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Bibliografia

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Bibliografia

Ostatnia aktualizacja: 2026-05-26

Pełna lista referencji dla projektu badawczego. Publikacje z [PDF] posiadają pełne podsumowanie w bazie wiedzy.

A. Fundamenty — Energia i Języki Programowania

[1] Pereira, R., Couto, M., Ribeiro, F., Rua, R., Cunha, J., Fernandes, J. P., & Saraiva, J. (2017). Energy efficiency across programming languages: how do energy, time, and memory relate? In Proceedings of the 10th ACM SIGPLAN International Conference on Software Language Engineering (SLE 2017). DOI: 10.1145/3136014.3136031 → publications/references/pereira-energy-efficiency-languages-2017/ Pierwsza systematyczna analiza 27 języków przez RAPL + CLBG. JavaScript (Node.js): 4.45× więcej energii niż C.

[2] Pereira, R., Couto, M., Ribeiro, F., Rua, R., Cunha, J., Fernandes, J. P., & Saraiva, J. (2021). Ranking programming languages by energy efficiency. Science of Computer Programming, vol. 205, art. 102609. Elsevier. DOI: 10.1016/j.scico.2021.102609 → publications/references/pereira-ranking-languages-energy-2021/ Rozszerzenie [1]: walidacja na Rosetta Code, analiza TOPSIS, narzędzie multi-criteria. JavaScript: 15. miejsce na 27.

[3] Cunha, S., Silva, L., Saraiva, J., & Fernandes, J. P. (2024). Trading Runtime for Energy Efficiency: Leveraging Power Caps to Save Energy across Programming Languages. In Proceedings of the 17th ACM SIGPLAN International Conference on Software Language Engineering (SLE 2024), pp. 130–142. DOI: 10.1145/3687997.3695638 → publications/references/cunha-power-caps-energy-languages-2024/ Power cap (RAPL PKGP) jako mechanizm optymalizacji — sweet spot 70-80% dla JS. Kontynuacja linii Pereira.

B. Narzędzia Pomiarowe

[4] Noureddine, A. (2022). [PDF] PowerJoular and JoularJX: Multi-Platform Software Power Monitoring Tools. 18th International Conference on Intelligent Environments (IE 2022), Biarritz, France. IEEE. DOI: 10.1109/IE54923.2022.9826760 | HAL: hal-03608223v1 → publications/with-pdf/noureddine-powerjoular-2022/ PowerJoular: CLI (Ada), per-process monitoring przez RAPL (x86) + polynomial model (ARM/RPi). Kluczowe narzędzie dla JE-3.

C. WebAssembly vs JavaScript — Energia i Wydajność

[5] De Macedo, J., Abreu, R., Pereira, R., & Saraiva, J. (2022). WebAssembly versus JavaScript: Energy and Runtime Performance. 2022 International Conference on ICT for Sustainability (ICT4S), IEEE, pp. 24–34. DOI: 10.1109/ICT4S55073.2022.00014 → publications/references/de-macedo-wasm-vs-js-energy-2022/ Pierwsza systematyczna analiza Wasm vs JS energetycznie. Wasm lepszy o ~20-30% dla CPU-bound. Tylko Node.js (V8).

D. Node.js / Deno / Bun — Porównania

[6] Smirnov, A. A., Podolskiy, E. A., Cherenkov, A. V., & Gosudarev, I. B. (2024). A comparative analysis of the performance of JavaScript code execution environments: Node.js, Deno and Bun. Программные системы и вычислительные методы (Programming Systems and Computational Methods), nr 4, pp. 109–123. DOI: 10.7256/2454-0714.2024.4.72206 → publications/references/smirnov-nodejs-deno-bun-comparison-2024/ Jedyne akademickie porównanie Node.js/Deno/Bun — tylko czas, bez energii. Gap badawczy JE-1.

