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IEEE INTERNET computing 2024年第6期28卷 5-7页

作者： Ibrahim, Shadi Rana, Omer Beaumont, Olivier Chu, Xiaowen Rennes Univ Inria Ctr F-35042 Rennes France Cardiff Univ Sch Comp Sci & Informat Cardiff CF24 4AG Wales Univ Bordeaux Inria Ctr F-33405 Talence France Hong Kong Univ Sci & Technol Guangzhou Guangzhou 511453 Peoples R China

This special issue covers emerging research and development challenges in serverless computing, ranging from the development of an allocation and scheduling simulator to evaluate various orchestration policies, to support for scalable and cost-effective deployment of serverless machine learning models in heterogeneous edge environments. The articles also demonstrate how the serverless computing paradigm has been deployed across different system architectures, from edge-only deployments to those that make use of a combination of edge and cloud environments. There is clear enthusiasm in the systems community in utilizing this paradigm for a wide range of applications.

关键词： Special Issues And Sections serverless computing Research And Development Cost Benefit Analysis Edge computing Cloud computing Systems Architecture Processor Scheduling Machine Learning Computational Modeling

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serverless computing and DevOps: A Synergistic Approach to Modern Software Development 1st

Serverless Computing and DevOps: A Synergistic Approach to M...

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2024 International Conference on Computational Intelligence and Generative AI

作者： Puppala, Ramya Goutham, Potharlanka Rohan, Shaik Abdul Sainadh, Jaladi Teja Krishna David, Thalathoti Jason Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Guntur Andhra Pradesh India

ISBN: (纸本)9783031766121;9783031766107;9783031766091

This discussion explores the evolution of serverless computing, highlighting its origins, rise in importance, and the challenges it presents. serverless computing represents a significant shift from traditional, hardware-dependent environments to cloud-based, code-centric architectures. The concept of serverless emerged with the introduction of platforms like Google App Engine and AWS Lambda, freeing developers from server management concerns and enabling them to focus solely on code development. The integration of serverless computing with DevOps practices is examined, emphasizing the role of Infrastructure as Code (IaC), version control, continuous integration (CI), and continuous deployment (CD). These practices are essential for streamlining the development and deployment of serverless applications, enhancing consistency, and promoting collaboration between development and operations teams. Furthermore, the discussion addresses the specific challenge of cold starts in serverless computing and how DevOps practices can help mitigate their impact. Strategies such as proactive monitoring, scaling policies, scheduled warm-up, provisioned concurrency, performance testing, automation, and continuous optimization are presented as solutions to minimize cold start delays. Lastly, the importance of Testing Automation within the CI/CD pipeline is highlighted. Through rapid feedback, comprehensive testing, consistency, regression testing, and scalability, Testing Automation ensures the quality and reliability of software in a fast-paced development environment, aligning with the principles of DevOps.

关键词： serverless computing Function-as-a-service Infrastructure as code Continuous integration Continuous delivery Continuous deployment Cold starts Limited state management

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Unveiling overlooked performance variance in serverless computing

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EMPIRICAL SOFTWARE ENGINEERING 2025年第2期30卷 1-26页

作者： Wen, Jinfeng Chen, Zhenpeng Sarro, Federica Wang, Shangguang Beijing Univ Posts & Telecommun Beijing Peoples R China Beiyou Shenzhen Inst Beijing Peoples R China Nanyang Technol Univ Singapore Singapore UCL London England

serverless computing is an emerging cloud computing paradigm for developing applications at the function level, known as serverless functions. Due to the highly dynamic execution environment, multiple identical runs of the same serverless function can yield different performance, specifically in terms of end-to-end response latency. However, surprisingly, our analysis of serverless computing-related papers published in top-tier conferences highlights that the research community lacks awareness of the performance variance problem, with only 38.38% of these papers employing multiple runs for quantifying it. To further investigate, we analyze the performance of 72 serverless functions collected from these papers. Our findings reveal that the performance of these serverless functions can differ by up to 338.76% (44.28% on average) across different runs. Moreover, 61.11% of these functions produce unreliable performance results, with a low number of repetitions commonly employed in the serverless computing literature. Our study highlights a lack of awareness in the serverless computing community regarding the well-known performance variance problem in software engineering. The empirical results illustrate the substantial magnitude of this variance, emphasizing that ignoring the variance can affect research reproducibility and result reliability.

