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27. Cho, J., Tootaghaj, D.Z., Cao, L., Sharma, P.: SLA-Driven ML Inference Framework for Clouds With Heterogeneous Accelerators. p. 13 (2022)

28. Cloud, G.: Cloud tpu vms are generally available (2022). URL https://cloud.google.com/blog/products/compute/cloud-tpu-vms-are-generally-available

29. Cloud Native Computing Foundation: New SlashData report: 5.6 million developers use Kubernetes, an increase of 67% over one year (2021). URL https://www.cncf.io/blog/2021/12/20/new-slashdata-report-5-6-million-developers-use-kubernetes-an-increase-of-67-over-one-year/

30. Cloud Native Computing Foundation: Kubernetes (2022). URL https://kubernetes.io/

31. Cloud Native Computing Foundation: Kubevirt (2022). URL http://kubevirt.io/

32. Cloudflare: Announcing D1: our first SQL database (2022). URL https://blog.cloudflare.com/introducing-d1/

33. D., J.: Baby's First AWS Deployment (2022). URL https://blog.verygoodsoftwarenotvirus.ru/posts/babys-first-aws/

34. Dartois, J.E., B. Ribeiro, H., Boukhobza, J., Barais, O.: Cuckoo: Opportunistic MapReduce on Ephemeral and Heterogeneous Cloud Resources. In: 2019 IEEE 12th International Conference on Cloud Computing (CLOUD), pp. 396–403. IEEE, Milan, Italy (2019). DOI 10.1109/CLOUD.2019.00070. URL https://doi.org/10.1109/CLOUD.2019.00070

35. Diamantopoulos, D., Polig, R., Ringlein, B., Purandare, M., Weiss, B., Hagleitner, C., Lantz, M., Abel, F.: Acceleration-as-a-µService: A Cloud-native Monte-Carlo Option Pricing Engine on CPUs, GPUs and Disaggregated FPGAs. In: 2021 IEEE 14th International Conference on Cloud Computing (CLOUD), pp. 726–729. IEEE, Chicago, IL, USA (2021). DOI 10.1109/CLOUD53861.2021.00096. URL https://doi.org/10.1109/CLOUD53861.2021.00096

36. Docker Inc.: Docker (2022). URL https://www.docker.com/

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38. Du, D., Yu, T., Xia, Y., Zang, B., Yan, G., Qin, C., Wu, Q., Chen, H.: Catalyzer: Sub-millisecond Startup for Serverless Computing with Initialization-less Booting. In: Proceedings of the Twenty-Fifth International Conference on Architectural Support for Programming Languages and Operating Systems, pp. 467–481. ACM, Lausanne Switzerland (2020). DOI 10.1145/3373376.3378512. URL https://doi.org/10.1145/3373376.3378512

39. Elsakhawy, M., Bauer, M.: FaaS2F: A Framework for Defining Execution-SLA in Serverless Computing. In: 2020 IEEE Cloud Summit, pp. 58–65. IEEE, Harrisburg, PA, USA (2020). DOI 10.1109/IEEECloudSummit48914.2020.00015. URL https://doi.org/10.1109/IEEECloudSummit48914.2020.00015

40. Fehling, C., Leymann, F., Retter, R., Schupeck, W., Arbitter, P.: Cloud Computing Patterns. Springer Vienna, Vienna (2014). DOI 10.1007/978-3-7091-1568-8. URL https://doi.org/10.1007/978-3-7091-1568-8

41. Foundation, C.N.C.: Cloud native computing foundation (2022). URL https://www.cncf.io/

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26. Chikhaoui, A., Lemarchand, L., Boukhalfa, K., Boukhobza, J.: Multi-objective Optimization of Data Placement in a Storage-as-a-Service Federated Cloud. ACM Transactions on Storage 17(3), 1–32 (2021). DOI https://doi.org/10.1145/3452741. URL https://doi.org/10.1145/3452741

27. Cho, J., Tootaghaj, D.Z., Cao, L., Sharma, P.: SLA-Driven ML Inference Framework for Clouds With Heterogeneous Accelerators. p. 13 (2022)

28. Cloud, G.: Cloud tpu vms are generally available (2022). URL https://cloud.google.com/blog/products/compute/cloud-tpu-vms-are-generally-available

