Abstract
As mentioned in the previous chapters, domain-specific clouds and particularly the MSC platform take advantage of heterogeneous systems for their compute engine. The virtualization platform to enable isolation across users is another dimension of the computing engine in the MSC platform. In this chapter, we investigate how heterogeneous machines can be efficiently harnessed to maximize the QoE and minimize the cost in the MSC platform. In addition, the overhead of various virtualization platforms is analyzed. The chapter is concluded by providing a case-study on how heterogeneous computing is used in practice for live streaming.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
The video file is free-licensed and is publicly available in the following address: https://peach.blender.org/download/.
- 2.
- 3.
The videos can be downloaded from: https://goo.gl/TE5iJ5.
- 4.
The workload traces are available at: https://goo.gl/B6T5aj.
- 5.
This is big_buck_bunny_720p_h264_02tolibx264 video in the benchmark.
- 6.
Similarly, the value of Δth can be obtained from the cost preference value: \(\Delta _{th} = \frac {\ln {\frac {c}{1-c}}}{\alpha } - \beta \).
- 7.
- 8.
- 9.
References
S. Newman, Building microservices: designing fine-grained systems. O’Reilly Media, Inc., 2015.
M. Parashar, M. AbdelBaky, I. Rodero, and A. Devarakonda, “Cloud paradigms and practices for computational and data-enabled science and engineering,” Computing in Science & Engineering, vol. 15, no. 4, pp. 10–18, Jul 2013.
Z. Li, M. Kihl, Q. Lu, and J. A. Andersson, “Performance overhead comparison between hypervisor and container based virtualization,” in Proceedings of the 31st IEEE International Conference on Advanced Information Networking and Applications, ser. AINA ’17, Mar. 2017.
R. Morabito, J. Kjällman, and M. Komu, “Hypervisors vs. lightweight virtualization: a performance comparison,” in Proceedings of the IEEE International Conference on Cloud Engineering, Mar. 2015.
W. Felter, A. Ferreira, R. Rajamony, and J. Rubio, “An updated performance comparison of virtual machines and linux containers,” in Proceedings of the IEEE international symposium on performance analysis of systems and software, ser. ISPASS ’15, Mar. 2015, pp. 171–172.
R. K. Barik, R. K. Lenka, R. K. Rahul, and D. Ghose, “Performance analysis of virtual machines and containers in cloud computing,” in Proceedings of the IEEE International Conference on Computing, Communication and Automation, ser. ICCCA ’16, Apr. 2016.
L. Wang, M. Li, Y. Zhang, T. Ristenpart, and M. Swift, “Peeking behind the curtains of serverless platforms,” in Proceedings of the 2018 USENIX Annual Technical Conference, ser. USENIX ATC ’18, July 2018, pp. 133–146.
W. Lloyd, S. Ramesh, S. Chinthalapati, L. Ly, and S. Pallickara, “Serverless computing: An investigation of factors influencing microservice performance,” in Proceedings of the 2018 IEEE International Conference on Cloud Engineering, ser. (IC2E’18), Apr. 2018, pp. 159–169.
A. W. Services. Amazon web services. [Online]. Available: https://aws.amazon.com/lambda/
M. A. Services. Azure service fabric. [Online]. Available: https://azure.microsoft.com/en-us/services/service-fabric/
A. Podzimek, L. Bulej, L. Y. Chen, W. Binder, and P. Tuma, “Analyzing the impact of cpu pinning and partial cpu loads on performance and energy efficiency,” in Proceedings of the 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, May 2015, pp. 1–10.
M. Amini Salehi, B. Javadi, and R. Buyya, “Resource provisioning based on leases preemption in InterGrid,” in Proceeding of the 34th Australasian Computer Science Conference, ser. ACSC ’11, 2011, pp. 25–34.
M. A. Salehi and R. Buyya, “Contention-aware resource management system in a virtualized grid federation,” in PhD Symposium of the 18th international conference on High performance computing, ser. HiPC ’11, Dec. 2011.
Amazon, “Aws nitro system.” [Online]. Available: https://aws.amazon.com/ec2/nitro/
HCI, “Hci: Hyper converge infrastructure.” [Online]. Available: https://en.wikipedia.org/wiki/Hyper-converged_infrastructure
N. technologies, “Nutanix: Hyper converge infrastructure.” [Online]. Available: https://www.nutanix.com/
Maxta, “Maxta: Hyper converge infrastructure.” [Online]. Available: https://www.maxta.com/
Cloudistics, “Cloudistics: Hyper converge infrastructure.” [Online]. Available: https://www.cloudistics.com/
D. Technologies, “Dell emc unity xt all-flash unified storage.” [Online]. Available: https://www.delltechnologies.com/en-us/storage/unity.htm
A. F. Nogueira, J. C. Ribeiro, M. Zenha-Rela, and A. Craske, “Improving la redoute’s ci/cd pipeline and devops processes by applying machine learning techniques,” in Proceedings of the 11th International Conference on the Quality of Information and Communications Technology, ser. QUATIC ’18, Sep. 2018, pp. 282–286.
