serverless computing has recently attracted a lot of attention from research and industry due to its promise of ultimate elasticity and operational simplicity. However, there is no consensus yet on whether or not the ...
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ISBN:
(纸本)9781450367356
serverless computing has recently attracted a lot of attention from research and industry due to its promise of ultimate elasticity and operational simplicity. However, there is no consensus yet on whether or not the approach is suitable for data processing. In this paper, we present Lambada, a serverless distributed data processing framework designed to explore how to perform data analytics on serverless computing. In our analysis, supported with extensive experiments, we show in which scenarios serverless makes sense from an economic and performance perspective. We address several important technical questions that need to be solved to support data analytics and present examples from several domains where serverless offers a cost and performance advantage over existing solutions.
The growing demand for video processing and the advantages in scalability and cost reduction brought by the emerging serverless computing have attracted significant attention in serverless computing powered video proc...
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ISBN:
(纸本)9781450362986
The growing demand for video processing and the advantages in scalability and cost reduction brought by the emerging serverless computing have attracted significant attention in serverless computing powered video processing. However, how to implement and configure serverless functions to optimize the performance and cost of video processing applications remains unclear. In this paper, we explore the configuration and implementation schemes of typical video processing functions deployed to the serverless platforms and quantify their influence on the execution duration and monetary cost from a developer's perspective. Our measurement reveals that memory configuration is non-trivial. Dynamic profiling of workloads is necessary to find the best memory configuration. Moreover, compared with calling external video processing APIs, implementing these services locally in serverless functions can be competitive. We also find that the performance of video processing applications could be affected by the underlying infrastructure. Our work provides guidelines for further function-level optimization and complements the existing measurement studies for both serverless computing and video processing.
The increasing number and scale of federated learning (FL) jobs necessitates resource efficient scheduling and management of aggregation to make the economics of cloud-hosted aggregation work. Existing FL research has...
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ISBN:
(纸本)9781665455800
The increasing number and scale of federated learning (FL) jobs necessitates resource efficient scheduling and management of aggregation to make the economics of cloud-hosted aggregation work. Existing FL research has focused on the design of FL algorithms and optimization, and less on aggregation efficacy. In this paper, we propose a new FL aggregation paradigm - "just-in-time" (JIT) aggregation that leverages unique properties of FL jobs, especially the periodicity of model updates, to defer aggregation as much as possible and free compute resources for other FL jobs or other datacenter workloads. We describe a novel way to prioritize FL jobs for aggregation, and demonstrate using multiple datasets, models and FL aggregation algorithms that our techniques can reduce resource usage by 60+% when compared to eager aggregation used in existing FL platforms. We demonstrate that using JIT aggregation has negligible overhead and impact on the latency of the FL job.
serverless computing is an ever-growing programming paradigm being adopted by developers all over the world. Its highly scalable, automatic load balancing, and pay for what you use design is a powerful tool that can a...
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serverless computing is an ever-growing programming paradigm being adopted by developers all over the world. Its highly scalable, automatic load balancing, and pay for what you use design is a powerful tool that can also greatly reduce operational costs. However, these advantages also leave serverless computing open to a unique threat, Denial-of-Wallet (DoW). It is the intentional targeting of serverless function endpoints with request traffic in order to artificially raise the usage bills for the application owner. A subset of these attacks are leeches. They perform DoW at a rate that could go undetected as it is not a sudden violent influx of requests. We devise a means of detecting such attacks by utilizing a novel approach of representing request traffic as heat maps and training an image classification algorithm to distinguish between normal and malicious traffic behaviour. Our classifier utilizes convolutional neural networks and achieves 97.98% accuracy. We then design a system for the implementation of this model that would allow application owners to monitor their traffic in real time for suspicious behaviour.
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