[6b] Laakso, J. (2025). [PDF] The Next Generation of Server-Side JavaScript Runtimes: Node.js, Deno and Bun. Bachelor’s Thesis UAS, Turku University of Applied Sciences, Business Information Technology. 43 pp. URL: http://www.theseus.fi/bitstream/10024/905982/2/Laakso_Juuso.pdf → publications/with-pdf/laakso-js-runtimes-2025/ Grey literature (praca licencjacka). Benchmark data: Bun 103k req/s vs Node.js 73k vs Deno 73k (native); JavaScriptCore vs V8. Bez energii = gap JE-1. State of JS 2024: Node 90.8%, Bun 16.4%, Deno 11.8%.

E. WebAssembly w Edge i Serverless

[7] Marcelino, C., Copik, M., Calotoiu, A., Nastic, S., Schulz, M., & Dustdar, S. (2025). [PDF] Lumos: Performance Characterization of WebAssembly as a Serverless Runtime in the Edge-Cloud Continuum. arXiv: 2504.04570v1 → publications/with-pdf/marcelino-lumos-wasm-serverless-edge-2025/ Lumos: model wydajnościowy Wasm (Wasmtime, WasmEdge, Wasmedge AOT) na Knative. Wasm interpretowany 30-55× wolniejszy od AOT.

[8] Besozzi, M., Baresi, L., & Quattrocchi, G. (2025). [PDF] WebAssembly and Unikernels: A Comparative Study for Serverless Computing at the Edge. arXiv: 2503.08948v1 → publications/with-pdf/besozzi-wasm-unikernels-serverless-edge-2025/ Wasm cold start: 5.6ms vs Firecracker 93.5ms. Unikernels (Unikraft) jako trzecia opcja.

[9] Colosi, M., Farahani, R., Lovén, L., Prodan, R., & Villari, M. (2025). [PDF] Serverless Everywhere: A Comparative Analysis of WebAssembly Workflows Across Browser, Edge, and Cloud. arXiv: 2512.04089v1 → publications/with-pdf/colosi-serverless-everywhere-wasm-2025/ 5-krokowy DAG Rust/Wasm na browser/edge/cloud. AOT redukuje cold start o rząd wielkości. Browser lepszy dla małych payloadów.

[10] Marcelino, C., & Nastic, S. (2025). [PDF] CWASI: A WebAssembly Runtime Shim for Inter-function Communication in the Serverless Edge-Cloud Continuum. ACM/IEEE Symposium on Edge Computing (SEC ‘23), December 2023, Wilmington, DE. DOI: 10.1145/3583740.3626611 | arXiv: 2504.21503v1 → publications/with-pdf/marcelino-cwasi-wasm-shim-2025/ CWASI: trójtrybowy IPC dla co-located Wasm (embedding, UDS, Redis). 95% redukcja latencji vs WasmEdge.

F. Carbon-Aware Serverless

[11] Chadha, M., John, A., & Gerndt, M. (2023). [PDF] GreenCourier: Carbon-Aware Scheduling for Serverless Functions. ACM/IFIP Middleware 2023, Bologna, Italy. DOI: 10.1145/3631295.3631396 → publications/with-pdf/chadha-greencourier-carbon-serverless-2023/ Carbon-aware scheduling na OpenWhisk: carbon intensity regionów → 12% CO₂ redukcja bez degradacji latencji.

G. Cold Start i Serverless Performance

[12] Golec, M., Walia, G. K., Kumar, M., Cuadrado, F., Gill, S. S., & Uhlig, S. (2024). [PDF] Cold Start Latency in Serverless Computing: A Systematic Review, Taxonomy, and Future Directions. ACM Computing Surveys, Vol. 37, No. 4, Article 111, August 2024. DOI: 10.1145/nnnnnnn.nnnnnnn | arXiv: 2310.08437v2 → publications/with-pdf/golec-cold-start-review-2024/ Pierwsza SLR cold start. Interpretowane (JS/Node.js): krótszy cold start niż Java/.NET. Gap: brak pomiaru energii cold start.

