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serverless computing: From Planet Mars to the Cloud

computing IN SCIENCE & ENGINEERING 2018年第6期20卷 73-79页

作者： Luis Vazquez-Poletti, Jose Martin Llorente, Ignacio Hinsen, Konrad Turk, Matthew Univ Complutense Madrid Madrid Spain Univ Complutense Madrid Open Source Software & Open Technol Off Madrid Spain Univ Complutense Madrid Comp Architecture Madrid Spain Univ Complutense Madrid Data Intens Cloud Lab Madrid Spain

serverless computing is a new way of managing computations in the cloud. We show how it can be put to work for scientific data analysis. For this, we detail our serverless architecture for an application analyzing data from one of the instruments onboard the ESA Mars Express orbiter, and then, we compare it with a traditional server solution.

关键词： Astronomy computing Data Analysis Mars Space Vehicles Planet Mars serverless computing Scientific Data Analysis serverless Architecture ESA Mars Express Orbiter Cloud Cloud computing Containers Mars Servers Computational Modeling Artificial Intelligence Programming

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serverless computing: State-of-the-Art, Challenges and Opportunities

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IEEE TRANSACTIONS ON SERVICES computing 2023年第2期16卷 1522-1539页

作者： Li, Yongkang Lin, Yanying Wang, Yang Ye, Kejiang Xu, Chengzhong Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Guangdong Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Univ Macau Dept Comp & Informat Sci Macau Peoples R China

serverless computing is growing in popularity by virtue of its lightweight and simplicity of management. It achieves these merits by reducing the granularity of the computing unit to the function level. Specifically, serverless allows users to focus squarely on the function itself while leaving other cumbersome management and scheduling issues to the platform provider, who is responsible for striking a balance between high-performance scheduling and low resource cost. In this article, we conduct a comprehensive survey of serverless computing with a particular focus on its infrastructure characteristics. Whereby some existing challenges are identified, and the associated cutting-edge solutions are analyzed. With these results, we further investigate some typical open-source frameworks and study how they address the identified challenges. Given the great advantages of serverless computing, it is expected that its deployment would dominate future cloud platforms. As such, we also envision some promising research opportunities that need to be further explored in the future. We hope that our work in this article can inspire those researchers and practitioners who are engaged in related fields to appreciate serverless computing, thereby setting foot in this promising area and making great contributions to its development.

关键词： serverless computing Containers Processor scheduling Virtualization FAA Databases Costs Survey serverless computing FaaS and BaaS startup latency isolation scheduling

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serverless computing: A Survey of Opportunities, Challenges, and Applications

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ACM computing SURVEYS 2022年第11S期54卷 1–32页

作者： Shafiei, Hossein Khonsari, Ahmad Mousavi, Payam KN Toosi Univ Technol Tehran Iran Univ Tehran Tehran Iran Inst Res Fundamental Sci IPM Tehran Iran

The emerging serverless computing paradigm has attracted attention from both academia and industry. This paradigm brings benefits such as less operational complexity, a pay-as-you-go pricing model, and an auto-scaling feature. The paradigm opens up new opportunities and challenges for cloud application developers. In this article, we present a comprehensive overview of the past development as well as the recent advances in research areas related to serverless computing. First, we survey serverless applications introduced in the literature. We categorize applications in eight domains and separately discuss the objectives and the viability of the serverless paradigm along with challenges in each of those domains. We then classify those challenges into nine topics and survey the proposed solutions. Finally, we present the areas that need further attention from the research community and identify open problems.

关键词： Cloud services serverless computing function-as-a-service (FaaS)

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serverless computing for QoS-Effective NFV in the Cloud Edge

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IEEE COMMUNICATIONS MAGAZINE 2024年第4期62卷 40-46页

作者： Sabbioni, Andrea Garbugli, Andrea Foschini, Luca Corradi, Antonio Bellavista, Paolo Univ Bologna Comp Sci & Engn Bologna Italy Univ Bologna Bologna Italy Univ Bologna Comp Engn Bologna Italy

Network Softwarization, particularly Network Function Virtualization (NFV), is revolutionizing networking by separating the hardware from the supported network functions, demonstrating unprecedented flexibility and cost-efficiency. With the rise of edge computing, NFV has found a strategic application at the network edge, where network functions are deployed closer to end-users and devices with improved performance and reduced latency. In this context, the serverless paradigm is gaining attention due to its fine-grained scalability, reduced management efforts, and resource efficiency. Despite the advantages, challenges arise from the ephemeral nature of serverless functions, leading to latency issues and hampering the creation of efficient coordination and orchestration mechanisms. Adopting this paradigm in resource-constrained scenarios, such as cloud edges, is even more challenging due to the insufficient presence of proper quality-of-service (QoS) mechanisms in these platforms. This article introduces our novel time effective middleware for priority oriented serverless network function virtualization (TEMPOS4NFV), a QoS-aware platform specialized in hosting serverless network functions in resource-constrained environments. TEMPOS4NFV addresses resource contention and coordination challenges, offering effective end-to-end QoS differentiation for network workloads executed over multiple federated sites. In addition, this article examines TEMPOS4NFV hosting a real virtual private network (VPN) case, demonstrating its ability to execute concurrent multi-site QoS differentiated workloads.

