ISSN 0253-2778

CN 34-1054/N

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Open AccessOpen Access JUSTC Original Paper

User behavior model based TVOS resource allocation

  • University of Science and Technology of China, Department of Automation, Network Communication System and Control, Hefei 230027

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2014.01.002
  • Received Date: 15 October 2012
  • Accepted Date: 16 March 2013
  • Rev Recd Date: 16 March 2013
  • Publish Date: 30 January 2014
    Abstract

  • Abstract

    The resource allocation of the existing smart TV operating system (TVOS) depends on the operating system task resource allocation scheme, which tries to maximize the throughput of the system. However, this scheme cannot guarantee the quality of service (QoS) of applications in the real-time or multimedia system. In order to solve this problem, the user preferences of applications based on the user behavior model of TVOS were quantified, and two resource allocation algorithms named RA_DP and RA_PLSH were proposed based on the application QoS model. The experimental result shows that the algorithm based on dynamic programming (RA_DP) ensures optimal solution with high time complexity, and can be used as a reference for other algorithms. However, the other algorithm based on resource pricing local search heuristic (RA_PLSH) can obtain near-optimal solution in a very short time, and is thus more suitable for smart TV real-time resource allocation.

    Abstract

    The resource allocation of the existing smart TV operating system (TVOS) depends on the operating system task resource allocation scheme, which tries to maximize the throughput of the system. However, this scheme cannot guarantee the quality of service (QoS) of applications in the real-time or multimedia system. In order to solve this problem, the user preferences of applications based on the user behavior model of TVOS were quantified, and two resource allocation algorithms named RA_DP and RA_PLSH were proposed based on the application QoS model. The experimental result shows that the algorithm based on dynamic programming (RA_DP) ensures optimal solution with high time complexity, and can be used as a reference for other algorithms. However, the other algorithm based on resource pricing local search heuristic (RA_PLSH) can obtain near-optimal solution in a very short time, and is thus more suitable for smart TV real-time resource allocation.
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    • References(14)
  • [1]
    Rajkumar R, Lee C, Lehoczky J, et al. A resource allocation model for QoS management[C]// Proceedings of the 18th IEEE Real-Time Systems Symposium. San Francisco, USA: IEEE Press, 1997: 298-307.
    [2]
    Rajkumar R, Lee C, Lehoczky J P, et al. Practical solutions for QoS-based resource allocation problems[C]// Proceedings of the 19th IEEE Real-Time Systems Symposium. Madrid, Spain: IEEE Computer Society, 1998: 296-306.
    [3]
    Lee C, Lehoczky J, Rajkumar R, et al. On quality of service optimization with discrete QoS options[C]// Proceedings of the 5th IEEE Real-Time Technology and Applications Symposium. Vancouver, Canada: IEEE Computer Society, 1999: 276-286.
    [4]
    Lee C, Lehoczky J, Siewiorek D, et al. A scalable solution to the multi-resource QoS problem[C]// Proceedings of the 20th IEEE Real-Time Systems Symposium. Phoenix, USA: IEEE Computer Society, 1999: 315-326.
    [5]
    Gertphol S, Prasanna V K. MIP formulation for robust resource allocation in dynamic real-time systems[J]. Journal of Systems and Software, 2005, 77(1): 55-65.
    [6]
    Harada F, Ushio T, Nakamoto Y. Adaptive resource allocation control for fair QoS management[J]. IEEE Transactions on Computers, 2007, 56(3): 344-357.
    [7]
    Khan S. Quality adaptation in a multisession multimedia system: Model, algorithms and architecture[D]. Department of ECE, University of Victoria, 1998.
    [8]
