Related papers: Kernel-as-a-Service: A Serverless Interface to GPU…
GPUs are widely used to accelerate many important classes of workloads today. However, we observe that several important emerging classes of workloads, including simulation engines for deep reinforcement learning and dynamic neural…
Serverless computing (FaaS) has been extensively utilized for deep learning (DL) inference due to the ease of deployment and pay-per-use benefits. However, existing FaaS platforms utilize GPUs in a coarse manner for DL inferences, without…
Edge computing has become a popular paradigm where services and applications are deployed at the network edge closer to the data sources. It provides applications with outstanding benefits, including reduced response latency and enhanced…
Serverless computing has emerged as an attractive paradigm due to the efficiency of development and the ease of deployment without managing any underlying infrastructure. Nevertheless, serverless computing approaches face numerous…
Graphics processors, or GPUs, have recently been widely used as accelerators in the shared environments such as clusters and clouds. In such shared environments, many kernels are submitted to GPUs from different users, and throughput is an…
We propose a server-based approach to manage a general-purpose graphics processing unit (GPU) in a predictable and efficient manner. Our proposed approach introduces a GPU server that is a dedicated task to handle GPU requests from other…
Serverless computing with cloud functions is quickly gaining adoption, but constrains programmers with its limited support for state management. We introduce a shared file system for cloud functions. It offers familiar POSIX semantics while…
Automatic network management strategies have become paramount for meeting the needs of innovative real-time and data-intensive applications, such as in the Internet of Things. However, meeting the ever-growing and fluctuating demands for…
It is increasingly common to outsource network functions (NFs) to the cloud. However, no cloud providers offer NFs-as-a-Service (NFaaS) that allows users to run custom NFs. Our work addresses how a cloud provider can offer NFaaS. We use the…
The most popular heterogeneous many-core platform, the CPU+GPU combination, has received relatively little attention in operating systems research. This platform is already widely deployed: GPUs can be found, in some form, in most desktop…
Serverless computing has emerged as a new paradigm for running short-lived computations in the cloud. Due to its ability to handle IoT workloads, there has been considerable interest in running serverless functions at the edge. However, the…
The usage of large language models (LLMs) has grown increasingly fragmented, with no single model dominating. Meanwhile, cloud providers offer a wide range of mid-tier and older-generation GPUs that enjoy better availability and deliver…
Serverless computing has become a major trend among cloud providers. With serverless computing, developers fully delegate the task of managing the servers, dynamically allocating the required resources, as well as handling availability and…
Function-as-a-Service (FaaS) is a growing cloud computing paradigm that is expected to reduce the user cost of service over traditional serverful approaches. However, the environmental impact of FaaS has not received much attention. We…
Machine Learning as a Service (MLaaS) has become a growing trend in recent years and several such services are currently offered. MLaaS is essentially a set of services that provides machine learning tools and capabilities as part of cloud…
Serverless computing (also known as functions as a service) is a new cloud computing abstraction that makes it easier to write robust, large-scale web services. In serverless computing, programmers write what are called serverless…
The Function-as-a-Service (FaaS) execution model increases developer productivity by removing operational concerns such as managing hardware or software runtimes. Developers, however, still need to partition their applications into FaaS…
Machine learning (ML) is an important part of modern data science applications. Data scientists today have to manage the end-to-end ML life cycle that includes both model training and model serving, the latter of which is essential, as it…
Next generation technologies such as smart healthcare, self-driving cars, and smart cities require new approaches to deal with the network traffic generated by the Internet of Things (IoT) devices, as well as efficient programming models to…
Multimedia conferencing is the real-time exchange of multimedia content between multiple parties. It is the basis of a wide range of applications (e.g., multimedia multiplayer game). Cloud-based provisioning of the conferencing services on…