关键词： Quality of service Method of moments Scalability Virtual private networks Computer architecture Optimization serverless computing Network function virtualization Cloud computing

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serverless computing: a security perspective

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JOURNAL OF CLOUD computing-ADVANCES SYSTEMS AND APPLICATIONS 2022年第1期11卷 1-12页

作者： Marin, Eduard Perino, Diego Di Pietro, Roberto Telefon Res Barcelona Spain Hamad Bin Khalifa Univ HBKU Coll Sci & Engn CSE Informat & Comp Technol ICT Doha Qatar

In this article we review the current serverless architectures, abstract and categorize their founding principles, and provide an in-depth security analysis. In particular, we: show the security shortcomings of the analyzed serverless architectural paradigms;point to possible countermeasures;and, highlight several research directions for practitioners, Industry, and Academia.

关键词： Cloud computing serverless computing Security Threat models Vulnerabilities Architectures

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serverless computing on Heterogeneous Computers 27

Serverless Computing on Heterogeneous Computers

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27th ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS)

作者： Du, Dong Liu, Qingyuan Jiang, Xueqiang Xia, Yubin Zang, Binyu Chen, Haibo Shanghai Jiao Tong Univ Inst Parallel & Distributed Syst Shanghai Peoples R China Shanghai Jiao Tong Univ Inst Parallel & Distributed Syst Shanghai Artificial Intelligence Lab Shanghai Peoples R China

ISBN: (纸本)9781450392051

Existing serverless computing platforms are built upon homogeneous computers, limiting the function density and restricting serverless computing to limited scenarios. We introduce Molecule, the first serverless computing system utilizing heterogeneous computers. Molecule enables both general-purpose devices (e.g., Nvidia DPU) and domain-specific accelerators (e.g., FPGA and GPU) for serverless applications that significantly improve function density (50% higher) and application performance (up to 34.6x). To achieve these results, we first propose XPU-Shim, a distributed shim to bridge the gap between underlying multi-OS systems (when using general-purpose devices) and our serverless runtime (i.e., Molecule). We further introduce vectorized sandbox, a sandbox abstraction to abstract hardware heterogeneity (when using domain-specific accelerators). Moreover, we also review state-of-the-art serverless optimizations on startup and communication latency and overcome the challenges to implement them on heterogeneous computers. We have implemented Molecule on real platforms with Nvidia DPUs and Xilinx FPGAs and evaluate it using benchmarks and real-world applications.

关键词： Cloud computing serverless computing heterogeneous computers function-as-a-service operating system

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serverless computing for NFV: Is it Worth it? A Performance Comparison Analysis

Serverless Computing for NFV: Is it Worth it? A Performance ...

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20th IEEE International Conference on Pervasive computing and Communications (IEEE PerCom)

作者： Savi, Marco Banfi, Alessandro Tundo, Alessandro Ciavotta, Michele Univ Milano Bicocca Dept Informat Syst & Commun Milan Italy

ISBN: (纸本)9781665416474

Network Function Virtualization has established itself as one of the most important paradigms towards software-based networking. While today Virtual Network Functions (VNFs) are typically deployed in the form of serverful virtual-machine or container-based applications, the emergence of serverless computing opens the door to the possibility of implementing them as serverless functions, with benefits in terms of scalability and resource efficiency. This paper aims to assess whether this really makes sense or not, given the system-level overheads that a serverless computing platform naturally brings. We propose an open source platform designed to optimize the execution of network-intensive VNFs and we implement a data-plane and a control-plane function (i.e., NAT and DHCP responder, respectively) as serverless functions. We carry out extensive benchmarking of performance with their serverful counterparts, implemented as stand-alone containerized applications. Our experience makes it possible to conclude that serverless computing is beneficial for the execution of short-lived and request-based control-plane VNFs, while it should be avoided for the execution of data-plane traffic-intensive VNFs.

关键词： Network Function Virtualization serverless computing Function-as-a-Service

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serverless computing Approach for Deploying Machine Learning Applications in Edge Layer 36

Serverless Computing Approach for Deploying Machine Learning...

