Muhammad Izzudin Farhans

Studi Literatur Efisiensi Biaya dan Performa AWS Lambda VS Google Cloud Functions di Skala Nasional

Introduction

Studi literatur efisiensi biaya dan performa aws lambda vs google cloud functions di skala nasional. Bandingkan biaya & performa AWS Lambda vs Google Cloud Functions untuk aplikasi serverless skala nasional. AWS Lambda unggul komputasi intensif, GCF hemat biaya beban ringan. Panduan developer di Indonesia.

Abstract

Serverless computing telah menjadi salah satu kejadian penting dalam era aplikasi modern, karena penyederhanaan manajemen infrastruktur menjadi tantangan utama bagi pengembang. Dalam studi ini, kami membandingkan biaya dan performa dari dua platform serverless yang paling popular yaitu AWS Lambda dan Google Cloud Functions dalam konteks aplikasi skala nasional. Untuk melakukan penelitian ini, kami menggunakan pendekatan studi literatur berdasarkan 15 jurnal yang terdaftar di basis data SINTA dan Internasional, serta uji coba eksperimental dengan skenario eksekusi fungsi yang sama di kedua platform. Hasil eksperimen menunjukkan bahwa solusi serverless AWS Lambda menawarkan performa eksekusi yang lebih baik untuk tugas komputasi intensif, sedangkan Google Cloud Functions lebih hemat biaya untuk beban ringan hingga sedang. Beberapa perbedaan signifikan juga kami temukan dalam model penetapan harga dan latensi layanan cloud, serta kemudahan integrasi dengan layanan lainnya. Temuan kami mungkin berguna bagi pengembang dan pengambil keputusan dalam implementasi aplikasi layanan publik di Indonesia.

Review

The paper, "Studi Literatur Efisiensi Biaya dan Performa AWS Lambda VS Google Cloud Functions di Skala Nasional," addresses a highly relevant and timely topic in modern application development: the comparative analysis of leading serverless platforms. By focusing on AWS Lambda and Google Cloud Functions, the study tackles a critical decision point for developers and organizations adopting serverless architectures. The chosen methodology, combining a literature review of 15 journals from SINTA and international databases with an experimental testbed using identical function execution scenarios, provides a robust dual-pronged approach to investigating both cost efficiency and performance. This holistic perspective, particularly with its stated aim to inform public service application development in Indonesia, positions the research as practically significant and contextually grounded. The findings present clear differentiations between the two platforms, indicating that AWS Lambda delivers superior execution performance for computationally intensive tasks, while Google Cloud Functions proves more cost-effective for light to moderate workloads. Beyond raw performance and cost, the authors also identify crucial distinctions in pricing models, service latency, and integration capabilities with other cloud services. These insights are invaluable for stakeholders, offering actionable guidance for platform selection based on specific application requirements and budget constraints. The explicit focus on public service applications in Indonesia further enhances the paper's contribution, providing tailored recommendations that could directly influence infrastructure decisions within a critical national context. While the abstract highlights a strong methodological approach, a full paper would benefit from a more detailed exposition of the experimental design. Specifically, clarifying how "national scale" demands were simulated or represented within the experimental setup would strengthen the applicability of the findings. Further elucidation on the specific criteria used to define "compute-intensive" versus "light to moderate" workloads, along with details of the functions executed, would enhance reproducibility and generalizability. Additionally, expanding on the synthesis method for the literature review – how the 15 journals were chosen, and how their findings were integrated with the experimental results – would provide greater transparency. Nevertheless, this study offers a commendable initial comparison, laying a solid foundation for further, more granular investigations into serverless platform optimization within specific regional or national contexts.

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