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Register a new userSeBS: A Serverless Benchmark Suite for Function-as-a-Service Computing
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Added by
Marcin Copik
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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Function-as-a-Service (FaaS) is one of the most promising directions for the future of cloud services, and serverless functions have immediately become a new middleware for building scalable and cost-efficient microservices and applications. However, the quickly moving technology hinders reproducibility, and the lack of a standardized benchmarking suite leads to ad-hoc solutions and microbenchmarks being used in serverless research, further complicating metaanalysis and comparison of research solutions. To address this challenge, we propose the Serverless Benchmark Suite: the first benchmark for FaaS computing that systematically covers a wide spectrum of cloud resources and applications. Our benchmark consists of the specification of representative workloads, the accompanying implementation and evaluation infrastructure, and the evaluation methodology that facilitates reproducibility and enables interpretability. We demonstrate that the abstract model of a FaaS execution environment ensures the applicability of our benchmark to multiple commercial providers such as AWS, Azure, and Google Cloud. Our work facilities experimental evaluation of serverless systems, and delivers a standardized, reliable and evolving evaluation methodology of performance, efficiency, scalability and reliability of middleware FaaS platforms.
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FaaS (Function as a Service) allows developers to upload and execute code in the cloud without managing servers. FaaS offerings from leading public cloud providers are based on system microVM or application container technologies such as Firecracker or Docker. In this paper, we demonstrate that lightweight high-level runtimes, such as WebAssembly, could offer performance and scaling advantages over existing solutions, and could enable finely-grained pay-as-you-use business models. We compared widely used performance benchmarks between Docker native and WebAssembly implementations of the same algorithms. We also discuss the barriers for WebAssembly adoption in serverless computing, such as the lack of tooling support.
Serverless computing has grown in popularity in recent years, with an increasing number of applications being built on Functions-as-a-Service (FaaS) platforms. By default, FaaS platforms support retry-based fault tolerance, but this is insufficient for programs that modify shared state, as they can unwittingly persist partial sets of updates in case of failures. To address this challenge, we would like atomic visibility of the updates made by a FaaS application. In this paper, we present AFT, an atomic fault tolerance shim for serverless applications. AFT interposes between a commodity FaaS platform and storage engine and ensures atomic visibility of updates by enforcing the read atomic isolation guarantee. AFT supports new protocols to guarantee read atomic isolation in the serverless setting. We demonstrate that aft introduces minimal overhead relative to existing storage engines and scales smoothly to thousands of requests per second, while preventing a significant number of consistency anomalies.
Serverless computing has rapidly grown following the launch of Amazons Lambda platform. Function-as-a-Service (FaaS) a key enabler of serverless computing allows an application to be decomposed into simple, standalone functions that are executed on a FaaS platform. The FaaS platform is responsible for deploying and facilitating resources to the functions. Several of todays cloud applications spread over heterogeneous connected computing resources and are highly dynamic in their structure and resource requirements. However, FaaS platforms are limited to homogeneous clusters and homogeneous functions and do not account for the data access behavior of functions before scheduling. We introduce an extension of FaaS to heterogeneous clusters and to support heterogeneous functions through a network of distributed heterogeneous target platforms called Function Delivery Network (FDN). A target platform is a combination of a cluster of homogeneous nodes and a FaaS platform on top of it. FDN provides Function-Delivery-as-a-Service (FDaaS), delivering the function to the right target platform. We showcase the opportunities such as varied target platforms characteristics, possibility of collaborative execution between multiple target platforms, and localization of data that the FDN offers in fulfilling two objectives: Service Level Objective (SLO) requirements and energy efficiency when scheduling functions by evaluating over five distributed target platforms using the FDNInspector, a tool developed by us for benchmarking distributed target platforms. Scheduling functions on an edge target platform in our evaluation reduced the overall energy consumption by 17x without violating the SLO requirements in comparison to scheduling on a high-end target platform.
Serverless computing has recently experienced significant adoption by several applications, especially Internet of Things (IoT) applications. In serverless computing, rather than deploying and managing dedicated virtual machines, users are able to deploy individual functions, and pay only for the time that their code is actually executing. However, since serverless platforms are relatively new, they have a completely different pricing model that depends on the memory, duration, and the number of executions of a sequence/workflow of functions. In this paper we present an algorithm that optimizes the price of serverless applications in AWS Lambda. We first describe the factors affecting price of serverless applications which include: (1) fusing a sequence of functions, (2) splitting functions across edge and cloud resources, and (3) allocating the memory for each function. We then present an efficient algorithm to explore different function fusion-placement solutions and find the solution that optimizes the applications price while keeping the latency under a certain threshold. Our results on image processing workflows show that the algorithm can find solutions optimizing the price by more than 35%-57% with only 5%-15% increase in latency. We also show that our algorithm can find non-trivial memory configurations that reduce both latency and price.
Edge computing has been developed to utilize multiple tiers of resources for privacy, cost and Quality of Service (QoS) reasons. Edge workloads have the characteristics of data-driven and latency-sensitive. Because of this, edge systems have developed to be both heterogeneous and distributed. The unique characteristics of edge workloads and edge systems have motivated EdgeBench, a workflow-based benchmark aims to provide the ability to explore the full design space of edge workloads and edge systems. EdgeBench is both customizable and representative. It allows users to customize the workflow logic of edge workloads, the data storage backends, and the distribution of the individual workflow stages to different computing tiers. To illustrate the usability of EdgeBench, we also implements two representative edge workflows, a video analytics workflow and an IoT hub workflow that represents two distinct but common edge workloads. Both workflows are evaluated using the workflow-level and function-level metrics reported by EdgeBench to illustrate both the performance bottlenecks of the edge systems and the edge workloads.
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