系统映射研究数据集

该数据集用于系统映射研究，专注于工程FaaS平台和工具的最新研究状态。数据集包含了搜索阶段每一步的原始数据、最终选定的出版物列表以及数据提取表格。

This dataset is designed for systematic mapping studies, focusing on the latest research status of engineering FaaS (Function as a Service) platforms and tools. The dataset includes raw data from each step of the search phase, a final list of selected publications, and data extraction tables.

原始信息汇总

数据集概述

数据集名称

Dataset for a systematic mapping study

数据集描述

该数据集用于系统映射研究，专注于工程FaaS（Function as a Service）平台和工具的最新研究状态。数据集包含搜索阶段的原始数据、最终选定的出版物列表以及数据提取表格。

数据集内容

• 搜索阶段原始数据
• 最终选定的出版物列表
• 数据提取表格

出版物列表

以下是数据集中包含的出版物列表及其详细信息：

Paper ID	Title	Authors	BibTex
P1	SAND: Towards High-performance Serverless Computing	Akkus et al.	Akkus:2018:STH:3277355.3277444
P2	Making Serverless Computing More Serverless	Al-Ali et al.	8457832
P3	S-FaaS: Trustworthy and Accountable Function-as-a-Service using Intel SGX	Alder et al.	2018arXiv181006080A
P4	Secure Serverless Computing Using Dynamic Information Flow Control	Alpernas et al.	Alpernas:2018:SSC:3288538.3276488
P5	Beyond Load Balancing: Package-Aware Scheduling for Serverless Platforms	Aumala et al.	8752939
P6	Putting the "Micro" Back in Microservice	Boucher et al.	Boucher:2018:PMB:3277355.3277417
P7	Trust More, Serverless	Brenner and Kapitza	Brenner:2019:TMS:3319647.3325825
P8	BalloonJVM: Dynamically Resizable Heap for FaaS	Chan et al.	chan2019balloonjvm
P9	An effective resource management approach in a FaaS environment	Christoforou and Andreou	christoforou2018effective
P10	openCoT: The opensource Cloud of Things platform	Danayi and Sharifian	2019arXiv190100302D
P11	PESS-MinA: A Proactive Stochastic Task Allocation Algorithm for FaaS Edge-Cloud environments	Danayi and Sharifian	8700543
P12	An Execution Model for Serverless Functions at the Edge	Hall and Ramachandran	Hall:2019:EMS:3302505.3310084
P13	Serverless Computation with openLambda	Hendrickson et al.	Hendrickson:2016:SCO:3027041.3027047
P14	A QoS-Aware Resource Allocation Controller for Function as a Service (FaaS) Platform	HoseinyFarahabady et al.	10.1007/978-3-319-69035-3_17
P15	A Model Predictive Controller for Managing QoS Enforcements and Microarchitecture-Level Interferences in a Lambda Platform	HoseinyFarahabady et al.	8126823
P16	Checkpointing and Migration of IoT Edge Functions	Karhula et al.	Karhula:2019:CMI:3301418.3313947
P17	Temporal Overbooking of Lambda Functions in the Cloud	Kesidis	2019arXiv190109842K
P18	Design of the Cost Effective Execution Worker Scheduling Algorithm for FaaS Platform Using Two-Step Allocation and Dynamic Scaling	Kim and Cha	8567385
P19	GPU Enabled Serverless Computing Framework	Kim et al.	8374513
P20	Dynamic Control of CPU Usage in a Lambda Platform	Kim et al.	8514884
P21	Will Serverless End the Dominance of Linux in the Cloud?	Koller and Williams	Koller:2017:SED:3102980.3103008
P22	Mitigating Cold Starts in Serverless Platforms: A Pool-Based Approach	Lin and Glikson	2019arXiv190312221L
P23	Serverless Computing: Design, Implementation, and Performance	McGrath and Brenner	7979855
P24	Retro-$Lambda$: An Event-sourced Platform for Serverless Applications with Retroactive Computing Support	Meissner et al.	MeiBner:2018:REP:3210284.3210285
P25	Pipsqueak: Lean Lambdas with Large Libraries	Oakes et al.	7979853
P26	SOCK: Rapid Task Provisioning with Serverless-optimized Containers	Oakes et al.	Oakes:2018:SRT:3277355.3277362
P27	Se-Lambda: Securing Privacy-Sensitive Serverless Applications Using SGX Enclave	Qiang et al.	10.1007/978-3-030-01701-9_25
P28	EMARS: Efficient Management and Allocation of Resources in Serverless	Saha and Jindal	8457882
P29	Towards Distributed Containerized Serverless Architecture in Multi Cloud Environment	Soltani et al.	SOLTANI2018121
P30	A Migration-based Approach to execute Long-Duration Multi-Cloud Serverless Functions.	Soltani et al.	soltani2018migration
P31	Snafu: Function-as-a-Service (FaaS) Runtime Design and Implementation	Spillner	2017arXiv170307562S
P32	The Serverless Scheduling Problem and NOAH	Stein	2018arXiv180906100S
P33	Clemmys: Towards Secure Remote Execution in FaaS	Trach et al.	Trach:2019:CTS:3319647.3325835
P34	A SPEC RG Cloud Groups Vision on the Performance Challenges of FaaS Cloud Architectures	van Eyk et al.	vanEyk:2018:SRC:3185768.3186308
P35	Replayable Execution Optimized for Page Sharing for a Managed Runtime Environment	Wang et al.	Wang:2019:REO:3302424.3303978
P36	Supporting Multi-Provider Serverless Computing on the Edge	Aske and Zhao	Aske:2018:SMS:3229710.3229742
P37	Using a Microbenchmark to Compare Function as a Service Solutions	Back and Andrikopoulos	10.1007/978-3-319-99819-0_11
P38	Reserved, on demand or serverless: Model-based simulations for cloud budget planning	Boza et al.	8247460
P39	Visualizing serverless cloud application logs for program understanding	Chang and Fink	8103476
P40	Costless: Optimizing Cost of Serverless Computing through Function Fusion and Placement	Elgamal	8567674
P41	Performance evaluation of heterogeneous cloud functions	Figiela et al.	doi:10.1002/cpe.4792
P42	FaaStest - Machine Learning Based Cost and Performance FaaS Optimization	Horovitz et al.	10.1007/978-3-030-13342-9_15
P43	Implementation of a DevOps Pipeline for Serverless Applications	Ivanov and Smolander	10.1007/978-3-030-03673-7_4
P44	An Investigation of the Impact of Language Runtime on the Performance and Cost of Serverless Functions	Jackson and Clynch	8605773
P45	Formal Foundations of Serverless Computing	Jangda et al.	2019arXiv190205870J
P46	Pocket: Elastic Ephemeral Storage for Serverless Analytics	Klimovic et al.	Klimovic:2018:PEE:3291168.3291200
P47	Towards an Optimized, Cloud-Agnostic Deployment of Hybrid Applications	Kritikos and Skrzypek	kritikos2019towards
P48	Costradamus: A Cost-Tracing System for Cloud-Based Software Services	Kuhlenkamp and Klems	10.1007/978-3-319-69035-3_48
P49	Modelling and managing deployment costs of microservice-based cloud applications	Leitner et al.	leitner2016modelling
P50	Tracing Function Dependencies across Clouds	Lin et al.	8457807
P51	Tracking Causal Order in AWS Lambda Applications	Lin et al.	8360312
P52	Cold Start Influencing Factors in Function as a Service	Manner et al.	8605777
P53	Troubleshooting Serverless functions: a combined monitoring and debugging approach	Manner et al.	Manner2019
P54	Visual-Textual Framework for Serverless Computation: A Luna Language Approach	Moczurad and Malawski	8605775
P55	The Less Server Architecture for Cloud Functions	Nadgowda et al.	Nadgowda:2017:LSA:3154847.3154850
P56	Function-as-a-Service Benchmarking Framework	Pellegrini et al.	2019arXiv190511707P
P57	Serverless computing for container-based architectures	Pérez et al.	PEREZ201850
P58	Dynamic Allocation of Serverless Functions in IoT Environments	Pinto et al.	8588841
P59	Transformation of Python Applications into Function-as-a-Service Deployments	Spillner	2017arXiv170508169S
P60	Java Code Analysis and Transformation into AWS Lambda Functions	Spillner and Dorodko	2017arXiv170205510S
P61	Model-based analysis of serverless applications	Winzinger and Wirtz	winzinger2019model
P62	Modeling and Automated Deployment of Serverless Applications Using TOSCA	Wurster et al.	8599581

