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ACPM: adaptive container provisioning model to mitigate serverless cold-start

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Abstract

In recent years, serverless computing has emerged as a new cloud service model that provides an elegant computing framework for application development. It allows developers to focus solely on application logic without having to worry about the underlying infrastructure or execution environment. One of the most attractive features of serverless computing is scale-to-zero, where function instances are immediately released as soon as they complete their execution. This makes the computing model more cost-effective. However, when a new function request arrives, a container must be prepared from scratch, including initialization, configuration, and loading of required libraries and packages. This requires additional startup time, also known as cold-start delay, which can degrade the performance of the computing framework. This paper proposes an integrated model, called adaptive container provisioning model (ACPM), to reduce cold-start latency through runtime provisioning of containers. ACPM promises to reduce the frequency of cold-starts and delay due to cold-start, improving the overall performance of serverless execution. It consists of two phases. In the first phase, an efficient deep learning model, such as long short term memory (LSTM), is used to predict the required number of pre-warmed containers in advance, which can be served as soon as the function request arrives. This phase is integrated with a container placement module for faster delivery of a container to minimize the frequency of cold-start delay. The second phase is based on a sandboxing mechanism where groups of similar functions can be placed in a single container instead of creating a separate container for each incoming request. ACPM drastically reduces cold-start delay and improves overall performance. The effectiveness of ACPM has been extensively compared with standard approaches such as container-sharing, container-reuse, pre-warming, restore-based, etc. From the experiment, it can be observed that ACPM outperforms other existing models in reducing cold-start latency.

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References

  1. Schleier-Smith, J., Sreekanti, V., Khandelwal, A., Carreira, J., Yadwadkar, N.J., Popa, R.A., Gonzalez, J.E., Stoica, I., Patterson, D.A.: What serverless computing is and should become: the next phase of cloud computing. Commun. ACM 64(5), 76–84 (2021)

    Article  Google Scholar 

  2. Castro, P., Ishakian, V., Muthusamy, V., Slominski, A.: Serverless programming (Function as a Service). In: 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS), 2017, pp. 2658–2659. IEEE (2017)

  3. Castro, P., Ishakian, V., Muthusamy, V., Slominski, A.: The rise of serverless computing. Commun. ACM 62(12), 44–54 (2019)

    Article  Google Scholar 

  4. Baldini, I., Castro, P., Chang, K., Cheng, P., Fink, S., Ishakian, V., Mitchell, N., Muthusamy, V., Rabbah, R., Slominski, A., et al.: Serverless computing: Current trends and open problems. In: Research Advances in Cloud Computing, pp. 1–20. Springer, Berlin (2017)

  5. Manner, J., Endreß, M., Heckel, T., Wirtz, G.: Cold start influencing factors in Function as a Service. In: 2018 IEEE/ACM International Conference on Utility and Cloud Computing Companion (UCC Companion), 2018, pp. 181–188. IEEE (2018)

  6. Kelly, D., Glavin, F., Barrett, E.: Serverless computing: behind the scenes of major platforms. In: 2020 IEEE 13th International Conference on Cloud Computing (CLOUD), 2020, pp. 304–312. IEEE (2020)

  7. Sbarski, P., Kroonenburg, S.: Serverless Architectures on AWS: With Examples Using AWS Lambda. Simon and Schuster, New York (2017)

    Google Scholar 

  8. Kim, J., Lee, K.: FunctionBench: a suite of workloads for serverless cloud function service. In: 2019 IEEE 12th International Conference on Cloud Computing (CLOUD), 2019. pp. 502–504. IEEE (2019)

  9. Sewak, M., Singh, S.: Winning in the era of serverless computing and Function as a Service. In: 2018 3rd International Conference for Convergence in Technology (I2CT), 2018, pp. 1–5. IEEE (2018)

  10. Kuntsevich, A., Nasirifard, P., Jacobsen, H.-A.: A distributed analysis and benchmarking framework for Apache OpenWhisk serverless platform. In: Proceedings of the 19th International Middleware Conference (Posters), 2018, pp. 3–4 (2018)

  11. Kumari, A., Sahoo, B., Behera, R.K., Misra, S., Sharma, M.M.: Evaluation of integrated frameworks for optimizing QoS in serverless computing. In: International Conference on Computational Science and Its Applications, 2021, pp. 277–288. Springer (2021)

  12. Shafiei, H., Khonsari, A., Mousavi, P.: Serverless computing: a survey of opportunities, challenges, and applications. ACM Comput. Surv. (CSUR) 54(11s), 1–32 (2019)

