Abstract
The number of mobile devices (e.g., smartphones, tablets, wearable devices) is rapidly growing. In line with this trend, a massive amount of mobile videos with metadata (e.g., geospatial properties), which are captured using the sensors available on these devices, are being collected. Clearly, a computing infrastructure is needed to store and manage this ever-growing large-scale video dataset with its structured data. Meanwhile, cloud computing service providers such as Amazon, Google and Microsoft allow users to lease computing resources with varying combinations of computing resources such as disk, network and CPU capacities. To effectively use these emerging cloud platforms in support of mobile video applications, the application workflow and resources required at each stage must be clearly defined. In this paper, we deploy a mobile video application (dubbed MediaQ), which manages a large amount of user-generated mobile videos, to Amazon EC2. We define a typical video upload workflow consisting of three phases: (1) video transmission and archival, (2) metadata insertion to database, and (3) video transcoding. While this workflow has a heterogeneous load profile, we introduce a single metric, frames-per-second, for video upload benchmarking and evaluation purposes on various cloud server types. This single metric enables us to quantitatively compare main system resources (disk, CPU, and network) with each other towards selecting the right server types on cloud infrastructure for this workflow.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Amazon EC2. http://docs.aws.amazon.com/AWSEC2/latest/UserGuide/instance-types.html
MediaQ Framework. http://mediaq.usc.edu
Kim S.H., Lu Y., Constantinou, G., Shahabi, C, Wang, G, Zimmermann, R.: MediaQ: mobile multimedia management system. In:5th ACM Multimedia Systems Conference, pp. 224–235. ACM, New York (2014)
Oracle. http://docs.oracle.com/cd/B12037_01/appdev.101/b10795/adfns_in.htm
Wang, G., Eugene, T.S.: The impact of virtualization on network performance of amazon EC2 data center. In: 29th Conference on Information Communications (INFOCOM), pp. 1163–1171. IEEE Press, Piscataway (2010)
Amdahl G.: Validity of the single processor approach to achieving large-scale computing capabilities. In: Spring Joint Conference (AFIPS), pp. 483–485. ACM, New York (1967)
Cisco’s Forecast. http://www.cisco.com/c/en/us/solutions/collateral/service-provider/ip-ngn-ip-next-generation-network/white_paper_c11-481360.pdf
Mc Kinsey’s Forecast. http://www.mckinsey.com/insights/business_technology/disruptive_ technologies
Curino, C., Difallah, D.E., Pavlo, A., Cudre-Mauroux, P.: Benchmarking OLTP/Web databases in the cloud: the OLTP-bench framework. In: 4th International Workshop on Cloud Data Management, pp. 17–20. ACM, New York (2012)
Kossmann, D., Kraska, T., Loesing, S.: An evaluation of alternative architectures for transaction processing in the cloud. In: International Conference on Management of Data (SIGMOD), pp. 579–590. ACM, New York (2010)
TPC: TPC-W 1.8. TPC Council (2002)
Cuzzocrea, A., Kittl, C., Simos, D.E., Weippl, E., Xu, L. (eds.): CD-ARES 2013. LNCS, vol. 8127, pp. 272–288. Springer, Heidelberg (2013)
Ffmpeg Library. www.ffmpeg.org
Android. http://developer.android.com/reference/android/hardware/Camera.Parameters.html#setPreviewFpsRange
Venkata, S., Ahn, I., Jeon, D., Gupta, A., Louie, C., Garcia, S., Belongie, S., Taylor, M.: Sd-vbs: The San Diego vision benchmark suite. In: International Symposium on Workload Characterization (IISWC), pp. 55–64. IEEE, Washington, DC (2009)
Cooper, B.F., Silberstein, A., Tam, E., Ramakrishnan, R., Sears, R.: Benchmarking cloud serving systems with YCSB. In: 1st ACM Symposium on Cloud Computing (SoCC), pp. 143–154. ACM, New York (2010)
Barahmand, S, Ghandeharizadeh, S.: BG: a benchmark to evaluate interactive social networking actions. In: Sixth Biennial Conference on Innovative Data Systems Research (CIDR), Asilomar, CA, USA (2013)
Patil, S., Polte, M., Ren, K, Tantisiriroj, W., Xiao, L., López, J, Gibson, G, Fuchs, A., Rinaldi, B.: YCSB++: benchmarking and performance debugging advanced features in scalable table stores. In: 2nd ACM Symposium on Cloud Computing (SOCC). ACM, New York (2011)
Gray, J.: The Benchmarking Handbook for Database and Transactions Systems. Morgan Kaufman, San Francisco (1992)
Ballani, H., Costa, P., Karagiannis, T., Rowstron, A.: Towards predictable datacenter networks. In: 17th International Conference on Data Communications (SIGCOMM), pp. 242–253. ACM, New York (2011)
Li, A., Yang, X., Kandula, S., Zhang, M.: CloudCmp: comparing public cloud providers. In: 10th International SIGCOMM Conference on Internet Measurements, pp. 1–14. ACM, New York (2010)
The Standard Performance Evaluation Corporation (SPEC). www.specbench.org
Guthaus, M., Ringenberg, J., Ernst, D., Austin, T., Mudge, T., Brown, R.: Mibench: a free, commercially representative embedded benchmark suite. In: International Symposium on Workload Characterization, pp. 3–14
Li, M.L., Sasanka, R., Adve, S.V., Chen, Y.K., Debes, E.: The ALPBench benchmark suite for complex multimedia applications. In: International Symposium on Workload Characterization, pp. 34–45. IEEE, Washington, DC (2005)
Luo, C., Zhan, J., Jia, Z., Wang, L., Lu, G., Zhang, L., Xu, C.Z., Sun, N.: CloudRank-D: benchmarking and ranking cloud computing systems for data processing applications. J. Front. Comput. Sci. 6(4), 347–362 (2012)
Wang, L., Zhan, J., Luo, C., Zhu, Y., Yang, Q., He, Y., Gao, W., Jia, Z., Shi, Y., Zhang, S., Zheng, C., Lu, G., Zhan, K., Li, X., Qiu, B.: BigDataBenchd: a big data benchmark suite from internet services. In: 20th IEEE International Symposium on High Performance Computer Architecture, pp. 488–499, Orlando, Florida, USA (2014)
Acknowledgements
This research has been funded in part by NSF grants IIS-1115153 and IIS-1320149, the USC Integrated Media Systems Center (IMSC), and unrestricted cash gifts from Google, Northrop Grumman, Microsoft, and Oracle. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of any of the sponsors such as the National Science Foundation.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Akdogan, A., To, H., Kim, S.H., Shahabi, C. (2014). A Benchmark to Evaluate Mobile Video Upload to Cloud Infrastructures. In: Zhan, J., Han, R., Weng, C. (eds) Big Data Benchmarks, Performance Optimization, and Emerging Hardware. BPOE 2014. Lecture Notes in Computer Science(), vol 8807. Springer, Cham. https://doi.org/10.1007/978-3-319-13021-7_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-13021-7_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13020-0
Online ISBN: 978-3-319-13021-7
eBook Packages: Computer ScienceComputer Science (R0)