Related papers: Kernel-as-a-Service: A Serverless Interface to GPU…
Application energy efficiency can be improved by executing each application component on the compute element that consumes the least energy while also satisfying time constraints. In principle, the function as a service (FaaS) paradigm…
Function-as-a-service (FaaS) is a popular serverless computing paradigm for developing event-driven functions that elastically scale on public clouds. FaaS workflows, such as AWS Step Functions and Azure Durable Functions, are composed from…
Organisations are increasingly putting machine learning models into production at scale. The increasing popularity of serverless scale-to-zero paradigms presents an opportunity for deploying machine learning models to help mitigate…
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 increasing use of hardware processing accelerators tailored for specific applications, such as the Vision Processing Unit (VPU) for image recognition, further increases developers' configuration, development, and management overhead.…
Serverless functions provide high levels of parallelism, short startup times, and "pay-as-you-go" billing. These attributes make them a natural substrate for data analytics workflows. However, the impossibility of direct communication…
Serverless computing, in particular the Function-as-a-Service (FaaS) execution model, has recently shown to be effective for running large-scale computations. However, little attention has been paid to highly-parallel applications with…
Serverless platforms face a trade-off: conventional cluster managers like Kubernetes offer compatibility for co-locating Function-as-a-Service (FaaS) and Backend-as-a-Service (BaaS) components of serverless applications, at the cost of high…
Due to the increased number of parameters and data in the pre-trained model exceeding a certain level, a foundation model (e.g., a large language model) can significantly improve downstream task performance and emerge with some novel…
The Infrastructure-as-a-Service (IaaS) model of cloud computing is a promising approach towards building elastically scaling systems. Unfortunately, building such applications today is a complex, repetitive and error-prone endeavor, as IaaS…
Data processing systems are increasingly deployed in the cloud. While monolithic systems run fully on virtual servers, recent systems embrace cloud infrastructure and utilize the disaggregation of compute and storage to scale them…
RDMA is increasingly adopted by cloud computing platforms to provide low CPU overhead, low latency, high throughput network services. On the other hand, however, it is still challenging for developers to realize fast deployment of…
Disaggregation maps parts of an AI workload to different types of GPUs, offering a path to utilize modern heterogeneous GPU clusters. However, existing solutions operate at a coarse granularity and are tightly coupled to specific model…
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…
Function-as-a-Service (FaaS) is a cloud service model enabling developers to offload event-driven executable snippets of code. The execution and management of such functions becomes a FaaS provider's responsibility, hereby included their…
Function-as-a-Service is a novel type of cloud service used for creating distributed applications and utilizing computing resources. Application developer supplies source code of cloud functions, which are small applications or application…
Cloud computing has achieved great success in modern IT industry as an excellent computing paradigm due to its flexible management and elastic resource sharing. To date, cloud computing takes an irrepalceable position in our socioeconomic…
Serverless computing relies on extreme multi-tenancy to remain economically viable, driving providers to rely on virtual machines (VMs) that ensure strong isolation and seamless ecosystem compatibility with the FaaS programming model.…
Due to the surge in the volume of data generated and rapid advancement in Artificial Intelligence (AI) techniques like machine learning and deep learning, the existing traditional computing models have become inadequate to process an…
Cloud-based Artificial Intelligence (AI) inference is increasingly latency- and context-sensitive, yet today's AI-as-a-Service is typically consumed as an application-chosen endpoint, leaving the network to provide only best-effort…