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Job Scheduling Under Differential Pricing: Hardness and Approximation Algorithms

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Book cover Wireless Algorithms, Systems, and Applications (WASA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10251))

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Abstract

To induce a favorable energy demand pattern, generalized pricing models were proposed to achieve better aggregated energy consumption pattern. In this work we study how to schedule jobs under two differential pricing models, namely the combined pricing of day ahead pricing (DAP) and inclining block rate (IBR) both in the micro scope and macro scope. In the micro scope we study offline job scheduling with a goal to minimize the electricity cost of consumers when the electricity price and job profile are known beforehand. In the macro scope we study the aggregated effect on the cost of power generation when each entity (e.g., a household or a factory) schedules their jobs autonomously. We first prove that the job scheduling problems are either APX-hard or NP-hard under two combined price models of DAP and IBR. We then present efficient methods with bounded approximation ratio and show that our scheduling achieves comparable electricity cost saving.

The research of Li is partially supported by China National Funds for Distinguished Young Scientists with No. 61625205, Key Research Program of Frontier Sciences, CAS, Nos. QYZDY-SSW-JSC002, NSF CMMI 1436786, and NSF CNS 1526638.

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Authors and Affiliations

  1. University of Science and Technology of China, Hefei, China

    Xiang-Yang Li

  2. Illinois Institute of Technology, Chicago, USA

    Jing Zhao, Haohua Du & Jiahui Hou

  3. University of Florida, Gainesville, USA

    Qiuyuan Huang

Authors
  1. Qiuyuan Huang
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  2. Jing Zhao
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  3. Haohua Du
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  4. Jiahui Hou
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  5. Xiang-Yang Li
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Corresponding authors

Correspondence to Jing Zhao or Xiang-Yang Li .

Editor information

Editors and Affiliations

  1. Texas Christian University, Fort Worth, Texas, USA

    Liran Ma

  2. New Jersey Institute of Technology, Newark, New Jersey, USA

    Abdallah Khreishah

  3. University of Oslo, Oslo, Norway

    Yan Zhang

  4. University of North Georgia, Dahlonega, Georgia, USA

    Mingyuan Yan

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Huang, Q., Zhao, J., Du, H., Hou, J., Li, XY. (2017). Job Scheduling Under Differential Pricing: Hardness and Approximation Algorithms. In: Ma, L., Khreishah, A., Zhang, Y., Yan, M. (eds) Wireless Algorithms, Systems, and Applications. WASA 2017. Lecture Notes in Computer Science(), vol 10251. Springer, Cham. https://doi.org/10.1007/978-3-319-60033-8_55

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  • DOI: https://doi.org/10.1007/978-3-319-60033-8_55

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60032-1

  • Online ISBN: 978-3-319-60033-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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