关键词： serverless computing Performance variance Empirical study Cloud computing

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Mitigating cold start problem in serverless computing using predictive pre-warming with machine learning

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computing 2025年第1期107卷 1-24页

作者： Hu, Qingmiao Li, Hongwei Nikougoftar, Elaheh Beijing Normal Univ Fac art & Sci Zhuhai 519000 Peoples R China Taali Inst Higher Educ Dept Comp & Elect Qom Iran

The cold start problem in serverless computing leads to increased latency when functions are invoked after being idle. This paper proposes a predictive pre-warming strategy that leverages machine learning and historical data analysis to mitigate the cold start problem. By using a Recurrent Neural Network (RNN) to predict future invocations and a pre-warming scheduler to determine the number of instances to pre-warm, our approach aims to optimize resource utilization and reduce latency. Results show that the proposed method adapts idle-container times efficiently, reducing cold starts and idle periods. The proposed method outperforms OpenWhisk by executing more invocations, demonstrating a 49.52% improvement and enhancing container resource allocation optimization.

关键词： serverless computing Cold start Machine learning Predictive pre-warming Recurrent neural network

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Enhancing Cybersecurity Through Fusion of Optimization With Deep Wavelet Neural Networks on Denial of Wallet Attack Detection in serverless computing

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IEEE ACCESS 2025年 13卷 47111-47122页

作者： Renukadevi, P. Amaran, Sibi Vikram, A. Prabhakara Rao, T. Ishak, Mohamad Khairi SRM Inst Sci & Technol Dept Comp Technol Kattankulathur 603203 India SRM Inst Sci & Technol Coll Engn & Technol Fac Engn & Technol Sch CompDept Comp Technol Kattankulathur India Aditya Univ Dept Comp Sci & Engn Surampalem Andhra Pradesh India Ajman Univ Coll Engn & Informat Technol Dept Elect & Comp Engn Ajman U Arab Emirates

A Denial of Wallet (DoW) attack is a cyberattack designed to drain financial resources by causing excessive charges or costs on a serverless computing or cloud platform. This attack is mainly associated with serverless frameworks due to features like auto-scaling, cost amplification, pay-as-you-go, and limited control. serverless computing, or Function-as-a-Service (FaaS), is a cloud computing (CC) model that enables developers to build and run applications without managing traditional server infrastructure. Deep learning (DL) models show strong potential in detecting DoW attacks, where attackers disrupt services by exploiting system resources. By utilizing advanced neural network architectures, DL methods can identify complex patterns in network traffic, offering enhanced accuracy and resilience against such attacks. This study presents a Fusion of Optimization with Deep Wavelet Neural Networks on Denial of Wallet Attack Detection (FODWNN-DoWAD) approach in serverless computing. The main intention of the FODWNN-DoWAD approach is to enhance cybersecurity in DoW attack detection. The FODWNN-DoWAD method utilizes min-max normalization to scale the input network data into a beneficial format. Besides, the pair barracuda swarm optimization (PBSO) method is employed to select features optimally. To recognize a DoW attack, the FODWNN-DoWAD method utilizes a deep wavelet neural network (DWNN) model. Finally, the hyperparameter tuning of the DWNN classifier is performed using the hierarchical learning-based chaotic crayfish optimizer (HLCCO) model. Wide-ranging experiments are accomplished on a benchmark database to demonstrate the good classification outcome of the FODWNN-DoWAD model. The performance validation of the FODWNN-DoWAD approach portrayed a superior accuracy value of 99.05% over existing methods.