C. Dupont, R. Giaffreda, and L. Capra, “Edge computing in IoT context: Horizontal and vertical linux container migration,” in Proceedings of the Global Internet of Things Summit, ser. GIoTS ’17, Jun. 2017, pp. 1–4.
Kernel-ORG, “Cgroup in kernel.org.” [Online]. Available: https://www.kernel.org/doc/Documentation/cgroup-v1/cgroups.txt
J. Bacik, “IO and cgroups, the current and future work.” Boston, MA: USENIX Association, Feb 2019.
C. Yu and F. Huan, “Live migration of docker containers through logging and replay,” in Proceedings of the 3rd International Conference on Mechatronics and Industrial Informatics, ser. ICMII ’15, Oct. 2015.
J. Thönes, “Microservices,” IEEE software, vol. 32, no. 1, pp. 116–116, 2015.
L. Ao, L. Izhikevich, G. M. Voelker, and G. Porter, “Sprocket: A serverless video processing framework,” in Proceedings of the ACM Symposium on Cloud Computing, ser. SoCC ’18, 2018, pp. 263–274.
X. Li, M. A. Salehi, M. Bayoumi, N.-F. Tzeng, and R. Buyya, “Cost-efficient and robust on-demand video stream transcoding using heterogeneous cloud services,” IEEE Transactions on Parallel and Distributed Systems (TPDS), vol. 29, no. 3, pp. 556–571, Mar. 2018.
X. Li, M. A. Salehi, Y. Joshi, M. K. Darwich, B. Landreneau, and M. Bayoumi, “Performance analysis and modeling of video transcoding using heterogeneous cloud services,” IEEE Transactions on Parallel and Distributed Systems, vol. 30, no. 4, p. 910–922, Apr. 2019.
A. M. Al-Qawasmeh, A. A. Maciejewski, R. G. Roberts, and H. J. Siegel, “Characterizing task-machine affinity in heterogeneous computing environments,” in Proceedings of 25th IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum, ser. IPDPSW ’11, pp. 34–44, May 2011.
M. A. Salehi, J. Smith, A. A. Maciejewski, H. J. Siegel, E. K. P. Chong, J. Apodaca, L. D. Briceno, T. Renner, V. Shestak, J. Ladd, A. Sutton, D. Janovy, S. Govindasamy, A. Alqudah, R. Dewri, and P. Prakash, “Stochastic-based robust dynamic resource allocation in heterogeneous computing system,” Journal of Parallel and Distributed Computing (JPDC), vol. 97, pp. 96–111, June 2016.
A. M. Al-Qawasmeh, A. A. Maciejewski, R. G. Roberts, and H. J. Siegel, “Characterizing task-machine affinity in heterogeneous computing environments,” in Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW), 2011 IEEE International Symposium on. IEEE, 2011, pp. 34–44.
M. Maheswaran, S. Ali, H. J. Siegel, D. Hensgen, and R. F. Freund, “Dynamic mapping of a class of independent tasks onto heterogeneous computing systems,” Journal of parallel and distributed computing, vol. 59, no. 2, pp. 107–131, 1999.
B. Khemka, A. A. Maciejewski, and H. J. Siegel, “A performance comparison of resource allocation policies in distributed computing environments with random failures,” in Proceedings of the International Conference on Parallel and Distributed Processing Techniques and Applications, ser. PDPTA ’12, pp. 1, June 2012.
S. Ali, H. J. Siegel, M. Maheswaran, D. Hensgen, and S. Ali, “Representing task and machine heterogeneities for heterogeneous computing systems,” Journal of Applied Science and Engineering, vol. 3, no. 3, pp. 195–207, Sep. 2000.
A. M. Al-Qawasmeh, A. A. Maciejewski, and H. J. Siegel, “Characterizing heterogeneous computing environments using singular value decomposition,” in Proceedings of the IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum, ser. IPDPSW ’10, pp. 1–9, Apr. 2010.
G. Lee and R. H. Katz, “Heterogeneity-aware resource allocation and scheduling in the cloud,” in Proceedings of the 3rd USENIX Conference on Hot Topics in Cloud Computing, ser. HotCloud ’11, pp. 4, Oct. 2011.
K. R. Jackson, L. Ramakrishnan, K. Muriki, S. Canon, S. Cholia, J. Shalf, H. J. Wasserman, and N. J. Wright, “Performance analysis of high performance computing applications on the amazon web services cloud,” in Proceedings of the 2nd IEEE International Conference on Cloud Computing Technology and Science, ser. CloudCom ’10, pp. 159–168, Nov. 2010.
