Fully-managed cloud and Function-as-a-Service (FaaS) services allow the wide adoption of serverless computing for various cloud-native applications. Despite the many advantages that serverless computing provides, no direct connection support exists between function run-times, and it is a barrier for data-intensive applications. To overcome this limitation, the leading cloud computing vendor Amazon Web Services (AWS) has started to support mounting the network file system (NFS) across different function run-times. This paper quantitatively evaluates the performance of accessing NFS storage from multiple function run-times and compares the performance with other methods of sharing data among function run-times. Despite the great qualitative benefits of the approach, the limited I/O bandwidth of NFS storage can become a bottleneck, especially when the number of concurrent access from function run-times increases.
完全托管的云和功能即服务(FaaS)服务允许为各种云原生应用程序广泛采用无服务器计算。尽管无服务器计算提供了许多优点，但是在函数运行时之间不存在直接连接支持，这对于数据密集型应用程序来说是一个障碍。为了克服这一限制，领先的云计算供应商Amazon Web Services (AWS)已经开始支持跨不同功能运行时挂载网络文件系统(NFS)。本文定量地评估了从多个函数运行时访问NFS存储的性能，并将其与其他函数运行时数据共享方法的性能进行了比较。尽管这种方法在质量上有很大的好处，但NFS存储有限的I/O带宽可能成为瓶颈，特别是当函数运行时并发访问的数量增加时。
{"title":"Evaluation of Network File System as a Shared Data Storage in Serverless Computing","authors":"Jaeghang Choi, Kyungyong Lee","doi":"10.1145/3429880.3430096","DOIUrl":"https://doi.org/10.1145/3429880.3430096","url":null,"abstract":"Fully-managed cloud and Function-as-a-Service (FaaS) services allow the wide adoption of serverless computing for various cloud-native applications. Despite the many advantages that serverless computing provides, no direct connection support exists between function run-times, and it is a barrier for data-intensive applications. To overcome this limitation, the leading cloud computing vendor Amazon Web Services (AWS) has started to support mounting the network file system (NFS) across different function run-times. This paper quantitatively evaluates the performance of accessing NFS storage from multiple function run-times and compares the performance with other methods of sharing data among function run-times. Despite the great qualitative benefits of the approach, the limited I/O bandwidth of NFS storage can become a bottleneck, especially when the number of concurrent access from function run-times increases.","PeriodicalId":224350,"journal":{"name":"Proceedings of the 2020 Sixth International Workshop on Serverless Computing","volume":"4 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":"114902078","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}
Federated learning (FL) enables resource-constrained edge devices to learn a shared Machine Learning (ML) or Deep Neural Network (DNN) model, while keeping the training data local and providing privacy, security, and economic benefits. However, building a shared model for heterogeneous devices such as resource-constrained edge and cloud makes the efficient management of FL-clients challenging. Furthermore, with the rapid growth of FL-clients, the scaling of FL training process is also difficult. In this paper, we propose a possible solution to these challenges: federated learning over a combination of connected Function-as-a-Service platforms, i.e., FaaS fabric offering a seamless way of extending FL to heterogeneous devices. Towards this, we present FedKeeper, a tool for efficiently managing FL over FaaS fabric. We demonstrate the functionality of FedKeeper by using three FaaS platforms through an image classification task with a varying number of devices/clients, different stochastic optimizers, and local computations (local epochs).
{"title":"Towards Federated Learning using FaaS Fabric","authors":"Mohak Chadha, Anshul Jindal, M. Gerndt","doi":"10.1145/3429880.3430100","DOIUrl":"https://doi.org/10.1145/3429880.3430100","url":null,"abstract":"Federated learning (FL) enables resource-constrained edge devices to learn a shared Machine Learning (ML) or Deep Neural Network (DNN) model, while keeping the training data local and providing privacy, security, and economic benefits. However, building a shared model for heterogeneous devices such as resource-constrained edge and cloud makes the efficient management of FL-clients challenging. Furthermore, with the rapid growth of FL-clients, the scaling of FL training process is also difficult. In this paper, we propose a possible solution to these challenges: federated learning over a combination of connected Function-as-a-Service platforms, i.e., FaaS fabric offering a seamless way of extending FL to heterogeneous devices. Towards this, we present FedKeeper, a tool for efficiently managing FL over FaaS fabric. We demonstrate the functionality of FedKeeper by using three FaaS platforms through an image classification task with a varying number of devices/clients, different stochastic optimizers, and local computations (local epochs).","PeriodicalId":224350,"journal":{"name":"Proceedings of the 2020 Sixth International Workshop on Serverless Computing","volume":"9 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":"123686682","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}
R. Cordingly, Hanfei Yu, Varik Hoang, Zohreh Sadeghi, David Foster, David Perez, Rashad Hatchett, W. Lloyd
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.