[13] Li, J., Kulkarni, S. G., Ramakrishnan, K. K., & Li, D. (2019). [PDF] Understanding Open Source Serverless Platforms: Design Considerations and Performance. Fifth International Workshop on Serverless Computing (WOSC ‘19), Davis, CA. ACM. DOI: 10.1145/3366623.3368139 | arXiv: 1911.07449 → publications/with-pdf/li-open-source-serverless-platforms-2019/ Porównanie Knative, Kubeless, Nuclio, OpenFaaS. Throughput różni się do 10×. Kontekst dla wyboru platformy benchmarkowej.

[14] Carl, S., Schambach, M., Steinmetz, J., & Jansen, A. (2026). [PDF] Serverless Abstractions for Short-Running, Lightweight Streams. → publications/with-pdf/carl-serverless-streams-2026/ Streaming workloads w serverless — wzorce long-running vs short-running streams.

[15] Kiener, M., Chadha, M., & Gerndt, M. (2021). [PDF] Towards Demystifying Intra-Function Parallelism in Serverless Computing. Workshop on Serverless Computing (WoSC ‘21), Québec, Canada. ACM. arXiv: 2110.12090 → publications/with-pdf/kiener-intra-function-parallelism-2021/ vCPU ≠ fizyczny rdzeń. Parallelizacja oszczędza 81% kosztów (AWS Lambda). Node.js wymaga worker_threads.

H. Metodologia Eksperymentalna — Wzorce

[16] Albonico, M., Cannizza, M. B., & Wortmann, A. (2025). Energy efficiency in ROS communication: a comparison across programming languages and workloads. Frontiers in Robotics and AI, vol. 12. DOI: 10.3389/frobt.2025.1548250 → publications/references/albonico-energy-ros-languages-2025/ C++ vs Python energia w ROS 2. Wzorzec eksperymentalny: język × częstotliwość × klienci. Python stale więcej energii.

I. Indeks cytowań i powiązań

IDAutorzyRokCytowaniaProjekt powiązany
[1]Pereira et al.2017~188JE-1, JE-3 (metodologia RAPL)
[2]Pereira et al.2021~172JE-1, JE-2 (multi-criteria ranking)
[3]Cunha et al.2024~4JE-3 (power cap)
[4]Noureddine2022~44JE-3 (narzędzie pomiarowe)
[5]De Macedo et al.2022~22JE-4 (Wasm vs JS energia)
[6]Smirnov et al.2024n/aJE-1 (gap: brak energii)
[7]Marcelino et al.2025n/aJE-4 (Wasm edge wydajność)
[8]Besozzi et al.2025n/aJE-4 (cold start Wasm)
[9]Colosi et al.2025n/aJE-3 (metodologia benchmark)
[10]Marcelino & Nastic2025n/aJE-4 (IPC Wasm, energia IPC)
[11]Chadha et al.2023n/aJE-5, JE-7 (carbon-aware)
[12]Golec et al.2024n/aJE-6 (cold start survey)
[13]Li et al.2019n/aJE-1, JE-3 (platformy serverless)
[14]Carl et al.2026n/aJE-8 (streaming workloads)
[15]Kiener et al.2021n/aJE-3 (vCPU, parallelism)
[16]Albonico et al.2025~2JE-1 (wzorzec metodologiczny)

J. Format cytowań (ACM/IEEE)

Dla pracy doktorskiej preferowany format IEEE:

[1] R. Pereira, M. Couto, F. Ribeiro, R. Rua, J. Cunha, J. P. Fernandes, and J. Saraiva,
    "Energy efficiency across programming languages: how do energy, time, and memory relate?"
    in Proc. 10th ACM SIGPLAN Int. Conf. Software Language Engineering (SLE), 2017.
    DOI: 10.1145/3136014.3136031

[2] R. Pereira et al., "Ranking programming languages by energy efficiency,"
    Science of Computer Programming, vol. 205, p. 102609, 2021.
    DOI: 10.1016/j.scico.2021.102609

