Related papers: Serverless Approach to Running Resource-Intensive …
Aggregated HPC resources have rigid allocation systems and programming models which struggle to adapt to diverse and changing workloads. Consequently, HPC systems fail to efficiently use the large pools of unused memory and increase the…
Serverless computing has become very popular today since it largely simplifies cloud programming. Developers do not need to longer worry about provisioning or operating servers, and they pay only for the compute resources used when their…
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…
Serverless computing is an excellent fit for big data processing because it can scale quickly and cheaply to thousands of parallel functions. Existing serverless platforms isolate functions in ephemeral, stateless containers, preventing…
Serverless computing allows developers to break their code into small components, known as functions, which are automatically managed by a service provider. Being lightweight and modular, serverless functions have been increasingly employed…
This paper presents ServerlessLLM, a distributed system designed to support low-latency serverless inference for Large Language Models (LLMs). By harnessing the substantial near-GPU storage and memory capacities of inference servers,…
Serverless computing is an emerging cloud paradigm that offers an elastic and scalable allocation of computing resources with pay-as-you-go billing. In the Function-as-a-Service (FaaS) programming model, applications comprise short-lived…
Serverless computing has recently experienced significant adoption by several applications, especially Internet of Things (IoT) applications. In serverless computing, rather than deploying and managing dedicated virtual machines, users are…
Recent years have witnessed increasing interest in machine learning inferences on serverless computing for its auto-scaling and cost effective properties. Existing serverless computing, however, lacks effective job scheduling methods to…
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…
Graph processing systems are essential for analyzing large-scale data with complex relationships, yet most existing frameworks rely on statically provisioned clusters, resulting in poor elasticity and inefficient resource utilization under…
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…
Iterative improvement of model architectures is fundamental to deep learning: Transformers first enabled scaling, and recent advances in model hybridization have pushed the quality-efficiency frontier. However, optimizing architectures…
Serverless computing platforms currently rely on basic pricing schemes that are static and do not reflect customer feedback. This leads to significant inefficiencies from a total utility perspective. As one of the fastest-growing cloud…
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…
Context. While in serverless computing, application resource management and operational concerns are generally delegated to the cloud provider, ensuring that serverless applications meet their performance requirements is still a…
Recent Serverless workloads tend to be largescaled/CPU-memory intensive, such as DL, graph applications, that require dynamic memory-to-compute resources provisioning. Meanwhile, recent solutions seek to design page management strategies…
High performance is needed in many computing systems, from batch-managed supercomputers to general-purpose cloud platforms. However, scientific clusters lack elastic parallelism, while clouds cannot offer competitive costs for…
Serverless computing has emerged as a compelling new paradigm of cloud computing models in recent years. It promises the user services at large scale and low cost while eliminating the need for infrastructure management. On cloud provider…
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…