Faa$T

Romero, Francisco; Chaudhry, Gohar Irfan; Goiri, Íñigo; Gopa, Pragna; Batum, Paul; Yadwadkar, Neeraja J.; Fonseca, Rodrigo; Kozyrakis, Christos; Bianchini, Ricardo

doi:10.1145/3472883.3486974

Cited by 64 publications

(17 citation statements)

References 16 publications

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“…The edge nodes are selected randomly from the set of nodes in the topology. Workload: For the workload, we adopted a trace-driven approach starting from a dataset of traces obtained in 2020 from a production system and publicly released by Microsoft [22]. The dataset contains a log of read/write backend activities of user applications, with associated geographical regions.…”

Section: A Methodology Assumptions and Toolsmentioning

confidence: 99%

FaaS execution models for edge applications

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Conti

Passarella

2022

Pervasive and Mobile Computing

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Section: A Methodology Assumptions and Toolsmentioning

confidence: 99%

FaaS execution models for edge applications

Cicconetti

Conti

Passarella

2022

Pervasive and Mobile Computing

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“…As a result, a larger number of containers may be required to host more serverless functions, thereby raising the cost of infrastructure leasings, such as VM or pods. Previous studies have investigated several metrics to assess autoscaling solutions from a cost perspective, including resource cost [22,23], overhead cost [24,25], and additional serverful cost [26]. Resource cost pertains to the number of containers activated as invokers, workers, or servers to handle workloads.…”

Section: Costmentioning

confidence: 99%

Autoscaling in Serverless Computing : Taxonomy and OpenChallenges

Jawaddi

Ismail

2023

Preprint

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The popularity of serverless computing has been fueled by its operational simplicity, pay-per-use pricing model, and the ability to autoscale. However, there is a lack of comprehensive reviews that focus on the autoscaling context in serverless computing. In this paper, we address this gap by proposing a taxonomy of autoscaling properties for serverless computing. To gather relevant information, we review recent contributions on autoscaling in serverless computing from 2018 to 2022. Using the proposed taxonomy, we analyze the existing autoscaling solutions. Our analysis reveals that the scaling objectives explored by researchers are limited to certain elements, and the existing serverless autoscaling approaches do not provide guarantees that the scaling policies or strategies can meet the Service Level Agreement (SLA) requirements. We conclude by recommending open challenges from three perspectives: verification of autoscaling, energy-driven autoscaling, and anomaly-aware autoscaling. These challenges highlight the need for future research to address the limitations of existing autoscaling approaches and provide more robust and reliable autoscaling mechanisms for serverless computing.

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“…This state can be used to cache locally the state that is expensive to obtain or compute [19], [20]. Recent proposals further extend FaaS platforms so that storage accesses are transparently cached within function containers [37], [38]. As cache hits are served from local memory, applications can see speedups of up to 92% [38].…”

Section: Palette Load Balancing: Locality Hints For Serverless Functionsmentioning

confidence: 99%

“…As a second example, we look at a FaaS implementation of Dask [42], where each DAG node is executed as a serverless invocation. We use the Faa$T [38] distributed serverless cache running on the Azure Functions host [43], and augment its interface to also take locality hints for intermediate DAG data. Our results show that Palette reduces run times by 46% and 18% on Task Bench and TPC-H, respectively, compared to a locality-oblivious FaaS platform.…”

Section: Palette Load Balancing: Locality Hints For Serverless Functionsmentioning

confidence: 99%

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Informed optimization of cloud applications

Abdi

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Cloud applications running in private data centers or public clouds are critical to every aspect of modern society, from entertainment to healthcare to financial services. The rapid growth of cloud applications is because organizations extensively leverage the general-purpose services offered by cloud platforms. Examples are general-purpose storage services such as AWS S3 [4]. These general-purpose services promise to provide high-performance and low-cost data analysis, elastic and scalable resources, and robust and reliable computations. Yet, cloud applications confront challenges to achieving these promises for several reasons: the exponential growth of data, the network limitations, the overhead of accessing the remote storage service, and the scarcity of complete information to make optimized decisions at different layers of the applications.This dissertation focuses on proposing systems to address the challenges mentioned above.We describe two systems that expand the general-purpose service APIs to exchange information critical to optimizing decisions. We also introduce components and algorithms that interpret the exchanged information and make optimized decisions.First, we describe Kariz to address the challenge of providing high-performance and scalable analytic applications with disaggregated storage-compute deployments. Kariz targets cloud applications that leverage general-purpose services such as analytical frameworks and object storage services. Kariz introduces a distributed caching system that extends the general-purpose object storage service and caches and prefetches input data accessed by the analytical framework. It extends the analytic framework APIs to exchange job dependency graphs (DAGs) and scheduling information with the cache, makes I/O scheduling decisions according to the scheduling information, estimates the run-time improvements, and predicts bandwidth to object storage service. Kariz integrates a cross-DAG scheduler and prefers pre-fetching input data shared by multiple DAGs. To build and evaluate Kariz, we characterize the job dependency graphs(DAGs) generated by analytic frameworks in a workload from in-production clusters.Then, we describe the elements of Kariz: a straggler-resistant partial cache system, a cache controller that run alongside the analytic frameworks-i.e. Spark and Pig-, and two graph-aware caching and pre-fetching algorithm for optimizing the caching of individual queries and three policies for optimizing across queries from, potentially, multiple different clusters. With an vi algorithm that fully exploits the rich information available from Kariz, we demonstrate major speedups (as much as 3×) for TPC-H and TPC-DS.Second, we describe Palette, to address the growing challenge of having computation close to data in general purpose Function-as-a-Service (FaaS) serverless computing. FaaS platforms enable a simple programming model with almost unbounded elasticity, making them an exciting choice for most cloud applications. However, current FaaS platforms achieve this...

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Faa$T

Cited by 64 publications

References 16 publications

FaaS execution models for edge applications

FaaS execution models for edge applications

Autoscaling in Serverless Computing : Taxonomy and OpenChallenges

Informed optimization of cloud applications

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