Proceedings of the 2020 Sixth International Workshop on Serverless Computing最新文献

Evaluation of Network File System as a Shared Data Storage in Serverless Computing 无服务器计算中网络文件系统作为共享数据存储的评价

Proceedings of the 2020 Sixth International Workshop on Serverless Computing Pub Date : 2020-12-07 DOI: 10.1145/3429880.3430096

Jaeghang Choi, Kyungyong Lee

引用次数: 4

Towards Federated Learning using FaaS Fabric 迈向使用FaaS结构的联邦学习

Proceedings of the 2020 Sixth International Workshop on Serverless Computing Pub Date : 2020-12-07 DOI: 10.1145/3429880.3430100

Mohak Chadha, Anshul Jindal, M. Gerndt

引用次数: 19

The Serverless Application Analytics Framework: Enabling Design Trade-off Evaluation for Serverless Software 无服务器应用分析框架:实现无服务器软件的设计权衡评估

Proceedings of the 2020 Sixth International Workshop on Serverless Computing Pub Date : 2020-12-07 DOI: 10.1145/3429880.3430103

R. Cordingly, Hanfei Yu, Varik Hoang, Zohreh Sadeghi, David Foster, David Perez, Rashad Hatchett, W. Lloyd

{"title":"The Serverless Application Analytics Framework: Enabling Design Trade-off Evaluation for Serverless Software","authors":"R. Cordingly, Hanfei Yu, Varik Hoang, Zohreh Sadeghi, David Foster, David Perez, Rashad Hatchett, W. Lloyd","doi":"10.1145/3429880.3430103","DOIUrl":"https://doi.org/10.1145/3429880.3430103","url":null,"abstract":"To help better understand factors that impact performance on Function-as-a-Service (FaaS) platforms we have developed the Serverless Application Analytics Framework (SAAF). SAAF provides a reusable framework supporting multiple programming languages that developers can integrate into a function's package for deployment to multiple commercial and open source FaaS platforms. SAAF improves the observability of FaaS function deployments by collecting forty-eight distinct metrics to enable developers to profile CPU and memory utilization, monitor infrastructure state, and observe platform scalability. In this paper, we describe SAAF in detail and introduce supporting tools highlighting important features and how to use them. Our client application, FaaS Runner, provides a tool to orchestrate workloads and automate the process of conducting experiments across FaaS platforms. We provide a case study demonstrating the integration of SAAF into an existing open source image processing pipeline built for AWS Lambda. Using FaaS Runner, we automate experiments and acquire metrics from SAAF to profile each function of the pipeline to evaluate performance implications. Finally, we summarize contributions using our tools to evaluate implications of different programming languages for serverless data processing, and to build performance models to predict runtime for serverless workloads.","PeriodicalId":224350,"journal":{"name":"Proceedings of the 2020 Sixth International Workshop on Serverless Computing","volume":"15 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114115042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 18

An Evaluation of Serverless Data Processing Frameworks 无服务器数据处理框架的评估

Proceedings of the 2020 Sixth International Workshop on Serverless Computing Pub Date : 2020-12-07 DOI: 10.1145/3429880.3430095

Sebastian Werner, Richard Girke, Jörn Kuhlenkamp

引用次数: 4

Implications of Public Cloud Resource Heterogeneity for Inference Serving 公共云资源异构对推理服务的影响

Proceedings of the 2020 Sixth International Workshop on Serverless Computing Pub Date : 2020-12-07 DOI: 10.1145/3429880.3430093

J. Gunasekaran, Cyan Subhra Mishra, P. Thinakaran, M. Kandemir, C. Das

{"title":"Implications of Public Cloud Resource Heterogeneity for Inference Serving","authors":"J. Gunasekaran, Cyan Subhra Mishra, P. Thinakaran, M. Kandemir, C. Das","doi":"10.1145/3429880.3430093","DOIUrl":"https://doi.org/10.1145/3429880.3430093","url":null,"abstract":"We are witnessing an increasing trend towards using Machine Learning (ML) based prediction systems, spanning across different application domains, including product recommendation systems, personal assistant devices, facial recognition, etc. These applications typically have diverse requirements in terms of accuracy and response latency, that can be satisfied by a myriad of ML models. However, the deployment cost of prediction serving primarily depends on the type of resources being procured, which by themselves are heterogeneous in terms of provisioning latencies and billing complexity. Thus, it is strenuous for an inference serving system to choose from this confounding array of resource types and model types to provide low-latency and cost-effective inferences. In this work we quantitatively characterize the cost, accuracy and latency implications of hosting ML inferences on different public cloud resource offerings. Our evaluation shows that, prior work does not solve the problem from both dimensions of model and resource heterogeneity. Hence, to holistically address this problem, we need to solve the issues that arise from combining both model and resource heterogeneity towards optimizing for application constraints. Towards this, we discuss the design implications of a self-managed inference serving system, which can optimize for application requirements based on public cloud resource characteristics.","PeriodicalId":224350,"journal":{"name":"Proceedings of the 2020 Sixth International Workshop on Serverless Computing","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121690225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

