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Hardware accelerators like GPUs are now ubiquitous in data centers, but are not fully supported by common cloud abstractions such as Functions as a Service (FaaS). Many popular and emerging FaaS applications such as machine learning and…
Despite existing work in machine learning inference serving, ease-of-use and cost efficiency remain challenges at large scales. Developers must manually search through thousands of model-variants -- versions of already-trained models that…
Functions-as-a-Service (FaaS) is a Serverless Cloud paradigm where a platform manages the execution scheduling (e.g., resource allocation, runtime environments) of stateless functions. Recent developments demonstrate the benefits of using…
Function-as-a-Service (FaaS) is emerging as an important cloud computing service model as it can improve the scalability and usability of a wide range of applications, especially Machine-Learning (ML) inference tasks that require scalable…
This paper first presents a parallel solution for the Flowshop Scheduling Problem in parallel environment, and then proposes a novel load balancing strategy. The proposed Proportional Fairness Strategy (PFS) takes computational performance…
Integrating GPUs into serverless computing platforms is crucial for improving efficiency. However, existing solutions for GPU-enabled serverless computing platforms face two significant problems due to coarse-grained GPU management: long…
Most cloud platforms have a Function-as-a-Service (FaaS) offering that enables users to easily write highly scalable applications. To better understand how the platform's architecture impacts its performance, we present a research-focused…
The rapid growth of data generated from Internet of Things (IoTs) such as smart phones and smart home devices presents new challenges to cloud computing in transferring, storing, and processing the data. With increasingly more powerful edge…
Processing-using-DRAM (PUD) is a paradigm where the analog operational properties of DRAM are used to perform bulk logic operations. While PUD promises high throughput at low energy and area cost, we uncover three limitations of existing…
The increasing momentum of service-oriented architecture has led to the emergence of divergent delivered services, where service selection is meritedly required to obtain the target service fulfilling the requirements from both users and…
Current Serverless abstractions (e.g., FaaS) poorly support non-functional requirements (e.g., QoS and constraints), are provider-dependent, and are incompatible with other cloud abstractions (e.g., databases). As a result, application…
As particle accelerator control systems evolve in complexity and scale, the need for responsive, scalable, and cost-effective computational infrastructure becomes increasingly critical. Function-as-a-Service (FaaS) offers an alternative to…
Modern cloud computing advances have been pressing application modernization in the last 15 years, stressing the need for application redesign towards the use of more distributed and ephemeral resources. From the initial IaaS and PaaS…
Dynamic offloading of Machine Learning (ML) model partitions across different resource orchestration services, such as Function-as-a-Service (FaaS) and Infrastructure-as-a-Service (IaaS), can balance processing and transmission delays while…
Modern scientific applications are increasingly decomposable into individual functions that may be deployed across distributed and diverse cyberinfrastructure such as supercomputers, clouds, and accelerators. Such applications call for new…
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…
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…
We introduce Diffuse, a system that dynamically performs task and kernel fusion in distributed, task-based runtime systems. The key component of Diffuse is an intermediate representation of distributed computation that enables the necessary…
Serverless computing promises convenient abstractions for developing and deploying functions that execute in response to events. In such Function-as-a-Service (FaaS) platforms, scheduling is an integral task, but current scheduling…
Existing serverless data analytics systems rely on external storage services like S3 for data shuffling and communication between cloud functions. While this approach provides the elasticity benefits of serverless computing, it incurs…