Related papers: Serverless Query Processing with Flexible Performa…
In recent years, extensive research has been conducted in the area of Service Level Agreement (SLA) for utility computing systems. An SLA is a formal contract used to guarantee that consumers' service quality expectation can be achieved. In…
Serverless computing provides just-in-time infrastructure provisioning with rapid elasticity and a finely-grained pricing model. As full control of resource allocation is in the hands of the cloud provider and applications only consume…
In recent years, there has been a surge in the adoption of serverless computing due to the ease of deployment, attractive pay-per-use pricing, and transparent horizontal auto-scaling. At the same time, infrastructure advancements such as…
The development of cloud infrastructures inspires the emergence of cloud-native computing. As the most promising architecture for deploying microservices, serverless computing has recently attracted more and more attention in both industry…
Serverless computing, also known as Functions-as-a-Service, is a recent paradigm aimed at simplifying the programming of cloud applications. The idea is that developers design applications in terms of functions, which are then deployed on a…
Recent developments in Generative AI, Computer Vision, and Natural Language Processing have led to an increased integration of AI models into various products. This widespread adoption of AI requires significant efforts in deploying these…
Elastic scaling is one of the central benefits provided by serverless platforms, and requires that they scale resource up and down in response to changing workloads. Serverless platforms scale-down resources by terminating previously…
The increasing demand for computational power in big data and machine learning has driven the development of distributed training methodologies. Among these, peer-to-peer (P2P) networks provide advantages such as enhanced scalability and…
The topic of serverless computing has proved to be a controversial subject both within academic and industrial communities. Many have praised the approach to be a platform for a new era of computing and some have argued that it is in fact a…
Running data analytics queries on serverless (FaaS) workers has been shown to be cost- and performance-efficient for a variety of real-world scenarios, including intermittent query arrival patterns, sudden load spikes and management…
Many cloud service providers (CSPs) provide on-demand service at a price with a small delay. We propose a QoS-differentiated model where multiple SLAs deliver both on-demand service for latency-critical users and delayed services for…
Serverless computing has gained popularity in edge computing due to its flexible features, including the pay-per-use pricing model, auto-scaling capabilities, and multi-tenancy support. Complex Serverless-based applications typically rely…
Data is found everywhere, from health and human infrastructure to the surge of sensors and the proliferation of internet-connected devices. To meet this challenge, the data engineering field has expanded significantly in recent years in…
Function-as-a-Service (FaaS) is at the core of serverless computing, enabling developers to easily deploy applications without managing computing resources. With an Infrastructure-as-Code (IaC) approach, frameworks like the Serverless…
Function-as-a-Service (FaaS) allows to directly submit function code to a cloud provider without the burden of managing infrastructure resources. Each cloud provider establishes execution time limits to their FaaS offerings, which impose…
The serverless computing model has evolved as one of the key solutions in the cloud for fast autoscaling and capacity planning. In edge computing environments, however, the serverless model is challenged by the system heterogeneity and…
The widespread deployment of large-scale, compute-intensive applications such as high-performance computing, artificial intelligence, and big data is leading to convergence between cloud and high-performance computing infrastructures. Cloud…
Internet-scale web applications are becoming increasingly storage-intensive and rely heavily on in-memory object caching to attain required I/O performance. We argue that the emerging serverless computing paradigm provides a well-suited,…
The traditional cloud-centric approach for Deep Learning (DL) requires training data to be collected and processed at a central server which is often challenging in privacy-sensitive domains like healthcare. Towards this, a new learning…
We present a framework for scheduling multifunction serverless applications over a hybrid public-private cloud. A set of serverless jobs is input as a batch, and the objective is to schedule function executions over the hybrid platform to…