Resource Management for Cloud Functions with Memory Tracing, Profiling and Autotuning 具有内存跟踪、分析和自动调优的云功能的资源管理

Proceedings of the 2020 Sixth International Workshop on Serverless Computing Pub Date : 2020-12-07 DOI: 10.1145/3429880.3430094

Josef Spillner

引用次数: 8

Bringing scaling transparency to Proteomics applications with serverless computing 通过无服务器计算为蛋白质组学应用程序带来缩放透明度

Proceedings of the 2020 Sixth International Workshop on Serverless Computing Pub Date : 2020-12-07 DOI: 10.1145/3429880.3430101

M. Mirabelli, P. López, G. Vernik

{"title":"Bringing scaling transparency to Proteomics applications with serverless computing","authors":"M. Mirabelli, P. López, G. Vernik","doi":"10.1145/3429880.3430101","DOIUrl":"https://doi.org/10.1145/3429880.3430101","url":null,"abstract":"Scaling transparency means that applications can expand in scale without changes to the system structure or the application algorithms. Serverless Computing's inherent auto-scaling support and fast function launching is ideally suited to support scaling transparency in different domains. In particular, Proteomic applications could considerably benefit from scaling transparency and serverless technologies due to their high concurrency requirements. Therefore, the auto-provisioning nature of serverless platforms makes this computing model an alternative to satisfy dynamically the resources required by protein folding simulation processes. However, the transition to these architectures must face challenges: they should show comparable performance and cost to code running in Virtual Machines (VMs). In this article, we demonstrate that Proteomics applications implemented with the Replica Exchange algorithm can be moved to serverless settings guaranteeing scaling transparency. We also validate that we can reduce the total execution time by around forty percent with comparable cost to cluster technologies (Work Queue) over VMs.","PeriodicalId":224350,"journal":{"name":"Proceedings of the 2020 Sixth International Workshop on Serverless Computing","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116356563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Temporal Performance Modelling of Serverless Computing Platforms 无服务器计算平台的时间性能建模

Proceedings of the 2020 Sixth International Workshop on Serverless Computing Pub Date : 2020-12-07 DOI: 10.1145/3429880.3430092

Nima Mahmoudi, Hamzeh Khazaei

{"title":"Temporal Performance Modelling of Serverless Computing Platforms","authors":"Nima Mahmoudi, Hamzeh Khazaei","doi":"10.1145/3429880.3430092","DOIUrl":"https://doi.org/10.1145/3429880.3430092","url":null,"abstract":"Analytical performance models have been shown very efficient in analyzing, predicting, and improving the performance of distributed computing systems. However, there is a lack of rigorous analytical models for analyzing the transient behaviour of serverless computing platforms, which is expected to be the dominant computing paradigm in cloud computing. Also, due to its unique characteristics and policies, performance models developed for other systems cannot be directly applied to modelling these systems. In this work, we propose an analytical performance model that is capable of predicting several key performance metrics for serverless workloads using only their average response time for warm and cold requests. The introduced model uses realistic assumptions, which makes it suitable for online analysis of real-world platforms. We validate the proposed model through extensive experimentation on AWS Lambda. Although we focus primarily on AWS Lambda due to its wide adoption in our experimentation, the proposed model can be leveraged for other public serverless computing platforms with similar auto-scaling policies, e.g., Google Cloud Functions, IBM Cloud Functions, and Azure Functions.","PeriodicalId":224350,"journal":{"name":"Proceedings of the 2020 Sixth International Workshop on Serverless Computing","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121587727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 11

ACE 王牌

Proceedings of the 2020 Sixth International Workshop on Serverless Computing Pub Date : 2020-12-07 DOI: 10.1145/3429880.3430098

Anthony Byrne, S. Nadgowda, A. Coskun

引用次数: 2

Active-Standby for High-Availability in FaaS FaaS中的主备高可用性

Proceedings of the 2020 Sixth International Workshop on Serverless Computing Pub Date : 2020-12-07 DOI: 10.1145/3429880.3430097

Yasmina Bouizem, N. Parlavantzas, Djawida Dib, C. Morin

引用次数: 7