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Previous studies on task scheduling for cloud computing mostly focused on either energy saving or upgrading performance only.
Our CPN based scheme addresses two key issues on task scheduling problems, dependence detecting and task dispatching.
This paper focuses on task scheduling using a multi-objective nested Particle Swarm Optimization(TSPSO) to optimize energy and processing time.
It generates a forwarding structure on task scheduling that improves outputs' quality.
There is also a significant body of literature on task scheduling in the cloud mostly focusing on traditional cloud systems.
Although security issues are also of concern for using cloud computing environments, we focus on task scheduling mechanisms to improve the performance and server utilization.
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We ponder the problem of task scheduling on a multi-core processor with software controlled DVS where the objective is to reduce the energy consumption.
In this paper, we focus on resource provisioning at the SaaS level to study the effect of task scheduling schemes on the energy efficiency of a data center.
In addition, in order to satisfy the utility acquirement and the energy budget, Wu et al. [19] proposed a unimodal arbitrary arrival with energy bounds algorithm (EBUA), and the EBUA solved the problem of task scheduling based on unimodal arbitrary arrival model.
This paper presents a two-phase algorithm, called H2GS, for task scheduling on HeDCSs.
Section 4 describes the underlying system model for task scheduling based on online learning.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com