Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

Shipping code towards data in an inter-region serverless environment to leverage latency

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

Serverless computing emerges as a new standard to build cloud applications, where developers write compact functions that respond to events in the cloud infrastructure. Several cloud service industries started adopting serverless for deploying their applications. But one key limitation in serverless computing is that it disregards the significance of data. In the age of big data, when applications run around a huge volume, to transfer data from the data side to the computation side to co-allocate the data and code, leads to high latency. All existing serverless architectures are based on the data shipping architecture. In this paper, we present an inter-region code shipping architecture for serverless, that enables the code to flow from computation side to the data side where the size of the code is negligible compared to the data size. We tested our proposed architecture over a real-time cloud platform Amazon Web Services with the integration of the Fission serverless tool. The evaluation of the proposed code shipping architecture shows for a data file size of 64 MB, the latency in the proposed code shipping architecture is 8.36 ms and in existing data shipped architecture is found to be 16.8 ms. Hence, the proposed architecture achieves a speedup of 2x on the round latency for high data sizes in a serverless environment. We define round latency to be the duration to read and write back the data in the storage.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

Data availability

The datasets used or analyzed during the current study are available from the corresponding author on reasonable request.

Notes

  1. https://cloud.google.com/functions/.

  2. https://azure.microsoft.com/en-in/solutions/serverless/.

  3. https://www.ibm.com/in-en/cloud/functions.

  4. https://aws.amazon.com/lambda/.

  5. https://fission.io/.

References

  1. Lloyd W, Vu M, Zhang B, David O, Leavesley G (2018) Improving application migration to serverless computing platforms: latency mitigation with keep-alive workloads. In: 2018 IEEE/ACM International Conference on Utility and Cloud Computing Companion (UCC Companion), pp 195–200, IEEE, 2018

  2. Kjorveziroski V, Filiposka S (2022) Kubernetes distributions for the edge: serverless performance evaluation. J Supercomput, pp 1–28

  3. McGrath G, Brenner PR (2017) Serverless computing: Design, implementation, and performance. In: 2017 IEEE 37th International Conference on Distributed Computing Systems Workshops (ICDCSW), pp 405–410, IEEE

  4. Joseph JMF, Hellerstein M, Gonzalez J, Smith JS, Sreekanti V , Tumanov A, Wu C (2019) Serverless computing: One step forward, two steps back. In: 9th Biennial Conference on Innovative Data Systems Research, CIDR 2019, Asilomar, CA, USA, 13–16 Jan 2019, Online Proceedings, www.cidrdb.org

  5. Yu T, Liu Q, Du D, Xia Y, Zang B, Lu Z, Yang P, Qin C, Chen H (2020) Characterizing serverless platforms with serverlessbench. In: Proceedings of the 11th ACM Symposium on Cloud Computing, pp 30–44

  6. Aditya P, Akkus IE, Beck A, Chen R, Hilt V, Rimac I, Satzke K, Stein M (2019) Will serverless computing revolutionize nfv? Proc IEEE 107(4):667–678

    Article  Google Scholar 

  7. Science Advances The polar regions in a 2\(^\circ \)c warmer world (2021). https://advances.sciencemag.org/content/5/12/eaaw9883, 2019. Last accessed 11 Dec 2021

  8. ISRO Article - Files (2021). https://www.isro.gov.in/sites/default/files/article-files/capacity-building/supported-areas-of-research/, 2019. Last accessed 15 Mar 2021

  9. AWS Lambda-Pricing AWS (2020). https://aws.amazon.com/lambda/pricing/ Last accessed 26 Oct 2020

  10. (2020) Hadoop HDFS Architecture Guide (2020). https://hadoop.apache.org/docs/r1.2.1/hdfs_design.html. Last accessed 22 Sept 2020

  11. Tanenbum, Computer Network (2011) International series of monographs on physics, 5th edn. Clarendon Press

  12. Bas, E(2019) An introduction to queueing models. In: Basics of probability and stochastic processes, pp 233–252, Springer, Berlin

  13. Zheng G, Peng Y (2019) Globalflow: a cross-region orchestration service for serverless computing services. In 2019 IEEE 12th International Conference on Cloud Computing (CLOUD), pp 508–510

  14. Bardsley D, Ryan L, Howard J (2018) Serverless performance and optimization strategies. In 2018 IEEE International Conference on Smart Cloud (SmartCloud), pp 19–26, IEEE

  15. Sreekanti V, Subbaraj H, Wu C, Gonzalez JE, Hellerstein JM (2020) Optimizing prediction serving on low-latency serverless dataflow. arXiv preprint arXiv:2007.05832

  16. Wang L, Li M, Zhang Y, Ristenpart T, Swift M (2018) Peeking behind the curtains of serverless platforms. In: 2018 \(\{\)USENIX\(\}\) Annual Technical Conference (\(\{\)USENIX\(\}\)\(\{\)ATC\(\}\) 18), pp 133–146

  17. Gadepalli PK, Peach G, Cherkasova L, Aitken R, Parmer G, (2019) Challenges and opportunities for efficient serverless computing at the edge. In: 2019 38th Symposium on Reliable Distributed Systems (SRDS), pp 261–2615, IEEE

  18. Ghosh BC, Addya SK, Somy NB, Nath SB, Chakraborty S, Ghosh SK (2020) Caching techniques to improve latency in serverless architectures. In 2020 International Conference on Communication Systems and Networks (COMSNETS), pp 666–669, IEEE

  19. Shillaker S (2018) A provider-friendly serverless framework for latency-critical applications. In :12th Eurosys Doctoral Workshop, p 71

