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Sequence Clock: A Dynamic Resource Orchestrator for Serverless Architectures

Authors: Fakinos, Ioannis; Tzenetopoulos, Achilleas; Masouros, Dimosthenis; Xydis, Sotirios; Soudris, Dimitrios;

Sequence Clock: A Dynamic Resource Orchestrator for Serverless Architectures

Abstract

Function-as-a-service (FaaS) represents the next frontier in the evolution of cloud computing being an emerging paradigm that removes the burden of configuration and management issues from users. This is achieved by replacing the well-established monolithic approach with graphs of standalone, small, stateless, event-driven components called functions. At the same time, from the cloud providers’ perspective, problems such as availability, load balancing and scalability need to be resolved without being aware of the functionality, behavior or resource requirements of their tenants’ code. However, in this context, functions’ containers coexist with others inside a host of finite resources, where a passive resource allocation technique does not guarantee a well-defined quality of service (QoS) in regards to time latency. In this paper, we present Sequence Clock, an expandable latency targeting tool that actively monitors serverless invocations in a cluster and offers execution of a sequential chain of functions, also known as pipelines or sequences, while achieving the targeted time latency. Two regulation methods were utilized, with one of them achieving up to 82% decrease in the severity of time violations and in some cases even eliminating them completely.

Keywords

serverless computing, FaaS, OpenWhisk, QoS, Kubernetes

19 references, page 1 of 2

[1] E. Jonas, J. Schleier-Smith, V. Sreekanti, C.-C. Tsai, A. Khandelwal, Q. Pu, V. Shankar, J. Carreira, K. Krauth, N. Yadwadkar et al., “Cloud programming simplified: A berkeley view on serverless computing,” arXiv preprint arXiv:1902.03383, 2019.

[2] “Aws lambda,” Website. [Online]. Available: https://aws.amazon.com/lambda/

[3] “Google cloud functions,” Website. [Online]. Available: https://cloud.google.com/functions

[4] “Serverless architecture market,” Website. [Online]. Available: shorturl.at/gGMU5

[5] L. Wang, M. Li, Y. Zhang, T. Ristenpart, and M. Swift, “Peeking behind the curtains of serverless platforms,” Boston, MA, pp. 133-146, Jul. 2018. [Online]. Available: https://www.usenix.org/conference/atc18/presentation/wang-liang

[6] D. Masouros, S. Xydis, and D. Soudris, “Rusty: Runtime interferenceaware predictive monitoring for modern multi-tenant systems,” IEEE Transactions on Parallel and Distributed Systems, vol. 32, no. 1, pp. 184-198, 2020.

[7] T. Yu, Q. Liu, D. Du, Y. Xia, B. Zang, Z. Lu, P. Yang, C. Qin, and H. Chen, “Characterizing serverless platforms with serverlessbench,” in Proceedings of the 11th ACM Symposium on Cloud Computing, 2020, pp. 30-44.

[8] A. Tzenetopoulos, E. Apostolakis, A. Tzomaka, C. Papakostopoulos, K. Stavrakakis, M. Katsaragakis, I. Oroutzoglou, D. Masouros, S. Xydis, and D. Soudris, “Faas and curious: Performance implications of serverless functions on edge computing platforms,” in International Conference on High Performance Computing. Springer, 2021, pp. 428- 438. [OpenAIRE]

[9] F. Romero, M. Zhao, N. J. Yadwadkar, and C. Kozyrakis, “Llama: A heterogeneous & serverless framework for auto-tuning video analytics pipelines,” CoRR, vol. abs/2102.01887, 2021. [Online]. Available: https://arxiv.org/abs/2102.01887

[10] S. Kotni, A. Nayak, V. Ganapathy, and A. Basu, “Faastlane: Accelerating function-as-a-service workflows,” pp. 805-820, Jul. 2021. [Online]. Available: https://www.usenix.org/conference/atc21/presentation/kotni

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