G. B. Berriman, G. Juve, E. Deelman, M. Regelson, and P. Plavchan, “The application of cloud computing to astronomy: A study of cost and performance,” in Proceedings of the 6th IEEE International Conference on-Science Workshops, pp. 1–7, Oct. 2010.
R. R. Expósito, G. L. Taboada, S. Ramos, J. Touriño, and R. Doallo, “General-purpose computation on GPUs for high performance cloud computing,” Concurrency and Computation: Practice and Experience, vol. 25, no. 12, pp. 1628–1642, May 2012.
K. P. Puttaswamy, C. Kruegel, and B. Y. Zhao, “Silverline: toward data confidentiality in storage-intensive cloud applications,” in Proceedings of the 2nd ACM Symposium on Cloud Computing, pp. 10, Oct. 2011.
V. K. Adhikari, Y. Guo, F. Hao, M. Varvello, V. Hilt, M. Steiner, and Z.-L. Zhang, “Unreeling netflix: Understanding and improving multi-cdn movie delivery,” in Proceedings the 31st Annual IEEE International Conference on Computer Communications, ser. INFOCOM ’12, pp. 1620–1628, Mar. 2012.
I. Ahmad, X. Wei, Y. Sun, and Y.-Q. Zhang, “Video transcoding: an overview of various techniques and research issues,” IEEE Transactions on Multimedia, vol. 7, no. 5, pp. 793–804, Oct. 2005.
A. Vetro, C. Christopoulos, and H. Sun, “Video transcoding architectures and techniques: an overview,” IEEE Magazine on Signal Processing, vol. 20, no. 2, pp. 18–29, Mar. 2003.
T. Shanableh and M. Ghanbari, “Heterogeneous video transcoding to lower spatio-temporal resolutions and different encoding formats,” IEEE Transactions on Multimedia, vol. 2, no. 2, pp. 101–110, June 2000.
P. Yin, M. Wu, and B. Liu, “Video transcoding by reducing spatial resolution,” in Proceedings of International Conference on Image Processing, ser. ICIP ’00, vol. 1, pp. 972–975, Sep. 2000.
T. Dillon, C. Wu, and E. Chang, “Cloud computing: issues and challenges,” in Proceedings of the 24th IEEE international conference on advanced information networking and applications, ser. AINA ’10, pp. 27–33, Apr. 2010.
M. L. Puri and D. A. Ralescu, “Differentials of fuzzy functions,” Journal of Mathematical Analysis and Applications, vol. 91, no. 2, pp. 552–558, Feb. 1983.
R. N. Calheiros, R. Ranjan, A. Beloglazov, C. A. De Rose, and R. Buyya, “Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms,” Software: Practice and Experience, vol. 41, pp. 23–50, Aug. 2011.
X. Li, M. A. Salehi, M. Bayoumi, N. F. Tzeng, and R. Buyya, “Cost-efficient and robust on-demand video transcoding using heterogeneous cloud services,” IEEE Transactions on Parallel and Distributed Systems (TPDS), vol. 29, no. 3, pp. 556–571, March 2018.
M. A. Salehi, J. Smith, A. A. Maciejewski, H. J. Siegel, E. K. Chong, J. Apodaca, L. D. Briceño, T. Renner, V. Shestak, J. Ladd et al., “Stochastic-based robust dynamic resource allocation for independent tasks in a heterogeneous computing system,” Journal of Parallel and Distributed Computing (JPDC), vol. 97, pp. 96–111, Nov. 2016.
I. F. Spellerberg and P. J. Fedor, “A tribute to Claude Shannon (1916–2001) and a plea for more rigorous use of species richness, species diversity and the ‘Shannon–Wiener’ index,” Global ecology and biogeography, vol. 12, no. 3, pp. 177–179, May 2003.
“Promising Initial Results with AV1 Testing,” https://streaminglearningcenter.com/blogs/promising-initial-results-with-av1-testing.html, accessed on June. 07, 2021.
“AV1 Now Only 2X Slower than X265,” https://streaminglearningcenter.com/blogs/av1-now-only-2x-slower-than-x265.html, accessed on June. 07, 2021.
“Intel Quick Sync Encoder,” https://www.intel.com/content/www/us/en/architecture-and-technology/quick-sync-video/quick-sync-video-general.html, accessed on June. 07, 2021.
“How VP9 Delivers Value for Twitch’s Esports Live Streaming,” https://blog.twitch.tv/en/2018/12/19/how-v-p9-delivers-value-for-twitch-s-esports-live-streaming-35db26f6322f/, accessed on June. 07, 2021.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Amini Salehi, M., Li, X. (2021). Computing Infrastructure for Multimedia Streaming Clouds (MSC). In: Multimedia Cloud Computing Systems. Springer, Cham. https://doi.org/10.1007/978-3-030-88451-2_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-88451-2_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-88450-5
Online ISBN: 978-3-030-88451-2
eBook Packages: Computer ScienceComputer Science (R0)