A Survey and Taxonomy of Resource Optimisation for Executing Bag-of-Task Applications on Public Clouds

TL;DR

This survey reviews existing research on resource optimization strategies for executing Bag-of-Task applications on public cloud platforms, highlighting taxonomy, current trends, and future directions.

Contribution

It provides a comprehensive taxonomy and analysis of current frameworks for optimizing BoT application execution on cloud resources, and discusses future research directions.

Findings

Existing frameworks are categorized into a taxonomy for comparison.

Current trends include dynamic resource provisioning and cost-performance trade-offs.

Future research emphasizes automation and adaptive optimization techniques.

Abstract

Cloud computing has been widely adopted due to the flexibility in resource provisioning and on-demand pricing models. Entire clusters of Virtual Machines (VMs) can be dynamically provisioned to meet the computational demands of users. However, from a user's perspective, it is still challenging to utilise cloud resources efficiently. This is because an overwhelmingly wide variety of resource types with different prices and significant performance variations are available. This paper presents a survey and taxonomy of existing research in optimising the execution of Bag-of-Task applications on cloud resources. A BoT application consists of multiple independent tasks, each of which can be executed by a VM in any order; these applications are widely used by both the scientific communities and commercial organisations. The objectives of this survey are as follows: (i) to provide the reader with a concise understanding of existing research on optimising the execution of BoT applications on the cloud, (ii) to define a taxonomy that categorises current frameworks to compare and contrast them, and (iii) to present current trends and future research directions in the area.