Resource preprocessing and optimal task scheduling in cloud computing environments

Abstract

SummaryCloud computing came into being and is currently an essential infrastructure of many commerce facilities. To achieve the promising potentials of cloud computing, effective and efficient scheduling algorithms are fundamentally important. However, conventional scheduling methodology encounters a number of challenges. During the tasks scheduling in cloud systems, how to make full use of resources and how to effectively select resources are also important factors. At the same time, communication delay also plays an important role in cloud scheduling, which not only leads to waiting between tasks but also results in much idle interval time between processing units. In this paper, a fuzzy clustering method is used to effectively preprocess the cloud resources. Combining the list scheduling with the task duplication scheduling scheme, a new directed acyclic graph based scheduling algorithm called earliest finish time duplication algorithm for heterogeneous cloud systems is presented. Earliest finish time duplication attempts to insert suitable immediate parent nodes of the current selected node in order to reduce its waiting time on the processor. The case study and experimental results illustrate that the algorithm proposed in this paper is better than the popular heterogeneous earliest finish time algorithms. Copyright © 2014 John Wiley & Sons, Ltd.

Hard Real-Time Task Scheduling in Cloud Computing Using an Adaptive Genetic Algorithm

“…Therefore, the efficient use of the virtual machine is of a vital importance in the cloud computing environment. Within this context, effective and efficient scheduling algorithms are fundamentally very important [4,11,26,27]. This has attracted the attention of many researchers to study the task scheduling problem in a cloud computing environment.…”

Delay-Bounded Associated Tasks Scheduling Based on Hierarchical Graph Model in the Cloud

“…All the DAGs are generated by the DAG graph random generator. The computer resources pool can be randomly generated from the interval [5,10], while the task executing time is randomly generated from the interval [5,30] and the corresponding cost is inversely proportional to the time. In the experiments, we set 50 virtual nodes to establish a heterogeneous computing environment.…”

A Multiqueue Interlacing Peak Scheduling Method Based on Tasks’ Classification in Cloud Computing

“…Many research has been devoted to multiobjective optimization, e.g., the constraints of QoS [14], [31]- [33], energy consumption [34], [35], [44], economic costs [3], [7], [14], system performance [8], [11], [13], [37], [38], and all these comprehensive [14], [29], [32], [33], [36], [45].…”

“…Most other papers focused solely on task optimization and did not considering resource status. Another paper proposed a replication algorithm of the earliest completion time based on task copy [37]. First, it pretreats resources through fuzzy clustering and then schedules tasks using a directed acyclic graph and task duplication.…”