Hybrid load balance based on genetic algorithm in cloud environment

Saber, Walaa; Moussa, Walid; Ghuniem, Atef M.; Rizk, Rawya

doi:10.11591/ijece.v11i3.pp2477-2489

Cited by 16 publications

(14 citation statements)

References 26 publications

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“…The proposed method achieves good performance by assigning optimal tasks to virtual machines suitable for different scenarios (as shown in Figure 12). Figure 15 shows a comparison of the performance of the proposed method with previous methods [14,32,46]. As shown in Figure 15, the performance of the proposed method is better than other existing methods.…”

Section: Comparison Of the Proposed Methods With Previousmentioning

confidence: 93%

Load Balancing in Edge Computing Using Integer Linear Programming Based Genetic Algorithm and Multilevel Control Approach

Zhang

Shu

Navaei

2022

Wireless Communications and Mobile Computing

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Due to the proliferation of requests in heterogeneous resources in edge computing, the existence of a large number of tasks and workloads in virtual machines in the edge computing environment is inevitable. Thus, load balancing strives to facilitate an even distribution of workload across available resources. Its purpose is to provide continuous service and to ensure fair load distribution among resources. Load balancing, with the aim of minimizing response time for tasks and improving resource efficiency, tries to do the proper mapping of tasks among virtual machines at a lower cost. Flow scheduling, on the other hand, assigns a task (group of tasks) to computational resources by prioritizing tasks, so that the relationship between them is maintained. Therefore, in this research, a hierarchical control framework for load balancing and assignment of tasks in edge computing services is presented in order to create load balancing. In the proposed method, in the first level, the genetic algorithm receives a set of tasks in a workflow. Genetic algorithm prioritizes and assigns tasks to resources according to time constraints, resource processing power, resource availability, and task cost. For this purpose, the integer linear programming optimization in the evaluation function of the genetic algorithm will be utilized. In the second level, considering the past load distribution in edge resources, we estimate the probability of load distribution among sources according to hidden Markov model (HMM). Finally, in order to optimally map tasks to the virtual machines in each host, we will use game theory with service quality factors as an evaluation function. Previous methods have provided a hierarchical control framework that aims to achieve conflicting goals within a data center, but does not use linear programming. Considering the use of service quality criteria as evaluation function parameters in heuristic and optimization methods in this research, it is expected that the results of this research will improve compared to previous methods.

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Section: Comparison Of the Proposed Methods With Previousmentioning

confidence: 93%

Load Balancing in Edge Computing Using Integer Linear Programming Based Genetic Algorithm and Multilevel Control Approach

Zhang

Shu

Navaei

2022

Wireless Communications and Mobile Computing

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“…It is also observed that the total cost can be reduced through an energy consumption model that is composed of the processor's execution and transmission cost [7]. Furthermore, the improvement of the load deviation, the resource utilisation (RU) and the makespan can be achieved through the proposed algorithm in the paper [8] denoted hybrid load balance based on genetic algorithm (HLBGA). Similarly, resource utilisation, load balancing, and excellent performance can also be achieved by implementing a novel architecture that is based on the particle swarm optimisation (PSO) and the dynamic dispatch queues algorithms [2].…”

Section: Int J Elec and Comp Eng Issn: 2088-8708mentioning

confidence: 99%

An efficient cloudlet scheduling via bin packing in cloud computing

Chraibi

Alla

Ezzati

2022

IJECE

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<p>In this ever-developing technological world, one way to manage and deliver services is through cloud computing, a massive web of heterogenous autonomous systems that comprise adaptable computational design. Cloud computing can be improved through task scheduling, albeit it being the most challenging aspect to be improved. Better task scheduling can improve response time, reduce power consumption and processing time, enhance makespan and throughput, and increase profit by reducing operating costs and raising the system reliability. This study aims to improve job scheduling by transferring the job scheduling problem into a bin packing problem. Three modifies implementations of bin packing algorithms were proposed to be used for task scheduling (MBPTS) based on the minimisation of makespan. The results, which were based on the open-source simulator CloudSim, demonstrated that the proposed MBPTS was adequate to optimise balance results, reduce waiting time and makespan, and improve the utilisation of the resource in comparison to the current scheduling algorithms such as the particle swarm optimisation (PSO) and first come first serve (FCFS).</p>

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“…However, this approach lacks the issue of overloading the virtual machine. Recent work in a similar direction by [22] presented heterogeneous initialized workload balancing for task scheduling.…”

Section: Introductionmentioning

confidence: 99%

A combined computing framework for load balancing in multi-tenant cloud eco-system

Chandrashekhar

Sharvani²

2022

IJECE

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<span>Since the world is getting digitalized, cloud computing has become a core part of it. Massive data on a daily basis is processed, stored, and transferred over the internet. Cloud computing has become quite popular because of its superlative quality and enhanced capability to improvise data management, offering better computing resources and data to its user bases (UBs). However, there are many issues in the existing cloud traffic management approaches and how to manage data during service execution. The study introduces two distinct research models: data center virtualization framework under multi-tenant cloud-ecosystem (DCVF-MT) and collaborative workflow of multi-tenant load balancing (CW-MTLB) with analytical research modeling. The sequence of execution flow considers a set of algorithms for both models that address the core problem of load balancing and resource allocation in the cloud computing (CC) ecosystem. The research outcome illustrates that DCVF-MT, outperforms the one-to-one approach by approximately 24.778% performance improvement in traffic scheduling. It also yields a 40.33% performance improvement in managing cloudlet handling time. Moreover, it attains an overall 8.5133% performance improvement in resource cost optimization, which is significant to ensure the adaptability of the frameworks into futuristic cloud applications where adequate virtualization and resource mapping will be required.</span>

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Hybrid load balance based on genetic algorithm in cloud environment

Cited by 16 publications

References 26 publications

Load Balancing in Edge Computing Using Integer Linear Programming Based Genetic Algorithm and Multilevel Control Approach

Load Balancing in Edge Computing Using Integer Linear Programming Based Genetic Algorithm and Multilevel Control Approach

An efficient cloudlet scheduling via bin packing in cloud computing

A combined computing framework for load balancing in multi-tenant cloud eco-system

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