International Journal of Computer Networks and Applications (IJCNA)

Published By EverScience Publications

ISSN : 2395-0455

International Journal of Computer Networks and Applications (IJCNA)

International Journal of Computer Networks and Applications (IJCNA)

Published By EverScience Publications

ISSN : 2395-0455

Optimization of Computation and Communication Driven Resource Allocation in Mobile Cloud

Author NameAuthor Details

R. Shankar, Tharani Vimal

R. Shankar[1]

Tharani Vimal[2]

[1]Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India

[2]Department of Information Technology, St. Peter’s College of Engineering and Technology, Avadi, Chennai, Tamil Nadu, India

Abstract

With the emergence of accessing Smartphones in day-to-day life, Mobile Cloud Computing (MCC) technology has become popular with the advantage of resolving the resource constraints in mobile devices through the offloading method. The existing models have presented the different resource allocation solutions to ensure the seamless execution of the applications for the resource-constrained mobile devices with the Quality of Service (QoS). The optimization of resource allocation is the process of potentially allocating remote resources to mobile users without violating the Service Level Agreements (SLAs). However, resource allocation is still becoming a major constraint in the Mobile Cloud (MC) data centers due to higher consumption of energy and time factors during the execution of mobile requests on the remote cloud. The consumption of the energy and response time of the offloaded tasks or applications heavily relies on the cloud resource allocation for the mobile users. Hence, Resource Allocation Optimization (RAO) emerged as the significant objective to select the appropriate cloud resources for the requested tasks to increase the lifetime of the devices with improved time efficiency. Thus, this work focuses on optimizing MC resource allocation by optimizing the allocation of both the computation and communication resources. The proposed RAO model considers two potential factors, such as the energy and response time while allocating the computational and communicational resources. Initially, the Energy and Response time-driven RAO (EARO) approach prioritizes the request generated from the mobile users based on the estimated execution time. Modeling the Estimated Communication and Execution Time (ECET) algorithm tends to allocate the cloud resources and accomplish the minimal response time of the application requests. The EARO approach intends to minimize the execution time as well as the response time towards the target of alleviating the energy consumption during the resource allocation. Moreover, it selects the resources for the inter-VM communication with the knowledge of the minimal migration time ensuring bandwidth resources. Thus, EARO preserves the device's energy with minimal application completion time. The experimental results illustrate that the time efficiency of the proposed EARO model outperforms the existing resource allocation model in the MC environment.