29. Cloud Native Computing Foundation: New SlashData report: 5.6 million developers use Kubernetes, an increase of 67% over one year (2021). URL https://www.cncf.io/blog/2021/12/20/new-slashdata-report-5-6-million-developers-use-kubernetes-an-increase-of-67-over-one-year/

30. Cloud Native Computing Foundation: Kubernetes (2022). URL https://kubernetes.io/

31. Cloud Native Computing Foundation: Kubevirt (2022). URL http://kubevirt.io/

32. Cloudflare: Announcing D1: our first SQL database (2022). URL https://blog.cloudflare.com/introducing-d1/

33. D., J.: Baby's First AWS Deployment (2022). URL https://blog.verygoodsoftwarenotvirus.ru/posts/babys-first-aws/

34. Dartois, J.E., B. Ribeiro, H., Boukhobza, J., Barais, O.: Cuckoo: Opportunistic MapReduce on Ephemeral and Heterogeneous Cloud Resources. In: 2019 IEEE 12th International Conference on Cloud Computing (CLOUD), pp. 396–403. IEEE, Milan, Italy (2019). DOI 10.1109/CLOUD.2019.00070. URL https://doi.org/10.1109/CLOUD.2019.00070

35. Diamantopoulos, D., Polig, R., Ringlein, B., Purandare, M., Weiss, B., Hagleitner, C., Lantz, M., Abel, F.: Acceleration-as-a-µService: A Cloud-native Monte-Carlo Option Pricing Engine on CPUs, GPUs and Disaggregated FPGAs. In: 2021 IEEE 14th International Conference on Cloud Computing (CLOUD), pp. 726–729. IEEE, Chicago, IL, USA (2021). DOI 10.1109/CLOUD53861.2021.00096. URL https://doi.org/10.1109/CLOUD53861.2021.00096

36. Docker Inc.: Docker (2022). URL https://www.docker.com/

37. Dragoni, N., Lanese, I., Larsen, S.T., Mazzara, M., Mustafin, R., Safina, L.: Microservices: How To Make Your Application Scale. In: A.K. Petrenko, A. Voronkov (eds.) Perspectives of System Informatics, vol. 10742, pp. 95–104. Springer International Publishing, Cham (2018). DOI 10.1007/978-3-319-74313-4-8. URL https://doi.org/10.1007/978-3-319-74313-4-8

38. Du, D., Yu, T., Xia, Y., Zang, B., Yan, G., Qin, C., Wu, Q., Chen, H.: Catalyzer: Sub-millisecond Startup for Serverless Computing with Initialization-less Booting. In: Proceedings of the Twenty-Fifth International Conference on Architectural Support for Programming Languages and Operating Systems, pp. 467–481. ACM, Lausanne Switzerland (2020). DOI 10.1145/3373376.3378512. URL https://doi.org/10.1145/3373376.3378512

39. Elsakhawy, M., Bauer, M.: FaaS2F: A Framework for Defining Execution-SLA in Serverless Computing. In: 2020 IEEE Cloud Summit, pp. 58–65. IEEE, Harrisburg, PA, USA (2020). DOI 10.1109/IEEECloudSummit48914.2020.00015. URL https://doi.org/10.1109/IEEECloudSummit48914.2020.00015

40. Fehling, C., Leymann, F., Retter, R., Schupeck, W., Arbitter, P.: Cloud Computing Patterns. Springer Vienna, Vienna (2014). DOI 10.1007/978-3-7091-1568-8. URL https://doi.org/10.1007/978-3-7091-1568-8

41. Foundation, C.N.C.: Cloud native computing foundation (2022). URL https://www.cncf.io/

42. Foundation, C.N.C.: Knative (2022). URL https://knative.dev/

43. Fowler, M., Lewis, J.: Microservices (2014). URL http://martinfowler.com/articles/microservices.html

44. Fuerst, A., Sharma, P.: FaasCache: Keeping Serverless Computing Alive with Greedy-Dual Caching. In: Proceedings of the 26th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, pp. 386–400. ACM, Virtual USA (2021). DOI 10.1145/3445814.3446757. URL https://doi.org/10.1145/3445814.3446757
45. Gill, S.S., Xu, M., Ottaviani, C., Patros, P., Bahsoon, R., Shaghaghi, A., Golec, M., Stankovski, V., Wu, H., Abraham, A., Singh, M., Mehta, H., Ghosh, S.K., Baker, T., Parlikad, A.K., Lutfiyya, H., Kanhere, S.S., Sakellariou, R., Dustdar, S., Rana, O., Brandic, I., Uhlig, S.: AI for next generation computing: Emerging trends and future directions. Internet of Things 19, 100514 (2022). DOI 10.1016/j.iot.2022.100514. URL https://doi.org/10.1016/j.iot.2022.100514