    Article  Google Scholar 

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

  14. Tian, Z., Shi, W., Wang, Y., Zhu, C., Du, X., Su, S., Sun, Y., Guizani, N.: Real-time lateral movement detection based on evidence reasoning network for edge computing environment. IEEE Trans. Ind. Inform. 15(7), 4285–4294 (2019)

    Article  Google Scholar 

  15. Agarwal, S., Rodriguez, M.A., Buyya, R.: A reinforcement learning approach to reduce serverless function cold start frequency. In: 2021 IEEE/ACM 21st International Symposium on Cluster, Cloud and Internet Computing (CCGrid), 2021, pp. 797–803. IEEE (2021)

  16. Xu, Z., Zhang, H., Geng, X., Wu, Q., Ma, H.: Adaptive function launching acceleration in serverless computing platforms. In: 2019 IEEE 25th International Conference on Parallel and Distributed Systems (ICPADS), 2019, pp. 9–16. IEEE (2019)

  17. Xie, R., Tang, Q., Qiao, S., Zhu, H., Yu, F.R., Huang, T.: When serverless computing meets edge computing: architecture, challenges, and open issues. IEEE Wirel. Commun. 28(5), 126–133 (2021)

    Article  Google Scholar 

  18. Hassan, H.B., Barakat, S.A., Sarhan, Q.I.: Survey on serverless computing. J. Cloud Comput. 10(1), 1–29 (2021)

    Article  Google Scholar 

  19. Lenarduzzi, V., Daly, J., Martini, A., Panichella, S., Tamburri, D.A.: Toward a technical debt conceptualization for serverless computing. IEEE Softw. 38(1), 40–47 (2020)

    Article  Google Scholar 

  20. Hendrickson, S., Sturdevant, S., Harter, T., Venkataramani, V., Arpaci-Dusseau, A.C., Arpaci-Dusseau, R.H.: Serverless computation with OpenLambda. In: 8th \(\{USENIX\}\) Workshop on Hot Topics in Cloud Computing (HotCloud 16), 2016 (2016)

  21. Akkus, I.E., Chen, R., Rimac, I., Stein, M., Satzke, K., Beck, A., Aditya, P., Hilt, V.: \(\{SAND\}\): towards \(\{High-Performance\}\) serverless computing. In: 2018 USENIX Annual Technical Conference (USENIX ATC 18), 2018, pp. 923–935 (2018)

  22. Oakes, E., Yang, L., Zhou, D., Houck, K., Harter, T., Arpaci-Dusseau, A., Arpaci-Dusseau, R.: \(\{SOCK\}\): rapid task provisioning with \(\{Serverless-Optimized\}\) containers. In: 2018 USENIX Annual Technical Conference (USENIX ATC 18), 2018, pp. 57–70 (2018)

  23. Silva, P., Fireman, D., Pereira, T.E.: Prebaking functions to warm the serverless cold start. In: Proceedings of the 21st International Middleware Conference, 2020, pp. 1–13 (2020)

  24. Lin, P.-M., Glikson, A.: Mitigating cold starts in serverless platforms: a pool-based approach. arXiv preprint (2019). arXiv:1903.12221

  25. Lee, S., Yoon, D., Yeo, S., Oh, S.: Mitigating cold start problem in serverless computing with function fusion. Sensors 21(24), 8416 (2021)

    Article  Google Scholar 

  26. Li, Z., Chen, Q., Guo, M.: Pagurus: eliminating cold startup in serverless computing with inter-action container sharing. arXiv preprint (2021). arXiv:2108.11240

  27. Gunasekaran, J.R., Thinakaran, P., Nachiappan, N.C., Kandemir, M.T., Das, C.R.: Fifer: tackling resource underutilization in the serverless era. In: Proceedings of the 21st International Middleware Conference, 2020, pp. 280–295 (2020)

  28. Vahidinia, P., Farahani, B., Aliee, F.S.: Mitigating cold start problem in serverless computing: a reinforcement learning approach. IEEE Internet Things J. 10(5), 3917–3927 (2022)

    Article  Google Scholar 

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

  30. Solaiman, K., Adnan, M.A.: WLEC: a not so cold architecture to mitigate cold start problem in serverless computing. In: 2020 IEEE International Conference on Cloud Engineering (IC2E), 2020, pp. 144–153. IEEE (2020)

  31. Shahrad, M., Fonseca, R., Goiri, Í., Chaudhry, G., Batum, P., Cooke, J., Laureano, E., Tresness, C., Russinovich, M., Bianchini, R.: Serverless in the wild: characterizing and optimizing the serverless workload at a large cloud provider. In: 2020 USENIX Annual Technical Conference (USENIX ATC 20), 2020, pp. 205–218 (2020)