{"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}
Serverless computing is a promising cloud execution model that significantly simplifies cloud users' operational concerns by offering features such as auto-scaling and a pay-as-you-go cost model. Consequently, serverless systems promise to provide an excellent fit for ad-hoc data processing. Unsurprisingly, numerous serverless systems/frameworks for data processing emerged recently from research and industry. However, systems researchers, decision-makers, and data analysts are unaware of how these serverless systems compare to each other. In this paper, we identify existing serverless frameworks for data processing. We present a qualitative assessment of different system architectures and an experiment-driven quantitative comparison, including performance, cost, and usability using the TPC-H benchmark. Our results show that the three publicly available serverless data processing frameworks outperform a comparatively sized Apache Spark cluster in terms of performance and cost for ad-hoc queries on cold data.
{"title":"An Evaluation of Serverless Data Processing Frameworks","authors":"Sebastian Werner, Richard Girke, Jörn Kuhlenkamp","doi":"10.1145/3429880.3430095","DOIUrl":"https://doi.org/10.1145/3429880.3430095","url":null,"abstract":"Serverless computing is a promising cloud execution model that significantly simplifies cloud users' operational concerns by offering features such as auto-scaling and a pay-as-you-go cost model. Consequently, serverless systems promise to provide an excellent fit for ad-hoc data processing. Unsurprisingly, numerous serverless systems/frameworks for data processing emerged recently from research and industry. However, systems researchers, decision-makers, and data analysts are unaware of how these serverless systems compare to each other. In this paper, we identify existing serverless frameworks for data processing. We present a qualitative assessment of different system architectures and an experiment-driven quantitative comparison, including performance, cost, and usability using the TPC-H benchmark. Our results show that the three publicly available serverless data processing frameworks outperform a comparatively sized Apache Spark cluster in terms of performance and cost for ad-hoc queries on cold data.","PeriodicalId":224350,"journal":{"name":"Proceedings of the 2020 Sixth International Workshop on Serverless Computing","volume":"19 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":"134221031","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}
J. Gunasekaran, Cyan Subhra Mishra, P. Thinakaran, M. Kandemir, C. Das
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.
{"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}
Application software provisioning evolved from monolithic designs towards differently designed abstractions including serverless applications. The promise of that abstraction is that developers are free from infrastructural concerns such as instance activation and autoscaling. Today's serverless architectures based on FaaS are however still exposing developers to explicit low-level decisions about the amount of memory to allocate for the respective cloud functions. In many cases, guesswork and ad-hoc decisions determine the values a developer will put into the configuration. We contribute tools to measure the memory consumption of a function in various Docker, OpenFaaS and GCF/GCR configurations over time and to create trace profiles that advanced FaaS engines can use to autotune memory dynamically. Moreover, we explain how pricing forecasts can be performed by connecting these traces with a FaaS characteristics knowledge base.
{"title":"Resource Management for Cloud Functions with Memory Tracing, Profiling and Autotuning","authors":"Josef Spillner","doi":"10.1145/3429880.3430094","DOIUrl":"https://doi.org/10.1145/3429880.3430094","url":null,"abstract":"Application software provisioning evolved from monolithic designs towards differently designed abstractions including serverless applications. The promise of that abstraction is that developers are free from infrastructural concerns such as instance activation and autoscaling. Today's serverless architectures based on FaaS are however still exposing developers to explicit low-level decisions about the amount of memory to allocate for the respective cloud functions. In many cases, guesswork and ad-hoc decisions determine the values a developer will put into the configuration. We contribute tools to measure the memory consumption of a function in various Docker, OpenFaaS and GCF/GCR configurations over time and to create trace profiles that advanced FaaS engines can use to autotune memory dynamically. Moreover, we explain how pricing forecasts can be performed by connecting these traces with a FaaS characteristics knowledge base.","PeriodicalId":224350,"journal":{"name":"Proceedings of the 2020 Sixth International Workshop on Serverless Computing","volume":"67 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":"115148857","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}
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.