该数据集为研究FaaS平台和工具的系统映射研究提供了全面的资源，包括搜索阶段的原始数据和详细的出版物列表。

搜集汇总

数据集介绍

构建方式

系统映射研究数据集的构建采用了系统化的搜索策略，涵盖文献检索、筛选和数据分析等多个步骤。该数据集整合了工程FaaS平台和工具最新研究成果的原始数据，包括搜索阶段的每一步骤，最终选定的出版物列表以及数据提取表。

特点

该数据集的特点在于其全面性，不仅包含了丰富的文献资源，还提供了用于系统映射研究的详细数据提取表。这些表格有助于研究人员对文献进行深入分析，从而对FaaS平台和工具的研究现状有一个清晰的认识。此外，每个条目都附有Bibtex键，便于用户快速定位和引用相关文献。

使用方法

使用该数据集时，研究人员可以先通过提供的Bibtex键查找特定文献的详细信息，然后利用数据提取表中的数据对文献进行分类和归纳。数据集的结构化设计使得用户能够高效地进行文献综述，支持系统映射研究的深入进行。

背景与挑战

背景概述

系统映射研究数据集，专注于工程FaaS平台和工具的最新研究状态。该数据集由一系列搜索阶段的原生数据、最终选定的出版物列表以及数据提取表组成。它为研究人员提供了一个宝贵的资源，以系统性地分析和理解FaaS领域的研究趋势和进展。该数据集的创建，汇集了众多研究人员和机构的努力，自发布以来，对促进Serverless计算领域的研究和交流产生了显著影响。

当前挑战

在研究领域，该数据集面临的挑战包括如何确保所选文献的全面性和代表性，以及如何处理大量的数据以提取有用信息。在构建过程中，挑战涉及对FaaS平台和工具的广泛搜索，筛选相关文献，并从中提取关键信息，这些步骤需要精确和系统的方法。此外，随着Serverless计算的快速发展，数据集的维护和更新也是一个持续的挑战。

常用场景

经典使用场景

系统映射研究数据集针对工程FaaS平台和工具的最新研究状态，提供了搜索阶段的原始数据、最终选定的出版物列表以及数据提取表。其经典使用场景在于，研究者可借此数据集对FaaS领域的文献进行全面的梳理与分析，从而构建系统的知识地图，为后续的研究提供坚实的基础。

解决学术问题

该数据集解决了学术研究中文献筛选与数据提取的一致性问题，确保了研究过程的可重复性。它帮助学者们高效地识别和分析FaaS平台和工具领域的现状与趋势，促进了学术研究的深度和广度，对于推动相关技术的进步具有重要的学术价值。

衍生相关工作

基于该数据集，研究者已衍生出多项相关工作，如构建专门的FaaS性能评估框架、成本优化策略、安全性增强方案等，这些工作进一步拓展了FaaS领域的研究边界，推动了技术的商业化应用和学术研究的深入发展。