Load Balancing Task Scheduling Based on Genetic Algorithm in Cloud Computing

Wang, Tingting; Liu, Zhaobin; Chen, Yi; Xu, Yujie; Dai, Xiaoming

doi:10.1109/dasc.2014.35

Cited by 113 publications

(40 citation statements)

References 17 publications

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“…The policy was combined with the first come first served (FCFS), the Min-Min, and the Min-Max algorithms. Another paper balanced load across nodes and reduced the average completion time using the genetic algorithm [27]. Another proposed an improved Min-Min algorithm based on user priorities to achieve load balancing, achieve the best span, and improve resource utilization [28].…”

Section: Focus On System Performancementioning

confidence: 99%

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

Zuo

Dong

Shu

et al. 2018

IEEE Systems Journal

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Abstract-In cloud computing, resources are dynamic, and the demands placed on the resources allocated to a particular task are diverse. These factors could lead to load imbalances, which affect scheduling efficiency and resource utilization. A scheduling method called interlacing peak is proposed. First, the resource load information, such as CPU, I/O, and memory usage, is periodically collected and updated, and the task information regarding CPU, I/O, and memory is collected. Second, resources are sorted into three queues according to the loads of the CPU, I/O, and memory: CPU intensive, I/O intensive, and memory intensive, according to their demands for resources. Finally, once the tasks have been scheduled, they need to interlace the resource load peak. Some types of tasks need to be matched with the resources whose loads correspond to a lighter types of tasks. In other words, CPUintensive tasks should be matched with resources with low CPU utilization; I/O-intensive tasks should be matched with resources with shorter I/O wait times; and memory-intensive tasks should be matched with resources that have low memory usage. The effectiveness of this method is proved from the theoretical point of view. It has also been proven to be less complex in regard to time and place. Four experiments were designed to verify the performance of this method. Experiments leverage four metrics: 1) average response time; 2) load balancing; 3) deadline violation rates; and 4) resource utilization. The experimental results show that this method can balance loads and improve the effects of resource allocation and utilization effectively. This is especially true when resources are limited. In this way, many tasks will compete for the same resources. However, this method shows advantage over other similar standard algorithms.

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Section: Focus On System Performancementioning

confidence: 99%

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

Zuo

Dong

Shu

et al. 2018

IEEE Systems Journal

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“…To solve the material, consider the new distinctiveness of cloud computing and unique adaptive genetic algorithmic program (AGA) a brand new scheduling algorithm supported dual-fitness adaptive algorithm-job straddling time and load balancing genetic algorithm (JLGA) is conventional. Then judge against the performance of JLGA with AGA through simulations [4].…”

Section: Tingting Wang Et Al [2014]mentioning

confidence: 99%

Optimal Scheduling and Load Balancing in Cloud using Enhanced Genetic Algorithm

Kaur¹,

Kaur²

2015

IJCA

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The cloud computing is the enlargement of distributed computing, equivalent computing and gridiron computing, or defined as the commercial achievement of these computer science concepts. One of the elementary issues in these circumstances is interrelated to task scheduling and Load Balancing. Cloud task arrangement is an NP-hard optimization dilemma, and numerous meta-heuristic algorithms have been anticipated to crack it. A superior task scheduler should acclimatize its arrangement stratagem to the varying situation and the types of tasks. This manuscript proposes a cloud task arrangement course of action based on Load Balancing Enhanced Genetic (EGA) algorithm. The major involvement of our exertion is to balance the whole system load although trying to minimizing the Makespan of a prearranged tasks set. The innovative scheduling strategy was simulated using the Net Beans toolkit package. Experiments results showed the proposed Enhanced Genetic (EGA) algorithm and Compare the EGA, ACO.

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“…We consider that each CSP has a multi-cloud datacenter, each data-center has several clusters and each cluster consists of numerous servers and each server runs numerous VMs (Virtual Machines). 273 | P a g e www.ijacsa.thesai.org six components [6], the characteristics of each one are summarized in the following section. We have the layers:  Interface layer: the responsible for the communication between the CSC and CSP.…”

Section: Proposed Model Architecture Cloud Computingmentioning

confidence: 99%

Priority Task Scheduling Strategy for Heterogeneous Multi-Datacenters in Cloud Computing

Er-raji¹,

Benabbou²

2017

ijacsa

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Abstract-With the rapid development in science and technology, cloud computing has emerged to be widely adopted in several IT (Information Technology) areas. It allows for the companies as well as researchers to use the computing resources as a service over a network as internet without owning the infrastructure. However, Due to increasing demand of cloud computing, the growing number of tasks affects the system load and performance. Scheduling of multitasks with respect SLA (Service Level Agreement) can face serious challenges. In order to overcome this problem as well as provide better quality of service, the tasks have to be scheduled in optimal way. In this paper, we address the problem of the priority task scheduling through proposing a global strategy over distributed data-center in cloud computing basing on three parameters: tasks deadline, task age and the task length.

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Load Balancing Task Scheduling Based on Genetic Algorithm in Cloud Computing

Cited by 113 publications

References 17 publications

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

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

Optimal Scheduling and Load Balancing in Cloud using Enhanced Genetic Algorithm

Priority Task Scheduling Strategy for Heterogeneous Multi-Datacenters in Cloud Computing

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