  20. Singhvi A, Houck K, Balasubramanian A, Shaikh MD, Venkataraman S, Akella A, (2019) Archipelago: a scalable low-latency serverless platform. arXiv preprint arXiv:1911.09849

  21. Hall A, Ramachandran U (2019) An execution model for serverless functions at the edge. In Proceedings of the International Conference on Internet of Things Design and Implementation, pp 225–236

  22. Li J, Kulkarni SG, Ramakrishnan K, Li D (2019) Understanding open source serverless platforms: Design considerations and performance. In: Proceedings of the 5th International Workshop on Serverless Computing, pp 37–42

  23. Lin C, Khazaei H (2020) Modeling and optimization of performance and cost of serverless applications. IEEE Trans Parall Distrib Syst 32(3):615–632

    Article  Google Scholar 

  24. Adzic G, Chatley R (2017) Serverless computing: economic and architectural impact. In: Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering, pp 884–889

  25. Eivy A, Weinman J (2017) Be wary of the economics of “serverless" cloud computing. IEEE Cloud Comput. 4(2):6–12

    Article  Google Scholar 

  26. Rahman MM, Hasibul Hasan M (2019) Serverless architecture for big data analytics. In: 2019 Global Conference for Advancement in Technology (GCAT), pp 1–5

  27. Hwang S, Choi H, Yu H (2019) Implementation of low-latency message delivery for serverless based workflow. In: 2019 IEEE 16th International Conference on Mobile Ad Hoc and Sensor Systems Workshops (MASSW), pp 170–171

  28. Werner S, Girke R, Kuhlenkamp J (2020) An evaluation of serverless data processing frameworks. In: Proceedings of the 2020 Sixth International Workshop on Serverless Computing, WoSC’20, (New York, NY, USA), pp 19–24, Association for Computing Machinery

  29. Skluzacek TJ, Chard R, Wong R, Li Z, Babuji YN, Ward L, Blaiszik B, Chard K, Foster I (2019) Serverless workflows for indexing large scientific data. In: Proceedings of the 5th International Workshop on Serverless Computing, WOSC ’19, (New York, NY, USA), pp 43–48, Association for Computing Machinery

  30. Mahgoub A, Shankar K, Mitra S, Klimovic A, Chaterji S, Bagchi S(2021) \(\{\)SONIC\(\}\): Application-aware data passing for chained serverless applications. In: 2021 \(\{\)USENIX\(\}\) Annual Technical Conference (\(\{\)USENIX\(\}\)\(\{\)ATC\(\}\) 21), pp 285–301

  31. Lu X, Kashyap A (2021) Towards offloadable and migratable microservices on disaggregated architectures: Vision, challenges, and research roadmap. arXiv preprint arXiv:2104.11272

  32. Vu T, Mediran C J, Peng Y (2019) Measurement and observation of cross-provider cross-region latency for cloud-based iot systems. In: 2019 IEEE World Congress on Services (SERVICES) 2642:364–365 (IEEE)

  33. Kuhlenkamp J, Werner S, Borges MC, El Tal K, Tai S (2019) An evaluation of faas platforms as a foundation for serverless big data processing. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing, pp 1–9

  34. Barcelona-Pons D, Sánchez-Artigas M, París G, Sutra P, García-López P (2019) On the faas track: building stateful distributed applications with serverless architectures. In Proceedings of the 20th International Middleware Conference, pp 41–54

  35. Rausch T, Rashed A, Dustdar S (2021) Optimized container scheduling for data-intensive serverless edge computing. Future Generation Comput Syst 114:259–271

    Article  Google Scholar 

  36. Mondal SK, Pan R, Kabir H, Tian T, Dai H-N (2022) Kubernetes in it administration and serverless computing: an empirical study and research challenges. J Supercomput 78(2):2937–2987

    Article  Google Scholar 

  37. Banaei A, Sharifi M (2022) Etas: predictive scheduling of functions on worker nodes of apache openwhisk platform. J Supercomput 78(4):5358–5393

    Article  Google Scholar 

  38. Hussein MK, Mousa MH, Alqarni MA (2019) A placement architecture for a container as a service (caas) in a cloud environment. J Cloud Comput 8:1–15

    Article  Google Scholar 

  39. Mouradian C, Yangui S, Glitho RH (2018) Robots as-a-service in cloud computing: Search and rescue in large-scale disasters case study. In: 2018 15th IEEE Annual Consumer Communications and Networking Conference (CCNC), pp 1–7, IEEE

  40. Kapitonov A, Lonshakov S, Bulatov V, Kia B, White J(2021) Robot-as-a-service: from cloud to peering technologies. In: Proceedings of the 4th International Conference on Information Science and Systems, pp 126–131

  41. Zhang T, Xie D, Li F, Stutsman R (2019) Narrowing the gap between serverless and its state with storage functions. Association for Computing Machinery, New York, NY, USA

    Book  Google Scholar 

  42. Mahmud MS, Huang JZ, Salloum S, Emara TZ, Sadatdiynov K (2020) A survey of data partitioning and sampling methods to support big data analysis. Big Data Min Anal 3(2):85–101

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, 721302, India

    Biswajeet Sethi, Jay Bhutada & Soumya K. Ghosh

  2. Department of Computer Science and Engineering, National Institute of Technology Karnataka, Surathkal, 575025, India

    Sourav Kanti Addya

Authors
  1. Biswajeet Sethi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sourav Kanti Addya
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jay Bhutada
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Soumya K. Ghosh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Biswajeet Sethi.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sethi, B., Addya, S.K., Bhutada, J. et al. Shipping code towards data in an inter-region serverless environment to leverage latency. J Supercomput 79, 11585–11610 (2023). https://doi.org/10.1007/s11227-023-05104-7

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-023-05104-7

Keywords

Search

Navigation