909 research outputs found
SEUSS: rapid serverless deployment using environment snapshots
Modern FaaS systems perform well in the case of repeat executions when function working sets stay small. However, these platforms are less effective when applied to more complex, large-scale and dynamic workloads. In this paper, we introduce SEUSS (serverless execution via unikernel snapshot stacks), a new system-level approach for rapidly deploying serverless functions. Through our approach, we demonstrate orders of magnitude improvements in function start times and cacheability, which improves common re-execution paths while also unlocking previously-unsupported large-scale bursty workloads.Published versio
On-demand serverless video surveillance with optimal deployment of deep neural networks
[EN] We present an approach to optimally deploy Deep Neural Networks (DNNs) in serverless cloud architectures.
A serverless architecture allows running code in response to events, automatically managing the required
computing resources. However, these resources have limitations in terms of execution environment (CPU
only), cold starts, space, scalability, etc. These limitations hinder the deployment of DNNs, especially
considering that fees are charged according to the employed resources and the computation time. Our
deployment approach is comprised of multiple decoupled software layers that allow effectively managing
multiple processes, such as business logic, data access, and computer vision algorithms that leverage DNN
optimization techniques. Experimental results in AWS Lambda reveal its potential to build cost-effective ondemand
serverless video surveillance systems.This work has been partially supported by the program ELKARTEK 2019 of the Basque Government under project AUTOLIB
Rise of the Planet of Serverless Computing: A Systematic Review
Serverless computing is an emerging cloud computing paradigm, being adopted to develop a wide range of software applications.
It allows developers to focus on the application logic in the granularity of function, thereby freeing developers from tedious and
error-prone infrastructure management. Meanwhile, its unique characteristic poses new challenges to the development and deployment
of serverless-based applications. To tackle these challenges, enormous research efforts have been devoted. This paper provides a
comprehensive literature review to characterize the current research state of serverless computing. Specifically, this paper covers 164
papers on 17 research directions of serverless computing, including performance optimization, programming framework, application
migration, multi-cloud development, testing and debugging, etc. It also derives research trends, focus, and commonly-used platforms
for serverless computing, as well as promising research opportunities
FedLesScan: Mitigating Stragglers in Serverless Federated Learning
Federated Learning (FL) is a machine learning paradigm that enables the
training of a shared global model across distributed clients while keeping the
training data local. While most prior work on designing systems for FL has
focused on using stateful always running components, recent work has shown that
components in an FL system can greatly benefit from the usage of serverless
computing and Function-as-a-Service technologies. To this end, distributed
training of models with serverless FL systems can be more resource-efficient
and cheaper than conventional FL systems. However, serverless FL systems still
suffer from the presence of stragglers, i.e., slow clients due to their
resource and statistical heterogeneity. While several strategies have been
proposed for mitigating stragglers in FL, most methodologies do not account for
the particular characteristics of serverless environments, i.e., cold-starts,
performance variations, and the ephemeral stateless nature of the function
instances. Towards this, we propose FedLesScan, a novel clustering-based
semi-asynchronous training strategy, specifically tailored for serverless FL.
FedLesScan dynamically adapts to the behaviour of clients and minimizes the
effect of stragglers on the overall system. We implement our strategy by
extending an open-source serverless FL system called FedLess. Moreover, we
comprehensively evaluate our strategy using the 2nd generation Google Cloud
Functions with four datasets and varying percentages of stragglers. Results
from our experiments show that compared to other approaches FedLesScan reduces
training time and cost by an average of 8% and 20% respectively while utilizing
clients better with an average increase in the effective update ratio of
17.75%.Comment: IEEE BigData 202
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