Yukan, Q. Qinru, and L. Yung-Hsiang, “A game theoretic resource allocation for overall energy minimization in Mobile cloud computing system”, In Proceedings of ACM/IEEE International symposium on Low power electronics and design, pp. 279-284, 2012.
  14. J. Zhanlin,I. Ganchev, M. O'Droma, L. Zhao, and X. Zhang, “A cloud-based car parking middleware for IoT-based smart cities: design and implementation”, Sensors, Vol.14, No.12, pp. 22372-22393, 2014.
  15. S.S. Yau, and A.B. Buduru, “Intelligent planning for developing mobile IoT applications using cloud systems”, IEEE International Conference on Mobile Services, pp. 55-62, 2014.
  16. S. Kim, “Nested game-based computation offloading scheme for Mobile Cloud IoT systems”, Springer EURASIP Journal on Wireless Communications and Networking, No.1, p. 1-11, 2015.
  17. M. Satyanarayanan, P. Bahl, R. Caceres, and N. Davies, “The case for VM-based cloudlets in mobile computing”, IEEE Pervasive Computing Vol.8, pp.14–23, 2009.

INASS Home | Copyright@2008 The Intelligent Networks and Systems Society