Deadline Constrained Cloud Computing Resources Scheduling through an Ant Colony System Approach

Chen, Zong-Gan; Zhan, Zhi-Hui; Li, Hai-Hao; Du, Ke-Lin; Zhong, Jinghui; Foo, Yong Wee; Li, Yun; Zhang, Mengjie

doi:10.1109/icccri.2015.14

Cited by 41 publications

(20 citation statements)

References 21 publications

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“…Note that, used by itself separately from NNs, EC algorithms is famous for its great ability in a variety of optimization problems such as resource scheduling [35,36], power system [37], and so on.…”

Section: Othersmentioning

confidence: 99%

When Neural Network Computation Meets Evolutionary Computation: A Survey

Chen

Zhan

Shi

et al. 2016

Advances in Neural Networks – ISNN 2016

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Abstract. Neural network (NN) and evolutionary computation (EC) are two of the most popular and important techniques in computational intelligence, which can be combined together to solve the complex real world problems. This paper represents a review of the researches that combined NN and EC. There are 3 main research focuses as follows. In the first research focus, EC algorithms have been widely used to optimize the structure and parameter of the NN, including weight, structure, learning rates, and others. In the second research focus, lots literatures have witnessed that EC based NNs are widespread in the applications such as classification, automatic control, prediction, and many other fields. These two kinds of researches into combining NN and EC are mainly focuses on using EC algorithms to optimize NN, to enhance the NN performance and the NN application ability. Our survey finds that particle swarm optimization is the most popular EC algorithm that the researchers choice to optimize NN in recent year, while genetic algorithm and differential evolution are also widely used. In the third research focus, there are also researches adopted NN as a tool to enhance the performance of EC algorithms. Although the literatures in this focus are not as many as the above two focuses, the existing results show that NN has great potential in enhancing EC algorithms. The survey shows that when NN and EC meet, combining them would result in an effective way to deal with the real world application. This interesting research topic has become more and more significant in the field of computer science and still has much room for development.

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Section: Othersmentioning

confidence: 99%

When Neural Network Computation Meets Evolutionary Computation: A Survey

Chen

Zhan

Shi

et al. 2016

Advances in Neural Networks – ISNN 2016

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“…Since power consumed by the idle state is the major source of wasted energy, reducing the number of active servers can save energy significantly. In the experiments, the consumed power is calculated according to the power model in (7).…”

Section: A Virtual Machine Placementmentioning

confidence: 99%

“…The power consumed by active but idle servers, that is, the quantity P idle , is the major source of energy waste. The value of P idle is expressed as a percentage or fraction k of the power consumed when the server operates at full capacity, P idle = kP max in (7). P idle is an inherent part of power consumption of an active server.…”

Section: H Energy Saved By Oemacsmentioning

confidence: 99%

“…This way, the physical resources are virtualized as uniform resources and therefore are efficient for parallel and distributed computing [5], [6]. Virtual machines (VMs) are created according to the type of operating system and the amount of required resources such as CPU, memory, and storage, specified by the customers and then run on a physical server to host application to meet requirements of customers [7], [8]. On the other hand, virtualization allows multiple VMs to be executed on the same physical server and share hardware resources.…”

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confidence: 99%

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An Energy Efficient Ant Colony System for Virtual Machine Placement in Cloud Computing

Liu

Zhan

Deng

et al. 2018

IEEE Trans. Evol. Computat.

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358

212

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Virtual machine placement (VMP) and energy efficiency are significant topics in cloud computing research. In this paper, evolutionary computing is applied to VMP to minimize the number of active physical servers, so as to schedule underutilized servers to save energy. Inspired by the promising performance of the ant colony system (ACS) algorithm for combinatorial problems, an ACS-based approach is developed to achieve the VMP goal. Coupled with order exchange and migration (OEM) local search techniques, the resultant algorithm is termed an OEMACS. It effectively minimizes the number of active servers used for the assignment of virtual machines (VMs) from a global optimization perspective through a novel strategy for pheromone deposition which guides the artificial ants toward promising solutions that group candidate VMs together. The OEMACS is applied to a variety of VMP problems with differing VM sizes in cloud environments of homogenous and heterogeneous servers. The results show that the OEMACS generally outperforms conventional heuristic and other evolutionary-based approaches, especially on VMP with bottleneck resource characteristics, and offers significant savings of energy and more efficient use of different resources. Index Terms-Ant colony system (ACS), cloud computing, virtual machine placement (VMP). I. INTRODUCTION C LOUD computing is a large-scale distributed computing paradigm, driven by an increasing demand for various

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“…In IaaS, cloud computing offers an unlimited amount of heterogeneous resources with high elasticity of use via a virtualization technique [4]. A virtual machine (VM) is created to host an application according to the customer requirements for resources such as CPU, memory, storage, and bandwidth [5,6]. The virtualization technique enables multiple VMs to share the physical resources on the same physical machine (PM).…”

Section: Introductionmentioning

confidence: 99%

Optimal Virtual Machine Placement Based on Grey Wolf Optimization

et al. 2019

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Virtual machine placement (VMP) optimization is a crucial task in the field of cloud computing. VMP optimization has a substantial impact on the energy efficiency of data centers, as it reduces the number of active physical servers, thereby reducing the power consumption. In this paper, a computational intelligence technique is applied to address the problem of VMP optimization. The problem is formulated as a minimization problem in which the objective is to reduce the number of active hosts and the power consumption. Based on the promising performance of the grey wolf optimization (GWO) technique for combinatorial problems, GWO-VMP is proposed. We propose transforming the VMP optimization problem into binary and discrete problems via two algorithms. The proposed method effectively minimizes the number of active servers that are used to host the virtual machines (VMs). We evaluated the proposed method on various VM sizes in the CloudSIM environment of homogeneous and heterogeneous servers. The experimental results demonstrate the efficiency of the proposed method in reducing energy consumption and the more efficient use of CPU and memory resources.

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Deadline Constrained Cloud Computing Resources Scheduling through an Ant Colony System Approach

Cited by 41 publications

References 21 publications

When Neural Network Computation Meets Evolutionary Computation: A Survey

When Neural Network Computation Meets Evolutionary Computation: A Survey

An Energy Efficient Ant Colony System for Virtual Machine Placement in Cloud Computing

Optimal Virtual Machine Placement Based on Grey Wolf Optimization

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