General Framework for Task Scheduling and Resource Provisioning in Cloud Computing Systems

Abstract

Clouds have become an important platform to deliver services for various applications. Task scheduling and resource provisioning are key components to improve system performance under provisioned resources and satisfy users' demands for quality of service (QoS). To address the diversity of cloud services and applications, much of recent research and development efforts have been engaged in designing and implementing scheduling strategies and algorithms for specific tasks, such as dependent or independent tasks, fault-tolerant tasks with real-time deadlines or energy-efficient tasks. However, these task scheduling and resource provisioning schemes, though optimized with specific objectives, suffer from several inherent problems in cloud execution environments. In this paper, we propose a general framework for task scheduling and resource provisioning in cloud computing systems with dynamic customizability. By utilizing software engineering framework as the design guideline, we incorporate multiple scheduling objectives and multiple types of tasks to be processed under varied resource constraints to enable cloud applications to dynamically select and assemble scheduling strategies and algorithms according to different runtime QoS requirements. We illustrate the flexibility and customizability of our framework through two example scheduling algorithms: EASU and RAS. We validate the ffectiveness of our proposed framework through experimental evaluation of the effectiveness of our proposed algorithms using both simulation and in real cloud platforms.