46. Golec, M., Ozturac, R., Pooranian, Z., Gill, S.S., Buyya, R.: iFaaS Bus: A Security- and Privacy-Based Lightweight Framework for Serverless Computing Using IoT and Machine Learning. IEEE Transactions on Industrial Informatics 18(5), 3522–3529 (2022). DOI 10.1109/TII.2021.3095466. URL https://doi.org/10.1109/TII.2021.3095466

47. Google: Google Cloud Functions (2022). URL https://cloud.google.com/functions/

48. Google: Google Workflows (2022). URL https://cloud.google.com/workflows/

49. Google: gVisor (2022). URL https://gvisor.dev/

50. Greenberger, M., of Management, S.S., of Technology. School of Industrial Management, M.I.: Management and the Computer of the Future, pp. 220–248. Published jointly by M.I.T. Press and Wiley, New York (1962). URL https://archive.org/details/managementcomput00gree/page/220/

51. Gujarati, A., Elnikety, S., He, Y., McKinley, K.S., Brandenburg, B.B.: Swayam: Distributed autoscaling to meet SLAs of machine learning inference services with resource efficiency. In: Proceedings of the 18th ACM/IFIP/USENIX Middleware Conference, pp. 109–120. ACM, Las Vegas Nevada (2017). DOI 10.1145/3135974.3135993. URL https://doi.org/10.1145/3135974.3135993

52. Handaoui, M., Dartois, J.E., Boukhobza, J., Barais, O., d'Orazio, L.: ReLeaSER: A Reinforce-ment Learning Strategy for Optimizing Utilization Of Ephemeral Cloud Resources. In: 2020 IEEE International Conference on Cloud Computing Technology and Science (CloudCom), pp. 65–73. IEEE, Bangkok, Thailand (2020). DOI 10.1109/CloudCom49646.2020.00009. URL https://doi.org/10.1109/CloudCom49646.2020.00009

53. Hassan, H.B., Barakat, S.A., Sarhan, Q.I.: Survey on Serverless Computing. Journal of Cloud Computing 10(1), 39 (2021). DOI 10.1186/s13677-021-00253-7. URL https://doi.org/10.1186/s13677-021-00253-7

54. Hellerstein, J.M., Faleiro, J.M., Gonzalez, J., Schleier-Smith, J., Sreekanti, V., Tumanov, A., Wu, C.: Serverless Computing: One Step Forward, Two Steps Back. In: 9th Biennial Conference on Innovative Data Systems Research, CIDR 2019, Asilomar, CA, USA, January 13-16, 2019, Online Proceedings. www.cidrdb.org (2019). URL http://cidrdb.org/cidr2019/papers/p119-hellerstein-cidr19.pdf

55. Holori: Holori GCP Pricing Calculator (2022). URL https://holori.com/gcp-pricing-calculator/

56. Horta, E., Chuang, H.R., VSathish, N.R., Philippidis, C., Barbalace, A., Olivier, P., Ravindran, B.: Xar-Trek: Run-Time Execution Migration among FPGAs and Heterogeneous-ISA CPUs. In: Proceedings of the 22nd International Middleware Conference, pp. 104–118. ACM, Québec city Canada (2021). DOI 10.1145/3464298.3493388. URL https://doi.org/10.1145/3464298.3493388

57. IBM: IBM Cloud Functions (2022). URL https://cloud.ibm.com/functions/

58. Ionescu, V.: Scaling containers on AWS in 2022 (2022). URL https://www.vladionescu.me/posts/scaling-containers-on-aws-in-2022/