关键词： serverless computing Computational modeling Neural networks Accuracy Optimization Costs Smart contracts Computer architecture Blockchains Tuning Denial of wallet deep wavelet neural network cybersecurity feature selection serverless computing

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Cold Start Latency in serverless computing: A Systematic Review, Taxonomy, and Future Directions

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ACM computing SURVEYS 2025年第3期57卷 1-36页

作者： Golec, Muhammed Walia, Guneet kaur Kumar, Mohit Cuadrado, Felix Gill, Sukhpal singh Uhlig, Steve Queen Mary Univ London Sch Elect Engn & Comp Sci London England Abdullah Gul Univ Kayseri Turkiye Dr BR Ambedkar Natl Inst Technol Dept Informat Technol Jalandhar India Univ Politecn Madrid Higher Tech Sch Comp Syst Engn Madrid Comunidad De Ma Spain

Recently, academics and the corporate sector have paid attention to serverless computing, which enables dynamic scalability and an economic model. In serverless computing, users only pay for the time they actually use resources, enabling zero scaling to optimise cost and resource utilisation. However, this approach also introduces the serverless cold start problem. Researchers have developed various solutions to address the cold start problem, yet it remains an unresolved research area. In this article, we propose a systematic literature review on cold start latency in serverless computing. Furthermore, we create a detailed taxonomy of approaches to cold start latency, which we use to investigate existing techniques for reducing the cold start time and frequency. We have classified the current studies on cold start latency into several categories such as caching and application-level optimisation-based solutions, as well as Artificial Intelligence/Machine Learning-based solutions. Moreover, we have analyzed the impact of cold start latency on quality of service, explored current cold start latency mitigation methods, datasets, and implementation platforms, and classified them into categories based on their common characteristics and features. Finally, we outline the open challenges and highlight the possible future directions.

关键词： serverless computing cold start latency function as a service survey cloud computing

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Qora: Neural-Enhanced Interference-Aware Resource Provisioning for serverless computing

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 10609-10624页

作者： Ma, Ruifeng Zhan, Yufeng Wu, Chuge Hong, Zicong Ali, Yasir Xia, Yuanqing Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China Beijing Inst Technol Zhengzhou Res Inst Zhengzhou 450000 Peoples R China Zhongyuan Univ Technol Zhengzhou 450007 Peoples R China

serverless is an emerging cloud paradigm that offers fine-grained resource sharing through serverless functions. However, this resource sharing can cause interference, leading to performance degradation and QoS violations. Existing white box-based approaches for serverless resource provision often demand extensive expert knowledge, which is challenging to obtain due to the complexity of interference sources. This paper proposes Qora, a neural-enhanced interference-aware resource provisioning system for serverless computing. We model the resource provisioning of serverless functions as a novel combinatorial optimization problem, wherein the constraints on the queries per second are derived from neural network performance model. By leveraging neural networks to model the nonlinear performance fluctuations under various interference sources, our approach better captures the real-world behavior of serverless functions. To solve the formulated problem efficiently, rather than adopting commercial optimizer solvers like Gurobi, we propose a two-stage-VNS algorithm that searches discrete variables more efficiently and supports Sigmoid activations, avoiding introducing redundant discrete variables. Unlike pure machine learning methods lacking theoretical optimal guarantees, our approach is rigorously proven globally optimal based on optimization theory. We implement Qora on Kubernetes as a serverless system automating resource provisioning. Experimental results demonstrate that Qora reduces the QoS violation rate by 98% while reducing up to 35% resource costs compared with the state-of-the-arts. Note to Practitioners-From the perspective of cloud service providers, this paper considers the automatic resource provisioning for serverless functions. To improve hardware utilization, cloud providers tend to co-locate serverless functions on the same server. However, co-located functions compete for shared resources (memory bandwidth, L3 cache, etc.), which causes interference and

关键词： Interference Neural networks Optimization Computational modeling serverless computing Quality of service Bandwidth Costs Linux Electronic mail performance interference resource provisioning

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A multi-agent deep reinforcement learning approach for optimal resource management in serverless computing

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CLUSTER computing-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2025年第2期28卷 1-18页

作者： Singh, Ashutosh Kumar Kumar, Satender Jain, Sarika Natl Inst Technol Kurukshetra Dept Comp Applicat Kurukshetra 136119 Haryana India Indian Inst Informat & Technol Bhopal 462003 Madhya Pradesh India

