Skip to main content
SpringerLink
Log in

Dynamic cloud resource management for efficient media applications in mobile computing environments

  • Original Article
  • Published:
Personal and Ubiquitous Computing Aims and scope Submit manuscript

Abstract

Single-instruction-set architecture (Single-ISA) heterogeneous multi-core processors (HMP) are superior to Symmetric Multi-core processors in performance per watt. They are popular in many aspects of the Internet of Things, including mobile multimedia cloud computing platforms. One Single-ISA HMP integrates both fast out-of-order cores and slow simpler cores, while all cores are sharing the same ISA. The quality of service (QoS) is most important for virtual machine (VM) resource management in multimedia mobile computing, particularly in Single-ISA heterogeneous multi-core cloud computing platforms. Therefore, in this paper, we propose a dynamic cloud resource management (DCRM) policy to improve the QoS in multimedia mobile computing. DCRM dynamically and optimally partitions shared resources according to service or application requirements. Moreover, DCRM combines resource-aware VM allocation to maximize the effectiveness of the heterogeneous multi-core cloud platform. The basic idea for this performance improvement is to balance the shared resource allocations with these resources requirements. The experimental results show that DCRM behaves better in both response time and QoS, thus proving that DCRM is good at shared resource management in mobile media cloud computing.

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

Similar content being viewed by others

References

  1. Held J, Bautista J, Koehl S (2006) From a few cores to many: a tera-scale computing research review. Intel Research White Paper, http://download.intel.com/research/platform/terascale/terascale_overview_paper.pdf

  2. Rodrigues R, Annamalai A, Koren I, Kundu S (2012) Scalable thread scheduling in asymmetric multicores for power efficiency. In: 2012 IEEE 24th international symposium on computer architecture and high performance computing

  3. Han G, Que W, Jia G, Shu L (2016) An efficient virtual machine consolidation scheme for multimedia cloud computing. Sensors 16(2):Article 246

    Article  Google Scholar 

  4. Hill M, Marty M (2008) Amdahl’s Law in the multicore era. Computer 41(7):33–38

    Article  Google Scholar 

  5. Winter JA, Albonesi DH, Shoemaker CA (2010) Scalable thread scheduling and global power management for heterogeneous many-core architectures. In: Proceedings of the 19th international conference on parallel architectures and compilation techniques, ser. PACT’10

  6. Annavaram M, Grochowski E, Shen J (2005) Mitigating Amdahl’s Law through EPI throttling. In: Proceedings of ISCA’05, pp 298–309

  7. Kumar R, Farkas KI, Jouppi N et al (2003) Single-ISA heterogeneous multi-core architectures: the potential for processor power reduction. In: Proceedings of MICRO, vol 36

  8. Goldberg RP (1974) Survey of virtual machine research. Computer 7(9):34–45

    Article  Google Scholar 

  9. Rosenblum M, Garfinkel T (2005) Virtual machine monitors: current technology and future trends. Computer 38(5):39–47

    Article  Google Scholar 

  10. Chen L, Wei Z, Cui Z, Chen M, Pan H, Bao Y (2014) CMD: classification-based memory deduplication through page access characteristics. In: VEE’14

  11. Mutlu O, Moscibroda T (2008) Parallelism-aware batch scheduling: enhancing both performance and fairness of shared DRAM systems. In: ISCA

  12. Jeong M, Yoon D, Sunwoo D, Sullivan M, Lee I, Erez M (2012) Balancing DRAM locality and parallelism in shared memory CMP systems. In: HPCA

  13. Deng Q, Meisner D, Ramos L, Wenisch TF, Bianchini R (2011) MemScale: active low-power modes for main memory. In: ASPLOS

  14. Abdullahi M, Ngadi MA (2016) Symbiotic organism search optimization based task scheduling in cloud computing environment. Futur Gener Comput Syst 56:640–650

    Article  Google Scholar 

  15. Abdulhamid SM, Latiff MSA, Abdul-saalam G, Madni SHH (2016) Secure scientific applications scheduling technique for cloud computing environment using global league championship algorithm. Plos One. https://doi.org/10.1371/journal.pone.0158102

  16. Abdulhamid SM, Abd Latiff MS, Ismaila I (2014) Tasks scheduling technique using league championship algorithm for makespan minimization in IAAS cloud. ARPN J Eng Appl Sci 9(12):2528–2533

    Google Scholar 

  17. Abdulhamid SM, Abd Latiff SM, Bashir MB (2014) Scheduling techniques in on-demand grid as a service cloud: a review. J Theoret Appl Inf Technol 63(1):10–19