    Toyoda Y. A simplified algorithm for obtaining approximate solution to zero-one programming problems[J]. Management Science, 1975, 21(12): 1 417-1 427.
    [9]
    Akbar M M, Manning E G, Shoja G C, et al. Heuristic solutions for the multiple-choice multi-dimension knapsack problem[C]// Proceedings of the International Conference on Computational Science. San Francisco: Springer-Verlag, 2001: 659-668.
    [10]
    Shahriar M, Akbar M M, Rahman M S, et al. A multiprocessor based heuristic for multi-dimensional multiple-choice knapsack problem[J]. The Journal of Supercomputing, 2008, 43(3): 257-280.
    [11]
    伍之昂, 罗君舟, 宋爱波，等. 具有QoS保证的服务资源联合分配与管理[J]. 软件学报, 2009, 20(12): 3 150-3 162.
    [12]
    Verkasalo H. Analysis of smartphone user behavior[C]// Proceedings of the 9th International Conference on Mobile Busines/9th Global Mobility Roundtable. Athens, Greece: IEEE Computer Society, 2010: 258-263.
    [13]
    Gerber S, Fry M, Kay J, et al. PersonisJ: Mobile, client-side user modelling[C]// Proceedings of the18th International Conference on User Modeling, Adaptation, and Personalization. Big Island, USA: Springer, 2010: 6075: 111-122.
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    维基百科. 凸包[EB/OL]. http://zh.wikipedia.org/wiki/%E5%87%B8%E5%8C%85.
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    [1]
    Rajkumar R, Lee C, Lehoczky J, et al. A resource allocation model for QoS management[C]// Proceedings of the 18th IEEE Real-Time Systems Symposium. San Francisco, USA: IEEE Press, 1997: 298-307.
    [2]
    Rajkumar R, Lee C, Lehoczky J P, et al. Practical solutions for QoS-based resource allocation problems[C]// Proceedings of the 19th IEEE Real-Time Systems Symposium. Madrid, Spain: IEEE Computer Society, 1998: 296-306.
    [3]
    Lee C, Lehoczky J, Rajkumar R, et al. On quality of service optimization with discrete QoS options[C]// Proceedings of the 5th IEEE Real-Time Technology and Applications Symposium. Vancouver, Canada: IEEE Computer Society, 1999: 276-286.
    [4]
    Lee C, Lehoczky J, Siewiorek D, et al. A scalable solution to the multi-resource QoS problem[C]// Proceedings of the 20th IEEE Real-Time Systems Symposium. Phoenix, USA: IEEE Computer Society, 1999: 315-326.
    [5]
    Gertphol S, Prasanna V K. MIP formulation for robust resource allocation in dynamic real-time systems[J]. Journal of Systems and Software, 2005, 77(1): 55-65.
    [6]
    Harada F, Ushio T, Nakamoto Y. Adaptive resource allocation control for fair QoS management[J]. IEEE Transactions on Computers, 2007, 56(3): 344-357.
    [7]
    Khan S. Quality adaptation in a multisession multimedia system: Model, algorithms and architecture[D]. Department of ECE, University of Victoria, 1998.
    [8]
    Toyoda Y. A simplified algorithm for obtaining approximate solution to zero-one programming problems[J]. Management Science, 1975, 21(12): 1 417-1 427.
    [9]
    Akbar M M, Manning E G, Shoja G C, et al. Heuristic solutions for the multiple-choice multi-dimension knapsack problem[C]// Proceedings of the International Conference on Computational Science. San Francisco: Springer-Verlag, 2001: 659-668.
    [10]
    Shahriar M, Akbar M M, Rahman M S, et al. A multiprocessor based heuristic for multi-dimensional multiple-choice knapsack problem[J]. The Journal of Supercomputing, 2008, 43(3): 257-280.
    [11]
    伍之昂, 罗君舟, 宋爱波，等. 具有QoS保证的服务资源联合分配与管理[J]. 软件学报, 2009, 20(12): 3 150-3 162.
    [12]
    Verkasalo H. Analysis of smartphone user behavior[C]// Proceedings of the 9th International Conference on Mobile Busines/9th Global Mobility Roundtable. Athens, Greece: IEEE Computer Society, 2010: 258-263.
    [13]
    Gerber S, Fry M, Kay J, et al. PersonisJ: Mobile, client-side user modelling[C]// Proceedings of the18th International Conference on User Modeling, Adaptation, and Personalization. Big Island, USA: Springer, 2010: 6075: 111-122.
    [14]
    维基百科. 凸包[EB/OL]. http://zh.wikipedia.org/wiki/%E5%87%B8%E5%8C%85.
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