Index Terms

MCC

Resource Allocation

Computation

Communication

Optimization

Energy Consumption

Bandwidth

Response Time

Reference

  1. 1.
    Fernando, Niroshinie Seng W. Loke, and Wenny Rahayu, “Mobile cloud computing: A survey”, Elsevier transaction on Future Generation Computer Systems, pp.84-106, Vol.29, No.1, 2013
  2. 2.
    Dinh Hoang T, Chonho Lee, Dusit Niyato, and Ping 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. 3.
    Hussain, Hameed, Saif Ur Rehman Malik, Abdul Hameed, Samee Ullah Khan, Gage Bickler, Nasro Min-Allah, and Muhammad Bilal Qureshi et al., “A survey on resource allocation in high performance distributed computing systems”, Elsevier transaction on Parallel Computing, Vol.39, No.11, pp.709-736, 2013
  4. 4.
    Arfeen, Muhammad Asad, Krzysztof Pawlikowski, and Andreas Willig, “A framework for resource allocation strategies in cloud computing environment”, IEEE transaction on Computer Software and Applications Conference Workshops (COMPSACW), pp.261-266, 2011
  5. 5.
    Madni, Syed Hamid Hussain, Muhammad Shafie Abd Latiff, and Yahaya Coulibaly, “Recent advancements in resource allocation techniques for cloud computing environment: a systematic review”, Cluster Computing, Vol.20, No.3, pp.2489-2533, 2017
  6. 6.
    Zhou, Bowen, and Rajkumar Buyya, “Augmentation techniques for mobile cloud computing: A taxonomy, survey, and future directions”, ACM Computing Surveys (CSUR),Vol.51, No.1, pp.1-38, 2018
  7. 7.
    HamaAli, Kurdistan Wns, and Subhi RM Zeebaree, “Resources allocation for distributed systems: A review”, International Journal of Science and Business, Vol.5, No.2, pp.76-88, 2021
  8. 8.
    Pallavi, L., B. Thirumala Rao, and A. Jagan, “Mobility Management Challenges and Solutions in Mobile Cloud Computing System for Next Generation Networks”, International Journal of Advanced Computer Science and Applications, Vol.11, No.3, pp.177-192, 2020
  9. 9.
    Shu, Peng, Fangming Liu, Hai Jin, Min Chen, Feng Wen, Yupeng Qu, and Bo Li, “eTime:energy-efficient transmission between cloud and mobile devices”, Proceedings IEEE in INFOCOM, pp.195-199, 2013
  10. 10.
    Rahimi, M. Reza, Nalini Venkatasubramanian, Sharad Mehrotra, and Athanasios V. Vasilakos, “On Optimal and Fair Service Allocation in Mobile Cloud Computing”, Distributed, Parallel, and Cluster computing, arXiv preprint arXiv:1308.4391, 21 pages, 2013
  11. 11.
    Sokol Kosta, Andrius Aucinas, Pan Hui, Richard Mortier, and Xinwen Zhang, “ThinkAir: Dynamic resource allocation and parallel execution in cloud for mobile code offloading”, IEEE proceedings INFOCOM, pp.945-953, 2012
  12. 12.
    Song, Jian, Yong Cui, Minming Li, Jiezhong Qiu, and Rajkumar Buyya, “Energy-Traffic Tradeoff Cooperative Offloading for Mobile Cloud Computing”, IEEE 22nd International symposium of Quality of service, pp.284-289, 2014
  13. 13.
    Ghasemi-Falavarjani, Simin, Mohammadali Nematbakhsh, and Behrouz Shahgholi Ghahfarokhi, “Context-aware multi-objective resource allocation in mobile cloud”, Computers & Electrical Engineering, Vol.44, pp.218-240, 2015
  14. 14.
    Sun, Huaiying, Huiqun Yu, Guisheng Fan, and Liqiong Chen, “Energy and time efficient task offloading and resource allocation on the generic IoT-fog-cloud architecture”, Peer-to-Peer Networking and Applications, Vol.13, No.2, pp.548-563, 2020
  15. 15.
    Wang, Kezhi, Kun Yang, and Chathura Sarathchandra Magurawalage. "Joint energy minimization and resource allocation in C-RAN with MOBILE CLOUD”, IEEE Transactions on Cloud Computing, Vol.6, No.3, pp.760-770, 2016
  16. 16.
    Jin, A-Long, Wei Song, and Weihua Zhuang, “Auction-based resource allocation for sharing cloudlets in MOBILE CLOUD computing”, IEEE Transactions on Emerging Topics in Computing, Vol.6, No.1, pp.45-57, 2015
  17. 17.
    Liu, Yanchen, Myung J. Lee, and Yanyan Zheng. "Adaptive multi-resource allocation for cloudlet-based MOBILE CLOUD computing system”, IEEE Transactions on Mobile Computing, Vol.15, No.10, pp.2398-2410¸ 2015
  18. 18.
    Chen, Meng-Hsi, Ben Liang, and Min Dong, “Joint offloading and resource allocation for computation and communication in mobile cloud with computing access point”, In IEEE INFOCOM 2017-IEEE Conference on Computer Communications, pp.1-9, 2017
  19. 19.
    Neha Jayant, Gagandeep, “Energy Efficient Dynamic Resource Allocation Technique in Mobile Cloud Computing”, International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Vol.6, No.8, 2017