{"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}
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.
{"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}
While much of the software running on today's serverless platforms is written in easily-analyzed high-level interpreted languages, many performance-conscious users choose to deploy their applications as container-encapsulated compiled binaries on serverless container platforms such as AWS Fargate or Google Cloud Run. Modern CI/CD workflows make this deployment process nearly-instantaneous, leaving little time for in-depth manual application security reviews. This combination of opaque binaries and rapid deployment prevents cloud developers and platform operators from knowing if their applications contain outdated, vulnerable, or legally-compromised code. This paper proposes Approximate Concrete Execution (ACE), a just-in-time binary analysis technique that enables automatic software component discovery for serverless binaries. Through classification and search engine experiments with common cloud software packages, we find that ACE scans binaries 5.2x faster than a state-of-the-art binary analysis tool, minimizing the impact on deployment and cold-start latency while maintaining comparable recall.
{"title":"ACE","authors":"Anthony Byrne, S. Nadgowda, A. Coskun","doi":"10.1145/3429880.3430098","DOIUrl":"https://doi.org/10.1145/3429880.3430098","url":null,"abstract":"While much of the software running on today's serverless platforms is written in easily-analyzed high-level interpreted languages, many performance-conscious users choose to deploy their applications as container-encapsulated compiled binaries on serverless container platforms such as AWS Fargate or Google Cloud Run. Modern CI/CD workflows make this deployment process nearly-instantaneous, leaving little time for in-depth manual application security reviews. This combination of opaque binaries and rapid deployment prevents cloud developers and platform operators from knowing if their applications contain outdated, vulnerable, or legally-compromised code. This paper proposes Approximate Concrete Execution (ACE), a just-in-time binary analysis technique that enables automatic software component discovery for serverless binaries. Through classification and search engine experiments with common cloud software packages, we find that ACE scans binaries 5.2x faster than a state-of-the-art binary analysis tool, minimizing the impact on deployment and cold-start latency while maintaining comparable recall.","PeriodicalId":224350,"journal":{"name":"Proceedings of the 2020 Sixth International Workshop on Serverless Computing","volume":"161 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":"114814231","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}
Yasmina Bouizem, N. Parlavantzas, Djawida Dib, C. Morin
Serverless computing is becoming more and more attractive for cloud solution architects and developers. This new computing paradigm relies on Function-as-a-Service (FaaS) platforms that enable deploying functions without being concerned with the underlying infrastructure. An important challenge in designing FaaS platforms is ensuring the availability of deployed functions. Existing FaaS platforms address this challenge principally through retrying function executions. In this paper, we propose and implement an alternative fault-tolerance approach based on active-standby failover. Results from an experimental evaluation show that our approach increases availability and performance compared to the retry-based approach.
{"title":"Active-Standby for High-Availability in FaaS","authors":"Yasmina Bouizem, N. Parlavantzas, Djawida Dib, C. Morin","doi":"10.1145/3429880.3430097","DOIUrl":"https://doi.org/10.1145/3429880.3430097","url":null,"abstract":"Serverless computing is becoming more and more attractive for cloud solution architects and developers. This new computing paradigm relies on Function-as-a-Service (FaaS) platforms that enable deploying functions without being concerned with the underlying infrastructure. An important challenge in designing FaaS platforms is ensuring the availability of deployed functions. Existing FaaS platforms address this challenge principally through retrying function executions. In this paper, we propose and implement an alternative fault-tolerance approach based on active-standby failover. Results from an experimental evaluation show that our approach increases availability and performance compared to the retry-based approach.","PeriodicalId":224350,"journal":{"name":"Proceedings of the 2020 Sixth International Workshop on Serverless Computing","volume":"3 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":"127743331","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}
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