59. Izraelevitz, J., Yang, J., Zhang, L., Kim, J., Liu, X., Memaripour, A., Soh, Y.J., Wang, Z., Xu, Y., Dulloor, S.R., Zhao, J., Swanson, S.: Basic Performance Measurements of the Intel Optane DC Persistent Memory Module. ArXiv abs/1903.05714 (2019). URL https://dblp.uni-trier.de/rec/journals/corr/abs-1903-05714.html

60. Jia, Z., Witchel, E.: Nightcore: Efficient and Scalable Serverless Computing for Latency-Sensitive, Interactive Microservices. In: Proceedings of the 26th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS '21, p. 152–166. Association for Computing Machinery, New York, NY, USA (2021). DOI 10.1145/3445814.3446701. URL https://doi.org/10.1145/3445814.3446701
45. Gill, S.S., Xu, M., Ottaviani, C., Patros, P., Bahsoon, R., Shaghaghi, A., Golec, M., Stankovski, V., Wu, H., Abraham, A., Singh, M., Mehta, H., Ghosh, S.K., Baker, T., Parlikad, A.K., Lutfiyya, H., Kanhere, S.S., Sakellariou, R., Dustdar, S., Rana, O., Brandic, I., Uhlig, S.: AI for next generation computing: Emerging trends and future directions. Internet of Things 19, 100514 (2022). DOI 10.1016/j.iot.2022.100514. URL https://doi.org/10.1016/j.iot.2022.100514

46. Golec, M., Ozturac, R., Pooranian, Z., Gill, S.S., Buyya, R.: iFaaS Bus: A Security- and Privacy-Based Lightweight Framework for Serverless Computing Using IoT and Machine Learning. IEEE Transactions on Industrial Informatics 18(5), 3522–3529 (2022). DOI 10.1109/TII.2021.3095466. URL https://doi.org/10.1109/TII.2021.3095466

47. Google: Google Cloud Functions (2022). URL https://cloud.google.com/functions/

48. Google: Google Workflows (2022). URL https://cloud.google.com/workflows/

49. Google: gVisor (2022). URL https://gvisor.dev/

50. Greenberger, M., of Management, S.S., of Technology. School of Industrial Management, M.I.: Management and the Computer of the Future, pp. 220–248. Published jointly by M.I.T. Press and Wiley, New York (1962). URL https://archive.org/details/managementcomput00gree/page/220/

51. Gujarati, A., Elnikety, S., He, Y., McKinley, K.S., Brandenburg, B.B.: Swayam: Distributed autoscaling to meet SLAs of machine learning inference services with resource efficiency. In: Proceedings of the 18th ACM/IFIP/USENIX Middleware Conference, pp. 109–120. ACM, Las Vegas Nevada (2017). DOI 10.1145/3135974.3135993. URL https://doi.org/10.1145/3135974.3135993

52. Handaoui, M., Dartois, J.E., Boukhobza, J., Barais, O., d'Orazio, L.: ReLeaSER: A Reinforce-ment Learning Strategy for Optimizing Utilization Of Ephemeral Cloud Resources. In: 2020 IEEE International Conference on Cloud Computing Technology and Science (CloudCom), pp. 65–73. IEEE, Bangkok, Thailand (2020). DOI 10.1109/CloudCom49646.2020.00009. URL https://doi.org/10.1109/CloudCom49646.2020.00009

53. Hassan, H.B., Barakat, S.A., Sarhan, Q.I.: Survey on Serverless Computing. Journal of Cloud Computing 10(1), 39 (2021). DOI 10.1186/s13677-021-00253-7. URL https://doi.org/10.1186/s13677-021-00253-7

54. Hellerstein, J.M., Faleiro, J.M., Gonzalez, J., Schleier-Smith, J., Sreekanti, V., Tumanov, A., Wu, C.: Serverless Computing: One Step Forward, Two Steps Back. In: 9th Biennial Conference on Innovative Data Systems Research, CIDR 2019, Asilomar, CA, USA, January 13-16, 2019, Online Proceedings. www.cidrdb.org (2019). URL http://cidrdb.org/cidr2019/papers/p119-hellerstein-cidr19.pdf

55. Holori: Holori GCP Pricing Calculator (2022). URL https://holori.com/gcp-pricing-calculator/

56. Horta, E., Chuang, H.R., VSathish, N.R., Philippidis, C., Barbalace, A., Olivier, P., Ravindran, B.: Xar-Trek: Run-Time Execution Migration among FPGAs and Heterogeneous-ISA CPUs. In: Proceedings of the 22nd International Middleware Conference, pp. 104–118. ACM, Québec city Canada (2021). DOI 10.1145/3464298.3493388. URL https://doi.org/10.1145/3464298.3493388