  32. Wu, S., Tao, Z., Fan, H., Huang, Z., Zhang, X., Jin, H., Yu, C., Cao, C.: Container lifecycle-aware scheduling for serverless computing. Softw. Pract. Exp. 52(2), 337–352 (2022)

    Article  Google Scholar 

  33. Jangda, A., Pinckney, D., Brun, Y., Guha, A.: Formal foundations of serverless computing. Proc. ACM Program. Lang. 3(OOPSLA), 1–26 (2019)

    Article  Google Scholar 

  34. Pérez, A., Risco, S., Naranjo, D.M., Caballer, M., Moltó, G.: On-premises serverless computing for event-driven data processing applications. In: 2019 IEEE 12th International Conference on Cloud Computing (CLOUD), 2019 pp. 414–421. IEEE (2019)

  35. Lv, Y., Shi, W., Zhang, W., Lu, H., Tian, Z.: Don’t trust the clouds easily: the insecurity of content security policy based on object storage. IEEE Internet Things J. (2023)

  36. Tian, Z., Luo, C., Qiu, J., Du, X., Guizani, M.: A distributed deep learning system for web attack detection on edge devices. IEEE Trans. Ind. Inform. 16(3), 1963–1971 (2019)

    Article  Google Scholar 

  37. Shillaker, S., Pietzuch, P.: FaaSm: lightweight isolation for efficient stateful serverless computing. arXiv preprint (2020). arXiv:2002.09344

  38. Rajan, A.P.: A review on serverless architectures–Function as a Service (FaaS) in cloud computing. TELKOMNIKA (Telecommun. Comput. Electron. Control) 18(1), 530–537 (2020)

    Article  MathSciNet  Google Scholar 

  39. Shahrad, M., Balkind, J., Wentzlaff, D.: Architectural implications of Function-as-a-Service computing. In: Proceedings of the 52nd Annual IEEE/ACM International Symposium on Microarchitecture, 2019, pp. 1063–1075 (2019)

  40. Somu, N., Daw, N., Bellur, U., Kulkarni, P.: Panopticon: a comprehensive benchmarking tool for serverless applications. In: 2020 International Conference on COMmunication Systems & NETworkS (COMSNETS), 2020, pp. 144–151. IEEE (2020)

  41. Copik, M., Kwasniewski, G., Besta, M., Podstawski, M., Hoefler, T.: SeBS: a serverless benchmark suite for Function-as-a-Service computing. In: Proceedings of the 22nd International Middleware Conference, 2021, pp. 64–78 (2021)

  42. Maissen, P., Felber, P., Kropf, P., Schiavoni, V.: FaaSdom: a benchmark suite for serverless computing. In: Proceedings of the 14th ACM International Conference on Distributed and Event-Based Systems, 2020, pp. 73–84 (2020)

  43. Suo, K., Son, J., Cheng, D., Chen, W., Baidya, S.: Tackling cold start of serverless applications by efficient and adaptive container runtime reusing. In: 2021 IEEE International Conference on Cluster Computing (CLUSTER), 2021, pp. 433–443. IEEE (2021)

  44. Ao, L., Porter, G., Voelker, G.M.: FaaSnap: FaaS made fast using snapshot-based VMs. In: Proceedings of the Seventeenth European Conference on Computer Systems, 2022, pp. 730–746 (2022)

  45. Liu, X., Wen, J., Chen, Z., Li, D., Chen, J., Liu, Y., Wang, H., Jin, X.: FaaSLight: general application-level cold-start latency optimization for function-as-a-service in serverless computing. In: ACM Transactions on Software Engineering and Methodology, 2023 (2023)

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Acknowledgements

This research work was supported by Cloud Computing Lab, Department of Computer Science and Engineering, National Institute of Technology Rourkela, Odisha, India. The authors wish to express their gratitude and heartiest thanks to the Department of Computer Science and Engineering, NIT Rourkela, India for providing their research support.

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The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

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  1. Department of Computer Science and Engineering, National Institute of Technology, Rourkela, India

    Anisha Kumari & Bibhudatta Sahoo

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  1. Anisha Kumari
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All authors contributed to the conception and design of the proposed model. AK and BS performed material preparation, data collection, and analysis. AK wrote the first draft of the manuscript and all authors commented on previous versions. All authors read and approved the final manuscript.

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Correspondence to Anisha Kumari.

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Kumari, A., Sahoo, B. ACPM: adaptive container provisioning model to mitigate serverless cold-start. Cluster Comput 27, 1333–1360 (2024). https://doi.org/10.1007/s10586-023-04016-8

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