Resource provisioning in Science Clouds: Requirements and challenges

TL;DR

This paper discusses the unique requirements and challenges of resource provisioning in Science Clouds, emphasizing the need for specialized scheduling and allocation strategies to support scientific workloads effectively.

Contribution

It identifies key scheduling and resource allocation challenges specific to Science Clouds and discusses requirements for supporting scientific applications.

Findings

Science workloads have unique features requiring tailored resource management.

Effective scheduling is crucial for high-performance scientific applications.

Addressing these challenges can improve Science Cloud efficiency and support for scientific research.

Abstract

Cloud computing has permeated into the information technology industry in the last few years, and it is emerging nowadays in scientific environments. Science user communities are demanding a broad range of computing power to satisfy the needs of high-performance applications, such as local clusters, high-performance computing systems, and computing grids. Different workloads are needed from different computational models, and the cloud is already considered as a promising paradigm. The scheduling and allocation of resources is always a challenging matter in any form of computation and clouds are not an exception. Science applications have unique features that differentiate their workloads, hence, their requirements have to be taken into consideration to be fulfilled when building a Science Cloud. This paper will discuss what are the main scheduling and resource allocation challenges for any Infrastructure as a Service provider supporting scientific applications.