57. IBM: IBM Cloud Functions (2022). URL https://cloud.ibm.com/functions/

58. Ionescu, V.: Scaling containers on AWS in 2022 (2022). URL https://www.vladionescu.me/posts/scaling-containers-on-aws-in-2022/

59. Izraelevitz, J., Yang, J., Zhang, L., Kim, J., Liu, X., Memaripour, A., Soh, Y.J., Wang, Z., Xu, Y., Dulloor, S.R., Zhao, J., Swanson, S.: Basic Performance Measurements of the Intel Optane DC Persistent Memory Module. ArXiv abs/1903.05714 (2019). URL https://dblp.uni-trier.de/rec/journals/corr/abs-1903-05714.html

60. Jia, Z., Witchel, E.: Nightcore: Efficient and Scalable Serverless Computing for Latency-Sensitive, Interactive Microservices. In: Proceedings of the 26th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS '21, p. 152–166. Association for Computing Machinery, New York, NY, USA (2021). DOI 10.1145/3445814.3446701. URL https://doi.org/10.1145/3445814.3446701
61. Jiang, J., Gan, S., Liu, Y., Wang, F., Alonso, G., Klimovic, A., Singla, A., Wu, W., Zhang, C.: Towards Demystifying Serverless Machine Learning Training. Proceedings of the 2021 International Conference on Management of Data (2021). DOI 10.1145/3448016.3459240. URL https://doi.org/10.1145/3448016.3459240

62. Jonas, E., Schleier-Smith, J., Sreekanti, V., Tsai, C., Khandelwal, A., Pu, Q., Shankar, V., Carreira, J., Krauth, K., Yadwadkar, N.J., Gonzalez, J.E., Popa, R.A., Stoica, I., Patterson, D.A.: Cloud Programming Simplified: A Berkeley View on Serverless Computing. CoRR abs/1902.03383 (2019). URL http://arxiv.org/abs/1902.03383

63. Khandelwal, A., Kejariwal, A., Ramasamy, K.: Le Taureau: Deconstructing the Serverless Landscape & A Look Forward. In: Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data, pp. 2641–2650. ACM, Portland OR USA (2020). DOI 10.1145/3318464.3383130. URL https://doi.org/10.1145/3318464.3383130

64. Kivity, A., Kamay, Y., Laor, D.: Kvm: The Linux Virtual Machine Monitor. In: In Proceedings of the 2007 Ottawa Linux Symposium (OLS’07, p. 8 (2007). URL https://www.kernel.org/doc/ols/2007/ols2007v1-pages-225-230.pdf

65. Klimovic, A., wen Wang, Y., Stuedi, P., Trivedi, A.K., Pfefferle, J., Kozyrakis, C.E.: Pocket: Elastic Ephemeral Storage for Serverless Analytics. login Usenix Mag. 44 (2018). DOI 10.5555/3291168.3291200. URL https://www.usenix.org/conference/osdi18/presentation/klimovic

66. Koomey, J.G.: Growth in data center electricity use 2005 to 2010. Analytics Press for the New York Times (2011). URL https://www.koomey.com/post/8323374335

67. Kuenzer, S., Bădoiu, V.A., Lefeuvre, H., Santhanam, S., Jung, A., Gain, G., Soldani, C., Lupu, C., Teodorescu, Ş., Răducanu, C., Banu, C., Mathy, L., Deaconescu, R., Raiciu, C., Huici, F.: Unikraft: Fast, Specialized Unikernels the Easy Way. In: Proceedings of the Sixteenth European Conference on Computer Systems, pp. 376–394. ACM, Online Event United Kingdom (2021). DOI 10.1145/3447786.3456248. URL https://doi.org/10.1145/3447786.3456248

68. Leite, L., Rocha, C., Kon, F., Milojicic, D., Meirelles, P.: A survey of devops concepts and challenges. ACM Comput. Surv. 52(6) (2019). DOI 10.1145/3359981. URL https://doi.org/10.1145/3359981

