10 research outputs found
Serverless Computing for Scientific Applications
Serverless computing has become an important model in cloud computing and
influenced the design of many applications. Here, we provide our perspective on
how the recent landscape of serverless computing for scientific applications
looks like. We discuss the advantages and problems with serverless computing
for scientific applications, and based on the analysis of existing solutions
and approaches, we propose a science-oriented architecture for a serverless
computing framework that is based on the existing designs. Finally, we provide
an outlook of current trends and future directions
A Review of Deep Reinforcement Learning in Serverless Computing: Function Scheduling and Resource Auto-Scaling
In the rapidly evolving field of serverless computing, efficient function
scheduling and resource scaling are critical for optimizing performance and
cost. This paper presents a comprehensive review of the application of Deep
Reinforcement Learning (DRL) techniques in these areas. We begin by providing
an overview of serverless computing, highlighting its benefits and challenges,
with a particular focus on function scheduling and resource scaling. We then
delve into the principles of deep reinforcement learning (DRL) and its
potential for addressing these challenges. A systematic review of recent
studies applying DRL to serverless computing is presented, covering various
algorithms, models, and performances. Our analysis reveals that DRL, with its
ability to learn and adapt from an environment, shows promising results in
improving the efficiency of function scheduling and resource scaling in
serverless computing. However, several challenges remain, including the need
for more realistic simulation environments, handling of cold starts, and the
trade-off between learning time and scheduling performance. We conclude by
discussing potential future directions for this research area, emphasizing the
need for more robust DRL models, better benchmarking methods, and the
exploration of multi-agent reinforcement learning for more complex serverless
architectures. This review serves as a valuable resource for researchers and
practitioners aiming to understand and advance the application of DRL in
serverless computing
Serverless Architecture for Machine Learning
Serverless computing is an area under cloud computing which does not require individual management of cloud infrastructure and services. It is the groundwork behind Function as a Service or FaaS cloud computing technique. FaaS provides a stateless event-driven orchestration of functions and services for applications deployed in the cloud, without having to manage the servers and other infrastructure resources. This event driven architecture is being well utilized to manage different web-applications and services. Machine learning can bring a unique challenge to serverless computing, as it involves high-intensive tasks which requires voluminous data. In such a scenario it becomes essential to optimize the cloud-deployment architecture to obtain accurate results efficiently. In addition, serverless computing suffers from drawbacks like cold start etc., which further increases the need of researching different serverless provisioning tools and techniques. This research work aims to deploy a machine learning model to detect real-time crisis, using various serverless computing resources provided by notable cloud vendors like Amazon Web Services (AWS) and Google Cloud Platform (GCP). It also compares among the various methodologies available and later aims to build a training platform for machine learning tasks
Towards Cloud-Based cost-effective serverless information system
E-commerce information systems are becoming increasingly popular for businesses to adopt. In this work, we propose a serverless information system that will reduce costs for small businesses trying to create an e-commerce website. The proposed serverless system is built entirely in Amazon Web Services (AWS). The proposed serverless system allows businesses to pay for the use of cloud resources on a per-order granularity. This model reduces the cost of the information system when compared to a traditional cloud-based system. As e-commerce websites become more vital for small businesses, a cost effective serverless approach is promising
Dynamic Backup Workers for Parallel Machine Learning
The most popular framework for distributed training of machine learning models is the (synchronous) parameter server (PS). This paradigm consists of n workers, which iteratively compute updates of the model parameters, and a stateful PS, which waits and aggregates all updates to generate a new estimate of model parameters and sends it back to the workers for a new iteration. Transient computation slowdowns or transmission delays can intolerably lengthen the time of each iteration. An efficient way to mitigate this problem is to let the PS wait only for the fastest n − b updates, before generating the new parameters. The slowest b workers are called backup workers. The correct choice of the number b of backup workers depends on the cluster configuration and workload, but also (as we show in this paper) on the hyper-parameters of the learning algorithm and the current stage of the training. We propose DBW, an algorithm that dynamically decides the number of backup workers during the training process to maximize the convergence speed at each iteration. Our experiments show that DBW 1) removes the necessity to tune b by preliminary time-consuming experiments, and 2) makes the training up to a factor 3 faster than the optimal static configuration
TAXONOMY OF SECURITY AND PRIVACY ISSUES IN SERVERLESS COMPUTING
The advent of cloud computing has led to a new era of computer usage. Networking and physical security are some of the IT infrastructure concerns that IT administrators around the world had to worry about for their individual environments. Cloud computing took away that burden and redefined the meaning of IT administrators. Serverless computing as it relates to secure software development is creating the same kind of change. Developers can quickly spin up a secure development environment in a matter of minutes without having to worry about any of the underlying infrastructure setups. In the paper, we will look at the merits and demerits of serverless computing, what is drawing the demand for serverless computing among developers, the security and privacy issues of serverless technology, and detail the parameters to consider when setting up and using a secure development environment based on serverless computin
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