[3] S. Cunha, L. Silva, J. Saraiva, and J. P. Fernandes,
    "Trading Runtime for Energy Efficiency: Leveraging Power Caps to Save Energy across Programming Languages,"
    in Proc. 17th ACM SIGPLAN Int. Conf. Software Language Engineering (SLE), 2024, pp. 130–142.
    DOI: 10.1145/3687997.3695638

[4] A. Noureddine, "PowerJoular and JoularJX: Multi-Platform Software Power Monitoring Tools,"
    in Proc. 18th Int. Conf. Intelligent Environments (IE 2022), 2022.
    DOI: 10.1109/IE54923.2022.9826760

[5] J. De Macedo, R. Abreu, R. Pereira, and J. Saraiva,
    "WebAssembly versus JavaScript: Energy and Runtime Performance,"
    in Proc. Int. Conf. ICT for Sustainability (ICT4S), 2022, pp. 24–34.
    DOI: 10.1109/ICT4S55073.2022.00014

[6] A. A. Smirnov, E. A. Podolskiy, A. V. Cherenkov, and I. B. Gosudarev,
    "A comparative analysis of the performance of JavaScript code execution environments: Node.js, Deno and Bun,"
    Programming Systems and Computational Methods, no. 4, pp. 109–123, 2024.
    DOI: 10.7256/2454-0714.2024.4.72206

[7] C. Marcelino et al., "Lumos: Performance Characterization of WebAssembly as a Serverless Runtime
    in the Edge-Cloud Continuum," arXiv:2504.04570, 2025.

[8] M. Besozzi, L. Baresi, and G. Quattrocchi,
    "WebAssembly and Unikernels: A Comparative Study for Serverless Computing at the Edge,"
    arXiv:2503.08948, 2025.

[9] M. Colosi, R. Farahani, L. Lovén, R. Prodan, and M. Villari,
    "Serverless Everywhere: A Comparative Analysis of WebAssembly Workflows Across Browser, Edge, and Cloud,"
    arXiv:2512.04089, 2025.

[10] C. Marcelino and S. Nastic, "CWASI: A WebAssembly Runtime Shim for Inter-function Communication
     in the Serverless Edge-Cloud Continuum,"
     in Proc. 8th ACM/IEEE Symp. Edge Computing (SEC '23), 2023.
     DOI: 10.1145/3583740.3626611

[11] M. Chadha, A. John, and M. Gerndt, "GreenCourier: Carbon-Aware Scheduling for Serverless Functions,"
     in Proc. ACM/IFIP Middleware, 2023.
     DOI: 10.1145/3631295.3631396

[12] M. Golec et al., "Cold Start Latency in Serverless Computing: A Systematic Review, Taxonomy,
     and Future Directions," ACM Computing Surveys, vol. 37, no. 4, art. 111, Aug. 2024.

[13] J. Li, S. G. Kulkarni, K. K. Ramakrishnan, and D. Li,
     "Understanding Open Source Serverless Platforms: Design Considerations and Performance,"
     in Proc. 5th Int. Workshop Serverless Computing (WOSC '19), 2019.
     DOI: 10.1145/3366623.3368139

[14] S. Carl, M. Schambach, J. Steinmetz, and A. Jansen,
     "Serverless Abstractions for Short-Running, Lightweight Streams," 2026.

[15] M. Kiener, M. Chadha, and M. Gerndt,
     "Towards Demystifying Intra-Function Parallelism in Serverless Computing,"
     in Proc. Workshop Serverless Computing (WoSC '21), 2021.
     arXiv:2110.12090

[16] M. Albonico, M. B. Cannizza, and A. Wortmann,
     "Energy efficiency in ROS communication: a comparison across programming languages and workloads,"
     Frontiers in Robotics and AI, vol. 12, 2025.
     DOI: 10.3389/frobt.2025.1548250

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