69. Linux Foundation Projects: Open Container Initiative (2022). URL https://opencontainers.org/

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73. Lloyd, W., Vu, M., Zhang, B., David, O., Leavesley, G.: 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, Zurich (2018). DOI 10.1109/UCC-Companion.2018.00056. URL https://doi.org/10.1109/UCC-Companion.2018.00056

74. Lopes, R.V., Menasce, D.: A Taxonomy of Job Scheduling on Distributed Computing Systems. IEEE Transactions on Parallel and Distributed Systems 27(12), 3412–3428 (2016). DOI 10.1109/TPDS.2016.2537821. URL https://doi.org/10.1109/TPDS.2016.2537821

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61. Jiang, J., Gan, S., Liu, Y., Wang, F., Alonso, G., Klimovic, A., Singla, A., Wu, W., Zhang, C.: Towards Demystifying Serverless Machine Learning Training. Proceedings of the 2021 International Conference on Management of Data (2021). DOI 10.1145/3448016.3459240. URL https://doi.org/10.1145/3448016.3459240

62. Jonas, E., Schleier-Smith, J., Sreekanti, V., Tsai, C., Khandelwal, A., Pu, Q., Shankar, V., Carreira, J., Krauth, K., Yadwadkar, N.J., Gonzalez, J.E., Popa, R.A., Stoica, I., Patterson, D.A.: Cloud Programming Simplified: A Berkeley View on Serverless Computing. CoRR abs/1902.03383 (2019). URL http://arxiv.org/abs/1902.03383

63. Khandelwal, A., Kejariwal, A., Ramasamy, K.: Le Taureau: Deconstructing the Serverless Landscape & A Look Forward. In: Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data, pp. 2641–2650. ACM, Portland OR USA (2020). DOI 10.1145/3318464.3383130. URL https://doi.org/10.1145/3318464.3383130

64. Kivity, A., Kamay, Y., Laor, D.: Kvm: The Linux Virtual Machine Monitor. In: In Proceedings of the 2007 Ottawa Linux Symposium (OLS’07, p. 8 (2007). URL https://www.kernel.org/doc/ols/2007/ols2007v1-pages-225-230.pdf

65. Klimovic, A., wen Wang, Y., Stuedi, P., Trivedi, A.K., Pfefferle, J., Kozyrakis, C.E.: Pocket: Elastic Ephemeral Storage for Serverless Analytics. login Usenix Mag. 44 (2018). DOI 10.5555/3291168.3291200. URL https://www.usenix.org/conference/osdi18/presentation/klimovic

66. Koomey, J.G.: Growth in data center electricity use 2005 to 2010. Analytics Press for the New York Times (2011). URL https://www.koomey.com/post/8323374335

67. Kuenzer, S., Bădoiu, V.A., Lefeuvre, H., Santhanam, S., Jung, A., Gain, G., Soldani, C., Lupu, C., Teodorescu, Ş., Răducanu, C., Banu, C., Mathy, L., Deaconescu, R., Raiciu, C., Huici, F.: Unikraft: Fast, Specialized Unikernels the Easy Way. In: Proceedings of the Sixteenth European Conference on Computer Systems, pp. 376–394. ACM, On-line Event United Kingdom (2021). DOI 10.1145/3447786.3456248. URL https://doi.org/10.1145/3447786.3456248

68. Leite, L., Rocha, C., Kon, F., Milojicic, D., Meirelles, P.: A survey of devops concepts and challenges. ACM Comput. Surv. 52(6) (2019). DOI 10.1145/3359981. URL https://doi.org/10.1145/3359981

69. Linux Foundation Projects: Open Container Initiative (2022). URL https://opencontainers.org/

70. Linux Foundation Projects: Xen Project (2022). URL https://xenproject.org/

71. Linux Kernel: KVM (2022). URL https://www.linux-kvm.org/page/Main_Page

72. Liu, N., Li, Z., Xu, J., Xu, Z., Lin, S., Qiu, Q., Tang, J., Wang, Y.: A Hierarchical Framework of Cloud Resource Allocation and Power Management Using Deep Reinforcement Learning. In: 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS), pp. 372–382. IEEE, Atlanta, GA, USA (2017). DOI 10.1109/ICDCS.2017.123. URL https://doi.org/10.1109/ICDCS.2017.123

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