相关论文: Serverless inferencing on Kubernetes

Model autoscaling is the key mechanism to achieve serverless model-as-a-service, but it faces a fundamental trade-off between scaling speed and storage/memory usage to cache parameters, and cannot meet frequent scaling requirements across…

Distributed training frameworks, like TensorFlow, have been proposed as a means to reduce the training time of deep learning models by using a cluster of GPU servers. While such speedups are often desirable---e.g., for rapidly evaluating…

Data lakes hold a growing amount of cold data that is infrequently accessed, yet require interactive response times. Serverless functions are seen as a way to address this use case since they offer an appealing alternative to maintaining…

Serverless computing, also referred to as Function-as-a-Service (FaaS), is a cloud computing model that has attracted significant attention and has been widely adopted in recent years. The serverless computing model offers an intuitive,…

In recent years Serverless Computing has emerged as a compelling cloud based model for the development of a wide range of data-intensive applications. However, rapid container provisioning introduces non-trivial challenges for FaaS cloud…

With the proliferation of machine learning (ML) libraries and frameworks, and the programming languages that they use, along with operations of data loading, transformation, preparation and mining, ML model development is becoming a…

In today's Function-as-a-Service offerings, a programmer is usually responsible for configuring function memory for its successful execution, which allocates proportional function resources such as CPU and network. However, right-sizing the…

The exponential growth of Internet of Things (IoT) has given rise to a new wave of edge computing due to the need to process data on the edge, closer to where it is being produced and attempting to move away from a cloud-centric…

Serverless technologies, also known as FaaS (Function as a Service), are promoted as solutions that provide dynamic scalability, speed of development, cost-per-consumption model, and the ability to focus on the code while taking attention…

Serverless computing, with its operational simplicity and on-demand scalability, has become a preferred paradigm for deploying workflow applications. However, resource allocation for workflows, particularly those with branching structures,…

Serverless computing has emerged as an attractive deployment option for cloud applications in recent times. The unique features of this computing model include, rapid auto-scaling, strong isolation, fine-grained billing options and access…

Function-as-a-Service (FaaS) is one of the most promising directions for the future of cloud services, and serverless functions have immediately become a new middleware for building scalable and cost-efficient microservices and…

Serverless computing is an emerging cloud computing paradigm for developing applications at the function level, known as serverless functions. Due to the highly dynamic execution environment, multiple identical runs of the same serverless…

Function-as-a-Service (FaaS) has raised a growing interest in how to "tame" serverless computing to enable domain-specific use cases such as data-intensive applications and machine learning (ML), to name a few. Recently, several systems…

Deep learning as a means to inferencing has proliferated thanks to its versatility and ability to approach or exceed human-level accuracy. These computational models have seemingly insatiable appetites for computational resources not only…

Serverless Computing (FaaS) has become a popular paradigm for deep learning inference due to the ease of deployment and pay-per-use benefits. However, current serverless inference platforms encounter the coarse-grained and static GPU…

Serverless clouds promise efficient scaling, reduced toil and monetary costs. Yet, serverless-ing a complex, legacy application might require major refactoring and thus is risky. As a case study, we use Airflow, an industry-standard…

Since the increasing popularity of large language model (LLM) backend systems, it is common and necessary to deploy stable serverless serving of LLM on multi-GPU clusters with autoscaling. However, there exist challenges because the…

Distributed machine learning systems require strong privacy guarantees, verifiable compliance, and scalable deployment across heterogeneous and multi-cloud environments. This work introduces a cloud-native privacy-preserving architecture…

Serverless cloud is an innovative cloud service model that frees customers from most cloud management duties. It also offers the same advantages as other cloud models but at much lower costs. As a result, the serverless cloud has been…