    Google Scholar 

  18. Madni SHH, Latiff MSA, Coulibaly Y, Abdulhamid SM (2016) Resource scheduling for infrastructure as a service (Iaas) in cloud computing: challenges and opportunities. J Netw Comput Appl 68:173–200

    Article  Google Scholar 

  19. Han G, Liu L, Jiang J, Shu L, Hancke G (2017) Analysis of energy-efficient connected target coverage algorithms for industrial wireless sensor networks. IEEE Trans Ind Inf 13(1):342– 350

    Article  Google Scholar 

  20. Lazri K, Laniepce S, Ben-Othman J (2013) When dynamic VM migration falls under the control of vm users. In: Proceedings of the IEEE 5th international conference on cloud computing technology and science. CloudCom, pp 395–402

  21. Han G, Wan L, Shu L, Feng N (2015) Two novel DoA estimation approaches for real time assistant calibration system in future vehicle industrial. IEEE Syst J. https://doi.org/10.1109/JSYST.2015.2434822

  22. Ficco M, Esposito C, Palmien F, Castiglione A (2016) A coral-reefs and game theory-based approach for optimizing elastic cloud resource allocation. Futur Gener Comput Syst. https://doi.org/10.1016/j.future.2016.05.025

  23. Arzuaga E, Kaeli DR (2010) Quantifying load imbalance on virtualized enterprise servers.. In: Proceedings of the 1st international conference on performance engineering, (WOSP/SIPEW), pp 235–242

  24. Jia G, Han G, Jiang J, Sun N, Wang K Dynamic resource partitioning for heterogeneous multi-core-based cloud computing in smart cities. IEEE Access. https://doi.org/10.1109/ACCESS.2015.2507576

  25. Han G, Liu L, Chan S, Yu R, Yang Y (2016) HySense: a hybrid mobile crowdsensing framework for sensing opportunities compensation under dynamic coverage constraint. IEEE Commun Mag. https://doi.org/10.1109/MCOM.2017.1600658CM

  26. Jia G, Han G, Jiang J, Liu L (2016) Dynamic adaptive replacement policy in shared last-level cache of DRAM/PCM hybrid memory for big data storage. IEEE Trans Ind Inf. https://doi.org/10.1109/TII.2016.2645941

  27. Liu L, Cui Z, Xing M, Bao Y, Chen M, Wu C (2012) A software memory partition approach for eliminating bank-level interference in multicore systems. In: PATC’12

  28. Lin J, Lu Q, ing XD, Zhang Z, Zhang X, Sadayappan P (2008) Gaining insights into multicore cache partitioning: bridging the gap between simulation and real systems. In: HPCA-14

  29. Xie M, Tong D, Feng Y, Huang K, Cheng X (2013) Page policy control with memory partitioning for DRAM performance and power efficiency. In: ISLPED

  30. Muralidhara S, Subramanian L, Mutlu O, Kandemir M, Moscibroda T (2011) Reducing memory interference in multicore systems via application-aware memory channel partitioning. In: MICRO

  31. Bellard F (2005) Qemu, a fast and portable dynamic translator. In: Proceedings of the annual conference on USENIX annual technical conference, ATEC’05, pp 41–46

  32. Kvm-kernel based virtual machine. http://www.linux-kvm.org/page/Main_Page

Download references

Acknowledgments

This work was supported by the National Science Foundation of China under Grant, No. 61602137, 61572172, 61401147 and 61401107, by by the Fundamental Research Funds for the Central Universities, No.2016B10714 and supported by Changzhou Sciences and Technology Program, No. CE20165023 and No. CE20160014 and Six talent peaks project in Jiangsu Province, No. XYDXXJS-00.

Author information

Authors and Affiliations

  1. The Department of Computer Science, Hangzhou Dianzi University, Hangzhou, 310018, China

    Gangyong Jia

  2. The Department of Information and Communication System, Hohai University, Jinling North Road, No. 200, Changzhou, 213022, China

    Guangjie Han & Jinfang Jiang

  3. The Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China

    Sammy Chan

  4. The Jiangsu Xin Zhongtian Plastic Industry Co., Ltd., Jiangsu, China

    Yuxin Liu

Authors
  1. Gangyong Jia
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Guangjie Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jinfang Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sammy Chan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yuxin Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Guangjie Han.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jia, G., Han, G., Jiang, J. et al. Dynamic cloud resource management for efficient media applications in mobile computing environments. Pers Ubiquit Comput 22, 561–573 (2018). https://doi.org/10.1007/s00779-018-1118-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00779-018-1118-5

Keywords

Search

Navigation