Hybrid Genetic Algorithm for Cloud Computing Applications

Zhu, Kai; Song, Huaguang; Liu, Lijing; Gao, Jinzhu; Cheng, Guojian

doi:10.1109/apscc.2011.66

Cited by 77 publications

(25 citation statements)

References 7 publications

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“…A novel approach of heuristic-based request scheduling at each server, in each of the geographically distributed datacenters, to globally minimize the penalty charged to the cloud computing system is proposed in [14]. Scheduling based genetic algorithm is proposed in [15,16]. This algorithms optimize the energy consumption, carbon dioxide emissions and the generated profit of a geographically distributed cloud computing infrastructure.…”

Section: Cloud Computing and Task Schedulingmentioning

confidence: 99%

Cloud Computing Online Scheduling

Keshk¹

2014

IOSRJEN

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-Cloud computing has gained a lot of attention to be used as a computing model for a variety of application domains. Task scheduling is the fundamental issue in this environment. To utilize cloud efficiently, a good task scheduling algorithm is needed to assign tasks to resources in cloud. Cloud task can be divided into two categories such as on-line mode service and the batch mode service. In this paper, online cloud task scheduling based on virtual machine adaptive fault tolerance and load balancing using ant colony algorithm is proposed. The main contribution of this work is that load balancing factor is added and the system tolerates the faults by tacking the decision on the basis of reliability of the virtual machines in scheduling process. The proposed scheduling strategy was simulated using the Cloudsim toolkit package. Experimental results show that the proposed algorithm achieved the better load balance than Join-shortest-queue (JSQ) and Modified Ant Colony Optimization (MACO) algorithms.

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Section: Cloud Computing and Task Schedulingmentioning

confidence: 99%

Cloud Computing Online Scheduling

Keshk¹

2014

IOSRJEN

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“…Guo-ning, TingLei and Shuai (2010) [8] designed an algorithm based on simulated annealing GA that considered the QoS requirements of different types of consumption tasks, corresponding to the characteristics of tasks in a cloud environment. Zhu, Song, Liu, Gao and Cheng (2011) [9] demonstrated the advantage of (Multi-Agent Genetic Algorithm) MAGA over traditional GA, by designing a load balancing model on the basis of virtualization resource management. Xiaoli and Yuping Wang (2012) [10] proposed an improved bi-level multi-objective evolutionary algorithm that used a modified operator and a local search scheme to speed up the convergence.…”

Section: Figure 1: Steps Of Genetic Algorithmmentioning

confidence: 99%

An Analysis of Task Scheduling in Cloud Computing using Evolutionary and Swarm-based Algorithms

Bilgaiyan¹,

Sagnika²,

Das³

2014

IJCA

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Cloud computing is a popular computing paradigm that performs processing of huge volumes of data using highly available geographically distributed resources that can be accessed by users on the basis of Pay As per Use policy. In the modern computing environment where the amount of data to be processed is increasing day by day, the costs involved in the transmission and execution of such amount of data is mounting significantly. So there is a requirement of appropriate scheduling of tasks which will help to manage the escalating costs of data intensive applications. This paper analyzes various evolutionary and swarm based task scheduling algorithms that address the above mentioned problem.

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“…The related works are summarized according to their optimization objectives in Table III and are described as follows. For optimizing the resource load balance, Zhu et al [2011] proposed using a Multiagent GA (MAGA) to solve the scheduling problem. In order to reduce the size of tasks, a group strategy based on the task parameters was designed to divide the tasks into groups.…”

Section: Scheduling For Provider Efficiencymentioning

confidence: 99%

Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches

et al. 2015

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A disruptive technology fundamentally transforming the way that computing services are delivered, cloud computing offers information and communication technology users a new dimension of convenience of resources, as services via the Internet. Because cloud provides a finite pool of virtualized on-demand resources, optimally scheduling them has become an essential and rewarding topic, where a trend of using Evolutionary Computation (EC) algorithms is emerging rapidly. Through analyzing the cloud computing architecture, this survey first presents taxonomy at two levels of scheduling cloud resources. It then paints a landscape of the scheduling problem and solutions. According to the taxonomy, a comprehensive survey of state-of-the-art approaches is presented systematically. Looking forward, challenges and potential future research directions are investigated and invited, including real-time scheduling, adaptive dynamic scheduling, large-scale scheduling, multiobjective scheduling, and distributed and parallel scheduling. At the dawn of Industry 4.0, cloud computing scheduling for cyber-physical integration with the presence of big data is also discussed. Research in this area is only in its infancy, but with the rapid fusion of information and data technology, more exciting and agenda-setting topics are likely to emerge on the horizon. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Permissions@acm.org.

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Hybrid Genetic Algorithm for Cloud Computing Applications

Cited by 77 publications

References 7 publications

Cloud Computing Online Scheduling

Cloud Computing Online Scheduling

An Analysis of Task Scheduling in Cloud Computing using Evolutionary and Swarm-based Algorithms

Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches

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