Research on Heuristic Based Load Balancing Algorithms in Cloud Computing

Pan, Jeng‐Shyang; Ren, Pingfei; Tang, Linlin

doi:10.1007/978-3-319-21206-7_35

Cited by 3 publications

(8 citation statements)

References 11 publications

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“…At load balancing time, tasks must be transferred from one virtual machine to another to reduce RT max , and the processing time of a task from a virtual machine is based on the virtual machine's capacity. RT max is obtained by Equation

{RT}_{\max} = ({}, \max_{i = 1}^{n} {RT}_{i,} \max_{j = 1}^{n} \sum_{i = 1}^{n} {PT}_{ij})

…”

Section: Proposed Methodsmentioning

confidence: 99%

“…Pt i is obtained according to Equation .

{Pt}_{i} = \frac{L_{i}}{C_{i}},

where L is the load and C is the capacity of the virtual machine …”

Section: Proposed Methodsmentioning

confidence: 99%

“…• Disregarding issues such as system privacy and security Pan et al 27 Using the modified bee algorithm for efficient scheduling and proper load balancing and taking into consideration the previous status of the nodes.…”

Section: Kalra and Singh 28mentioning

confidence: 99%

“…It is assumed that the target system consists of a heterogeneous resource set as R = {r i }za, and m is a virtual machine set as VM = {VM 1 , VM 2 , …, VM m }, where the n-task processing is set to T = {T 1 , T 2 , ..., T n }. 27 The list of symptoms used in the formulas is in the Appendix A ( Table 8). The T i task end time is indicated by RTi and the Ti task processing time on the VMj virtual machine is shown with PT ij .…”

Section: Mathematical Modelmentioning

confidence: 99%

“…In Pan et al, the bee algorithm has been inspired to solve the load balancing issue. This method obtains load balancing using the honey bee search behavior that targets to achieve load balancing across the virtual machine and maximize throughput.…”

Section: Related Workmentioning

confidence: 99%

See 4 more Smart Citations

A new fuzzy‐based method for load balancing in the cloud‐based Internet of things using a grey wolf optimization algorithm

Li¹,

Zhiwei²,

Cheng³

et al. 2020

Int J Communication

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Summary Cloud computing provides high accessibility, scalability, and flexibility in the era of computing for different practical applications. Internet of things (IoT) is a new technology that connects the devices and things to provide user required services. Due to data and information upsurge on IoT, cloud computing is usually used for managing these data, which is known as cloud‐based IoT. Due to the high volume of requirements, service diversity is one of the critical challenges in cloud‐based IoT. Since the load balancing issue is one of the NP‐hard problems in heterogeneous environments, this article provides a new method for response time reduction using a well‐known grey wolf optimization algorithm. In this paper, we supposed that the response time is the same as the execution time of all the tasks that this parameter must be minimized. The way is determining the status of virtual machines based on the current load. Then the tasks will be removed from the machine with the additional load depending on the condition of the virtual machine and will be transferred to the appropriate virtual machine, which is the criterion for assigning the task to the virtual machine based on the least distance. The results of the CloudSim simulation environment showed that the response time is developed in compared to the HBB‐LB and EBCA‐LB algorithm. Also, the load imbalancing degree is improved in comparison to TSLBACO and HJSA.

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{RT}_{\max} = ({}, \max_{i = 1}^{n} {RT}_{i,} \max_{j = 1}^{n} \sum_{i = 1}^{n} {PT}_{ij})

…”

Section: Proposed Methodsmentioning

confidence: 99%

“…Pt i is obtained according to Equation .

{Pt}_{i} = \frac{L_{i}}{C_{i}},

where L is the load and C is the capacity of the virtual machine …”

Section: Proposed Methodsmentioning

confidence: 99%

Section: Kalra and Singh 28mentioning

confidence: 99%

Section: Mathematical Modelmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 3 more Smart Citations

A new fuzzy‐based method for load balancing in the cloud‐based Internet of things using a grey wolf optimization algorithm

Li¹,

Zhiwei²,

Cheng³

et al. 2020

Int J Communication

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show abstract

Online and offline based load balance algorithm in cloud computing

Tang

Ren

et al. 2017

Knowledge-Based Systems

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Modified Bat Algorithm for Optimal VM's in Cloud Computing

Sundas¹,

Badotra²,

Alotaibi³

et al. 2022

Computers, Materials &Amp; Continua

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All task scheduling applications need to ensure that resources are optimally used, performance is enhanced, and costs are minimized. The purpose of this paper is to discuss how to Fitness Calculate Values (FCVs) to provide application software with a reliable solution during the initial stages of load balancing. The cloud computing environment is the subject of this study. It consists of both physical and logical components (most notably cloud infrastructure and cloud storage) (in particular cloud services and cloud platforms). This intricate structure is interconnected to provide services to users and improve the overall system's performance. This case study is one of the most important segments of cloud computing, i.e., Load Balancing. This paper aims to introduce a new approach to balance the load among Virtual Machines (VM's) of the cloud computing environment. The proposed method led to the proposal and implementation of an algorithm inspired by the Bat Algorithm (BA). This proposed Modified Bat Algorithm (MBA) allows balancing the load among virtual machines. The proposed algorithm works in two variants: MBA with Overloaded Optimal Virtual Machine (MBA-OOVM) and Modified Bat Algorithm with Balanced Virtual Machine (MBA-BVM). MBA generates cost-effective solutions and the strengths of MBA are finally validated by comparing it with Bat Algorithm.

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Research on Heuristic Based Load Balancing Algorithms in Cloud Computing

Cited by 3 publications

References 11 publications

A new fuzzy‐based method for load balancing in the cloud‐based Internet of things using a grey wolf optimization algorithm

A new fuzzy‐based method for load balancing in the cloud‐based Internet of things using a grey wolf optimization algorithm

Online and offline based load balance algorithm in cloud computing

Modified Bat Algorithm for Optimal VM's in Cloud Computing

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