  20. 20.
    Di Lorenzo, Paolo, Sergio Barbarossa, and Stefania Sardellitti, “Joint Optimization of Radio Resources and Code Partitioning in Mobile Cloud Computing”, arXiv preprint arXiv:1307.3835, pp.1-14, 2013
  21. 21.
    Angin Pelin, and Bharat Bhargava, “An agent-based optimization framework for mobile-cloud computing”, Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, Vol.4, No.2, pp.1-17, 2013
  22. 22.
    Barbarossa, Sergio, Stefania Sardellitti, and Paolo Di Lorenzo, “Joint allocation of computation and communication resources in multi-user mobile cloud computing”, IEEE 14th Workshop on Signal Processing Advances in Wireless Communications (SPAWC), pp.26-30, 2013
  23. 23.
    Kuribayashi, and Shin-ichi, “Optimal joint multiple resource allocation method for cloud computing environments”, International Journal of Research and Reviews in Computer Science (IJRRCS), arXiv preprint arXiv,1110.1730, Vol.2, No.1, 2011
  24. 24.
    Chaisiri, Sivadon, Bu-Sung Lee, and Dusit Niyato, “Optimization of resource provisioning cost in cloud computing”, IEEE Transactions on Services Computing, Vol.5, No.2, pp.164-177, 2012
  25. 25.
    Zhang, Weiwen, Yonggang Wen, Kyle Guan, Dan Kilper, Haiyun Luo, and D. Wu, “Energy-Optimal Mobile Cloud Computing under Stochastic Wireless Channel”, IEEE Transactions On Wireless Communications, Vol.12, No.9, pp.4569-4581, 2013
  26. 26.
    Hao, Liang, Gang Cui, Mingcheng Qu, and Wende Ke, “Resource Scheduling Optimization Algorithm of Energy Consumption for Cloud Computing Based on Task Tolerance”, Journal of Software, Vol.9, No.4, pp.895-901, 2014
  27. 27.
    Chrysa Papagianni, Aris Leivadeas, Symeon Papavassiliou, Vasilis Maglaris, Cristina Cervello Pastor, and Alvaro Monje, “On the optimal allocation of virtual resources in cloud computing networks”, IEEE Transactions on Computers, Vol.62, No.6, pp.1060-1071, 2013
  28. 28.
    Daren Fang, Xiaodong Liu, Lin Liu, and Hongji Yang, “OCSO: Off-the-cloud service optimization for green efficient service resource utilization”, Springer Open access Journal of Cloud Computing: Advances, Systems and Applications, Vol.3, No.9, pp.1-17, 2014
  29. 29.
    Younis, Ayman, Tuyen X. Tran, and Dario Pompili, “Bandwidth and energy-aware resource allocation for cloud radio access networks”, IEEE Transactions on Wireless Communications, Vol.17, No.10¸ pp.6487-6500, 2018
  30. 30.
    Liu, Li, and Qi Fan, “RAO based on mixed integer linear programming in the multi-cloudlet environment”, IEEE Access,Vol.6, pp.24533-24542, 2018
  31. 31.
    Meng, Sachula, Ying Wang, Zhongyu Miao, and Kai Sun, “Joint optimization of wireless bandwidth and computing resource in cloudlet-based mobile cloud computing environment”, Peer-to-Peer Networking and Applications, Vol.11, No.3, pp.462-472, 2018
  32. 32.
    Zhao, Junhui, Qiuping Li, Yi Gong, and Ke Zhang, “Computation offloading and resource allocation for cloud assisted mobile edge computing in vehicular networks”, IEEE Transactions on Vehicular Technology, Vol. 68, No.8, pp.7944-7956, 2019
  33. 33.
    Zhang, Jing, Weiwei Xia, Feng Yan, and Lianfeng Shen, “Joint computation offloading and RAO in heterogeneous networks with mobile edge computing”, IEEE Access, Vol.6 pp.19324-19337, 2018
  34. 34.
    Ramasubbareddy, Somula, G. Vedavasu, K. B. Gopi Krishna, and Alekhya Savithri, “PIOCM: Properly Identifying Optimized Cloudlet in Mobile Cloud Computing”, Journal of Computational and Theoretical Nanoscience, Vol.16, No.5-6, pp.1967-1971, 2019
  35. 35.
    Pallavi, L., A. Jagan, and B. Thirumala Rao, “ERMO2 algorithm: an energy efficient mobility management in mobile cloud computing system for 5G heterogeneous networks”, International Journal of Electrical and Computer Engineering, Vol.9, No.3, pp.1957-1967, 2019
  36. 36.
    Kiran, K. Tara Phani Surya, K. V. V. Satyanarayana, and P. Yellamma, “Advanced Q-MAC: optimal Resource allocating for dynamic application in mobile cloud computing using Qos with cache memory”, International Journal of Engineering & Technology Vol.7, No.3.1, pp.143-146, 2018
  37. 37.
    Kosta, Sokol, Andrius Aucinas, Pan Hui, Richard Mortier, and Xinwen Zhang, “Unleashing the power of MOBILE CLOUD computing using thinkair”, arXiv preprint arXiv:1105.3232, 2011.
SCOPUS
SCImago Journal & Country Rank