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36th International Conference on Information Networking (ICOIN)

作者： Bac, Ta Phuong Tran, Minh Ngoc Kim, YoungHan Soongsil Univ Sch Elect Engn Seoul South Korea

ISBN: (纸本)9781665413329

serverless computing- a stateless cloud computing model, is an emerging solution that has shown significant benefits to efficiency and cost for event-driven applications in the cloud environment, including artificial intelligence (AI), machine learning applications. With serverless computing, the machine learning system's complexity is minimized, flexible and straightforward in management. However, operating and managing serverless machine learning services on clouds faces many limitations such as latency and data privacy. Local distributed edge computing nodes which are closed to users can address these challenges of cloud-serverless AI applications. Based on this motivation, in this paper, we propose an architecture for deploying machine learning workload as serverless functions in the edge environment. We illustrate our proposed approach and evaluate its performance and effectiveness by exploiting a holistic end-to-end image classifier, a famous machine learning use case in the MNIST dataset. Our proof of concept provides comprehensive assessments that prove its effectiveness in latency reduction and distributed machine learning deployment.

关键词： serverless computing machine learning Edge-cloud computing architecture

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serverless computing Lifecycle Model for Edge Cloud Deployments

Serverless Computing Lifecycle Model for Edge Cloud Deployme...

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IEEE International Conference on Communications (IEEE ICC)

作者： Nguyen, Kien Loh, Frank Tung Nguyen Duong Doan Nguyen Huu Thanh Hossfeld, Tobias Univ Wurzburg Inst Comp Sci Wurzburg Germany Hanoi Univ Sci & Technol Hanoi Vietnam FPT Software LLC Hanoi Vietnam

ISBN: (纸本)9798350333077

serverless computing is a new cloud computing execution model. It offers little operational and administrative overhead, function activation on-demand, and significantly reduced resource consumption. For that reason, it is studied heavily in recent literature by means of handling incoming traffic, applicability for different tasks, and overall performance. Especially the introduction of states, where functionality is kept in CPU or memory can improve performance significantly. However, an in-depth analysis of the resource demand by among others, CPU and memory usage, latency, and energy consumption is still a blank spot in literature. To this end, we propose a serverless lifecycle model with intermediate states and deploy an image processing test setup in the edge cloud to test it. Furthermore, we measure important resource metrics of all states and state transitions of the model. This helps us to answer the question, whether keeping functions available in specific states of a serverless computing instance can improve performance without massive negative influence on the resource requirements.

关键词： serverless computing resource efficiency energy efficiency edge computing

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Real-time task dispatching and scheduling in serverless edge computing

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AD HOC NETWORKS 2025年 174卷

作者： Li, Ming Xu, Furong Wu, Yuqin Zhang, Jianshan Xu, Weitao Wu, Yuezhong Fuzhou Univ Coll Comp & Data Sci Fuzhou 350116 Peoples R China Fuzhou Univ Fujian Key Lab Network Comp & Intelligent Informat Fuzhou 350116 Peoples R China Fujian Inst Educ Fuzhou 350118 Peoples R China Ningde Normal Univ Coll Informat Engn Ningde 352000 Peoples R China Minjiang Univ Sch Comp & Big Data Fuzhou 350118 Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong 999077 Peoples R China

Edge computing brings computing resources closer to the Internet of Things (IoT) devices, significantly reducing transmission latency and bandwidth usage. However, the limited resources of edge servers require efficient management. serverless computing meets this demand through its elastic resource provisioning, leading to the emergence of serverless edge computing-a promising computing paradigm. Despite its potential, realtime task dispatching and scheduling in the highly complex and dynamic environment of serverless edge computing present significant challenges. On the one hand, task execution requires not only sufficient CPU resources but also free containers;on the other hand, tasks are typically event-driven, with strong burstiness and high concurrency, and impose stringent demands on fast decision-making. To address these challenges, we propose a real-time task dispatching and scheduling method, aiming to maximize the satisfaction rate of Service Level Objectives (SLOs) for tasks. First, we design a task dispatching algorithm named Adaptive Deep Reinforcement Learning (ADRL). This algorithm can quickly decide the execution position of tasks based on coarse information and effectively adapt to the changes in available servers in dynamic environments. Second, we propose a task scheduling algorithm named Warm-aware Shortest Remaining Idle Time (WSRIT), which guides the edge servers to schedule the tasks in the request queue based on the tasks' remaining idle time and the state of the warm containers. Considering the limited storage space of the edge servers, we further introduce a container replacement algorithm named Low Priority First (LPF) to ensure smooth container launches. Extensive simulation experiments are conducted based on Azure datasets. The results show that our methodcan improve the satisfaction rate of SLOs by 12.57 similar to 41.87% and achieve the lowest cold start rate compared to existing methods.

关键词： Edge computing serverless computing Real-time tasks Task scheduling Deep reinforcement learning

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