The flexibility of serverless resources enables cloud service providers to scale up dynamically and down the resource requirements over time. However, the varying incoming workload and prefixed capacity of worker nodes can lead to resource under-utilization. To tackle this issue and to improve the performance of data centers, a Multi-Agent deep reinforcement learning-based Resource Management model (MARM) is presented, that reduces the overhead of resource under-utilization. It utilizes the resources efficiently by choosing the best worker node for the incoming workload resource requirements. It allows multiple agents to operate cooperatively while sharing communication. The proposed model keeps track of minimal resources requested by incoming function instances and schedules it to the most suited worker node to encourage efficient resource utilization. Extensive experiments are conducted using the proposed model in a simulated environment using a synthetic dataset and scaled-down Alibaba Cluster Trace. The experimental results show the efficacy of the proposed model in terms of improved CPU and memory utilization up to 39 and 30% over state-of-the-art schedulers, respectively.

关键词： serverless computing Multi-agent Resource management Function instance Function scheduling

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Machine learning inference serving models in serverless computing: a survey

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computing 2025年第1期107卷 1-52页

作者： Aslani, Akram Ghobaei-Arani, Mostafa Islamic Azad Univ Dept Comp Engn Qom Branch Qom Iran

serverless computing has attracted many researchers with features such as scalability and optimization of operating costs, no need to manage infrastructures, and build programs at a higher speed. serverless computing can be used for real-time machine learning (ML) prediction using serverless inference functions. Deploying an ML serverless inference function involves building a compute resource, deploying an ML model, network infrastructure, and permissions to call the inference function. However, the subject of machine learning inference (MLI) has challenges such as resource management, delay and response time, large and complex models, and security and privacy, not many studies have been conducted in this field. This comprehensive literature review article examines the recent developments in MLI in serverless computing environments. The mechanisms presented in the taxonomy can be summarized in four categories: service level objective SLO-aware, acceleration-aware, framework-aware, and latency-aware. In each category, different methods and algorithms used to optimize inference in serverless environments have been examined along with their advantages and disadvantages. We show that acceleration-aware methods focus on the optimal use of computing resources, and framework-aware methods play an important role in improving system efficiency and scalability by examining different frameworks for inference in serverless environments. Also, SLO-aware and Latency-aware methods, considering time limits and service level agreement, help provide quality and reliable inference in serverless environments. Finally, this article presents a vision of future challenges and opportunities in this field and provides solutions for future research in the field of MLI in serverless.

关键词： serverless computing Function-as-a-service Machine learning inference Deep learning Inference serving models

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Fogfaas: providing serverless computing simulation for iFogSim and edge cloud

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JOURNAL OF SUPERcomputing 2025年第5期81卷 1-20页

作者： Ghaseminya, Mohammad Mahdi Fazeli, Seyed Abolfazl Shahzadeh Abouei, Jamshid Abbasi, Elham Yazd Univ Dept Comp Sci Pajohesh Blvd Yazd *** Iran Yazd Univ Parallel Proc Lab Pajohesh Blvd Yazd *** Iran Yazd Univ Dept Elect Engn Pajohesh Blvd Yazd *** Iran

serverless computing, or function as a service (FaaS), has transformed application development by abstracting server management and enabling event-driven execution of stateless functions. In the internet of things (IoT) context, FaaS is particularly useful due to the dynamic and resource-constrained nature of IoT devices. FaaS allows IoT applications to scale efficiently without needing to manage physical servers, which is crucial for IoT environments with fluctuating workloads and real-time data processing needs. In this paper, we address these issues and introduce FoGFaaS, an extension of the iFogSim simulator that incorporates the ability to simulate FaaS in fog computing environments. FoGFaaS leverages the robust modeling capabilities of iFogSim, but with extensive modifications (nearly 70%) to its core classes, providing a dedicated architecture for evaluating serverless platforms. These modifications maintain the core functionality of iFogSim, allowing users to perform fog and edge computing simulations using FoGFaaS in both traditional and serverless computing paradigms. This novel simulation tool enables researchers to analyze task scheduling, resource allocation, and performance metrics, advancing the study of FaaS in IoT and edge scenarios.

关键词： serverless computing Function as a services (FaaS) iFogSim Modeling and simulation

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