Skip to main content

Job Scheduling with Adaptable Computing Levels for Edge Computing

  • Conference paper
  • First Online:
Algorithms and Architectures for Parallel Processing (ICA3PP 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11335))

Abstract

Edge computing is an emerging technology that can help huge number of devices be connected and processed with low latency. However, the performance of edge servers is far less powerful than cloud servers. When dealing with a large number of job requests from user devices, traditional job scheduling methods are not efficient enough. In this paper, we propose a new job scheduling model by considering adaptable jobs that can be executed with different computing levels and accordingly different resource requirements. We design a new job scheduling algorithm based on such an adaptable job model. The algorithm can choose an appropriate level for each job according to resource availability. Compared with existing works, our design can achieve better tradeoff between resource utilization and quality of experience. To the best of our knowledge, this is the first paper that considers adaptable job computing levels.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Soyata, T., Muraleedharan, R., Funai, C., Kwon, M., Heinzelman, W.: Cloud-vision: real-time face recognition using a mobile cloudlet-cloud acceleration architecture. In: International Symposium on Computers and Communications (2012)

    Google Scholar 

  2. Liu, J., Ahmed, E., Shiraz, M., Gani, A., Buyya, R., Qureshi, A.: Application partitioning algorithms in mobile cloud computing: taxonomy, review and future directions. J. Netw. Comput. Appl. 48, 99–117 (2015)

    Article  Google Scholar 

  3. Satyanarayanan, M., Bahl, P., Caceres, R., Davies, N.: The case for VM-based cloudlets in mobile computing. IEEE Pervasive Comput. 8(4), 14–23 (2011)

    Article  Google Scholar 

  4. Shi, W., Cao, J., Zhang, Q., Li, Y., Xu, L.: Edge computing: vision and challenges. IEEE Internet Things J. 3(5), 637–646 (2016)

    Article  Google Scholar 

  5. Wiseman, Y., Feitelson, D.G.: Paired gang scheduling. IEEE Trans. Parallel Distrib. Syst. 14(6), 581–592 (2003)

    Article  Google Scholar 

  6. Zaharia, M., Borthakur, D., Sen Sarma, J., Elmeleegy, K., Shenker, S., Stoica, I.: Delay scheduling: a simple technique for achieving locality and fairness in cluster scheduling. In: European Conference on Computer Systems, pp 265–278 (2010)

    Google Scholar 

  7. Lee, G., Chun, B., Katz, H.: Heterogeneity-aware resource allocation and scheduling in the cloud. In: IEEE International Conference on Cloud Computing Technology and Science, p. 4 (2011)

    Google Scholar 

  8. Lee, Y.H., Leu, S., Chang, R.S.: Improving job scheduling algorithms in a grid environment. Future Gener. Comput. Syst. 27(8), 991–998 (2011)

    Article  Google Scholar 

  9. Li, J., Feng, L., Fang, S.: An greedy-based job scheduling algorithm in cloud computing. J. Softw. 9(4), 921–925 (2014)

    Google Scholar 

  10. Grgic, S., Grgic, M., Zovko-Cihlar, B.: Performance analysis of image compression using wavelets. IEEE Trans. Ind. Electron. 48(3), 682–695 (2001)

    Article  Google Scholar 

  11. Dahl, G.E., Yu, D., Deng, L., Acero, A.: Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition. IEEE Trans. Audio Speech Lang. Process. 20(1), 30–42 (2012)

    Article  Google Scholar 

  12. Smith: Principles of data mining. Artif. Intell. Med. 26(1), 175–178 (2002)

    Article  Google Scholar 

  13. Lowe, D.: Object recognition from local scale-invariant features. In: International Conference on Computer Vision (1999)

    Google Scholar 

  14. Chen, X.: Decentralized computation offloading game for mobile cloud computing. IEEE Trans. Parallel Distrib. Syst. 26, 974–983 (2014)

    Article  Google Scholar 

  15. Application Performance Index. http://www.apdex.org/

Download references

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Huiwen Jiang or Weigang Wu .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Jiang, H., Wu, W. (2018). Job Scheduling with Adaptable Computing Levels for Edge Computing. In: Vaidya, J., Li, J. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2018. Lecture Notes in Computer Science(), vol 11335. Springer, Cham. https://doi.org/10.1007/978-3-030-05054-2_22

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-05054-2_22

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-05053-5

  • Online ISBN: 978-3-030-05054-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics