Abstract
While the environmental impact of cloud computing is increasingly evident, the climate crisis has become a major issue for society. For instance, data centers alone account for 2.7% of Europe’s energy consumption today. A considerable part of this load is accounted for by cloud-based services for automated software development, such as continuous integration and delivery (CI/CD) workflows.
In this paper, we discuss opportunities and challenges for greening CI/CD services by better aligning their execution with the availability of low-carbon energy. We propose a system architecture for carbon-aware CI/CD services, which uses historical runtime information and, optionally, user-provided information. Our evaluation examines the potential effectiveness of different scheduling strategies using real carbon intensity data and 7,392 workflow executions of Github Actions, a popular CI/CD service. Results show, that user-provided information on workflow deadlines can effectively improve carbon-aware scheduling.
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References
Masanet, E., Shehabi, A., Lei, N., Smith, S., Koomey, J.: Recalibrating global data center energy-use estimates. Science 367(6481), 984–986 (2020)
Montevecchi, F., Stickler, T., Hintemann, R., Hinterholzer, S.: Energy-efficient Cloud Computing Technologies and Policies for an Eco-friendly Cloud Market. Final Study Report. Publications Office of the European Union, LU (2020)
Meyer, M.: Continuous integration and its tools. IEEE Softw. 31(3), 14–16 (2014)
CD Foundation. State of Continuous Delivery Report: The Evolution of Software Delivery Performance (2022)
Ibrahim, M., et al.: An in-depth empirical investigation of state-of-the-art scheduling approaches for cloud computing. IEEE Access 8, 128282–128294 (2020)
World Bank. State and trends of carbon pricing 2022. Technical report. World Bank, Washington, DC (2022)
Wiesner, P., Behnke, I., Scheinert, D., Gontarska, K., Thamsen, L.: Let’s wait awhile: how temporal workload shifting can reduce carbon emissions in the cloud. ACM Middleware (2021)
Radovanovic, A., et al.: Carbon-aware computing for datacenters. IEEE Trans. Power Syst. (2022)
Fridgen, G., Körner, M.-F., Walters, S., Weibelzahl, M.: Not all doom and gloom: how energy-intensive and temporally flexible data center applications may actually promote renewable energy sources. Bus. Inf. Syst. Eng. 63(3) (2021)
Zheng, J., Chien, A.A., Suh, S.: Mitigating curtailment and carbon emissions through load migration between data centers. Joule 4(10) (2020)
Zhou, Z., et al.: Carbon-aware load balancing for geo-distributed cloud services. In: International Symposium on Modelling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS) (2013)
Moghaddam, F., Farrahi Moghaddam, R., Cheriet, M.: Carbon-aware distributed cloud: multi-level grouping genetic algorithm. Cluster Comput. 18, 477–491 (2015)
Hanafy, W.A., Liang, Q., Bashir, N., Irwin, D., Shenoy, P.: CarbonScaler: leveraging cloud workload elasticity for optimizing carbon-efficiency. In: ACM SIGMETRICS/IFIP Performance (2024)
Lin, L., Zavala, V.M., Chien, A.: Evaluating coupling models for cloud datacenters and power grids. ACM e-Energy (2021)
Goiri, I., et al.: Matching renewable energy supply and demand in green datacenters. Ad Hoc Netw. 25, 520–534 (2015)
Wiesner, P., Scheinert, D., Wittkopp, T., Thamsen, L., Kao, O.: Cucumber: renewable-aware admission control for delay-tolerant cloud and edge workloads. In: Cano, J., Trinder, P. (eds.) Euro-Par 2022. LNCS, vol. 13440, pp. 218–232. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-12597-3_14
Abdalkareem, R., Mujahid, S., Shihab, E., Rilling, J.: Which commits can be CI skipped? IEEE Trans. Softw. Eng. 47(3), 448–463 (2021)
Ficher, M., Berthoud, F., Ligozat, A.-L., Sigonneau, P., Wisslé, M., Tebbani, B.: Assessing the carbon footprint of the data transmission on a backbone network. In: Conference on Innovation in Clouds, Internet and Networks (ICIN) (2021)
Cirne, W., Berman, F.: A comprehensive model of the supercomputer workload. In: 4th IEEE International Workshop on Workload Characterization (2001)
Tsafrir, D., Etsion, Y., Feitelson, D.G.: Backfilling using system-generated predictions rather than user runtime estimates. IEEE Trans. Parallel Distrib. Syst. 18(6), 789–803 (2007)
Tang, W., Lan, Z., Desai, N., Buettner, D.: Fault-aware, utility-based job scheduling on Blue, Gene/P systems. In: IEEE CLUSTER (2009)
Ward, W.A., Mahood, C.L., West, J.E.: Scheduling jobs on parallel systems using a relaxed backfill strategy. In: Feitelson, D.G., Rudolph, L., Schwiegelshohn, U. (eds.) JSSPP 2002. LNCS, vol. 2537, pp. 88–102. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-36180-4_6
Wiesner, P., Behnke, I., Kao, O.: A testbed for carbon-aware applications and systems. arXiv:2306.09774 [cs.DC] (2023)
Acknowledgments
We sincerely thank WattTime for providing us with access to their marginal carbon intensity data. This research was supported by the German Ministry for Education and Research (BMBF) as Software Campus (grant 01IS17050).
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Claßen, H., Thierfeldt, J., Tochman-Szewc, J., Wiesner, P., Kao, O. (2024). Carbon-Awareness in CI/CD. In: Monti, F., et al. Service-Oriented Computing – ICSOC 2023 Workshops. ICSOC 2023. Lecture Notes in Computer Science, vol 14518. Springer, Singapore. https://doi.org/10.1007/978-981-97-0989-2_17
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DOI: https://doi.org/10.1007/978-981-97-0989-2_17
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