Skip to main content
SpringerLink
Log in

Workload scheduling for Nokkhum video surveillance as a service

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Video Surveillance Systems (VSS) on the Internet as known as Video Surveillance as a Service (VSaaS) or Cloud based Video Surveillance (CVS) systems. Video processing workload analysis has usually employed only one category of static video processing attribute, such as a frame rate with a single frame size on the same computing node specification, but VSaaS must handle a variety of video processing attributes. Also, in a static workload, it is difficult to identify the resource consumption of video processing attributes, especially involving a combination of frame rates and sizes on different computing nodes on virtual or physical machines. Consequently, it is difficult to place a task on a computing node if the resource usage information is unknown to the scheduler. In this paper, the video processing workload characteristics utilize various parameters, such as the type of video processing task, frame rate, frame size, and compute node specification. The analysis results have helped us to design a scheduler that supports different computing node specifications. We explore video processing workload for testing resource usage capacity in several computing nodes, and collect information for the scheduler’s estimation. This paper also proposes a resource estimation module for predicting the video processing resource usage for a new video processing task when there is no matching or close estimation. Furthermore, we suggest scheduler criteria for optimizing system resource usage.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  1. Alamri A, Hossain M S, Almogren A, Hassan M M, Alnafjan K, Zakariah M, Seyam L, Alghamdi A (2015) QoS-adaptive service configuration framework for cloud-assisted video surveillance systems. Multimed Tools Appl:1–16. doi:10.1007/s11042-015-3074-7

  2. Crockford D (2006) Json: the fat-free alternative to xml. In: Proceeding of XML, vol 2006

  3. Fielding R T, Taylor R N (2002) Principled design of the modern web architecture. ACM Trans Int Technol 2(2):115–150. doi:10.1145/514183.514185

    Article  Google Scholar 

  4. Hossain MA, Hossain MA (2014) Framework for a cloud-based multimedia surveillance system, framework for a cloud-based multimedia surveillance system. Int J Distrib Sensor Netw e135(257):2014. doi:10.1155/2014/135257

    Google Scholar 

  5. Hossain M, Hassan M, Qurishi M, Alghamdi A (2012) Resource allocation for service composition in cloud-based video surveillance platform. In: 2012 IEEE International conference on multimedia and expo workshops (ICMEW), pp 408 –412. doi:10.1109/ICMEW.2012.77

  6. Hossain M S (2013) QoS-aware service composition for distributed video surveillance. Multimed Tools Appl 73(1):169–188. doi:10.1007/s11042-012-1312-9

    Article  Google Scholar 

  7. Karimaa A (2011) Video surveillance in the cloud: dependability analysis. In: The Fourth international conference on dependability. IARIA XPS Press, pp 92–95. https://www.thinkmind.org/download.php?articleid=depend_2011_4_20_40042

  8. Kivity A, Kamay Y, Laor D, Lublin U, Liguori A (2007) kvm: the linux virtual machine monitor. In: Proceedings of the linux symposium, vol 1, pp 225–230

  9. Lee J, Feng T, Shi W, Bedagkar-Gala A, Shah S, Yoshida H (2012) Towards quality aware collaborative video analytic cloud. In: 2012 IEEE 5th international conference on cloud computing (CLOUD), pp 147–154. doi:10.1109/CLOUD.2012.141

  10. Limna T, Tandayya P (2014) A flexible and scalable component-based system architecture for video surveillance as a service, running on infrastructure as a service. Multimed Tools Appl:1–27. doi:10.1007/s11042-014-2373-8

  11. Lin C F, Yuan S M, Leu M C, Tsai C T (2012) A framework for scalable cloud video recorder system in surveillance environment. In: Proceedings of the 2012 9th international conference on ubiquitous intelligence computing and 9th international conference on autonomic trusted computing (UIC/ATC), pp 655–660. doi:10.1109/UIC-ATC.2012.72

  12. Miao D, Zhu W, Luo C, Chen C W (2011) Resource allocation for cloud-based free viewpoint video rendering for mobile phones. In: Proceedings of the 19th ACM international conference on multimedia, MM ’11. ACM, pp 1237–1240. doi:10.1145/2072298.2071983

  13. Nan X, He Y, Guan L (2011) Optimal resource allocation for multimedia cloud based on queuing model. In: 2011 IEEE 13th international workshop on multimedia signal processing (MMSP), pp 1–6. doi:10.1109/MMSP.2011.6093813

  14. Oh S, Hoogs A, Perera A, Cuntoor N, Chen C C, Lee J T, Mukherjee S, Aggarwal J K, Lee H, Davis L, Swears E, Wang X, Ji Q, Reddy K, Shah M, Vondrick C, Pirsiavash H, Ramanan D, Yuen J, Torralba A, Song B, Fong A, Roy-Chowdhury A, Desai M (2011) A large-scale benchmark dataset for event recognition in surveillance video. In: Proceedings of the 2011 IEEE conference on computer vision and pattern recognition, CVPR ’11. IEEE Computer Society, pp 3153–3160. doi:10.1109/CVPR.2011.5995586

  15. Prati A, Vezzani R, Fornaciari M, Cucchiara R (2013) Intelligent video surveillance as a service. In: Atrey PK, Kankanhalli MS, Cavallaro A (eds) Intelligent multimedia surveillance, Springer Berlin Heidelberg, pp 1–16. http://link.springer.com/chapter/10.1007/978-3-642-41512-8_1

  16. Vinoski S (2006) Advanced message queuing protocol. IEEE Int Comput 10(6):87–89. doi:10.1109/MIC.2006.116

    Article  Google Scholar 

  17. Wu YS, Chang YS, Juang TY, Yen JS (2012) An architecture for video surveillance service based on P2P and cloud computing. In: Proceedings of the 2012 9th international conference on ubiquitous intelligence computing and 9th international conference on autonomic trusted computing (UIC/ATC), pp 661–666. doi:10.1109/UIC-ATC.2012.43

Download references

Acknowledgments

The authors are grateful for financial support from the Thailand Research Fund and Prince of Songkla University through the Royal Golden Jubilee Ph.D. Program (Grant No. PHD/0047/2552).

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Faculty of Engineering, Prince of Songkla University, Hat Yai, Songkhla, Thailand

    Thanathip Limna & Pichaya Tandayya

Authors
  1. Thanathip Limna
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pichaya Tandayya
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pichaya Tandayya.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Limna, T., Tandayya, P. Workload scheduling for Nokkhum video surveillance as a service. Multimed Tools Appl 77, 1363–1389 (2018). https://doi.org/10.1007/s11042-016-4225-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-016-4225-1

Keywords

Search

Navigation