LBSR: Load Balance Over Slow Resources

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

doi:10.1109/cais.2018.8442041

Cited by 8 publications

(7 citation statements)

References 15 publications

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“…These methods have the efficiency to take care of the past issues into consideration forming a certain amount of feedback structure to avoid the problems raised in the previous stages. Round robin (RR) [5] is a well-known static scheduling algorithm whose design is straightforward. This method of allocation has been a classical one in these situations which holds the good amount of record when it comes to the execution process.…”

Section: Figure 1 Displays Virtual Machine Load Balancer (Vms)mentioning

confidence: 99%

An Efficient Hybrid Load Balancing Algorithm for Heterogeneous Data Centers in Cloud Computing

N¹

2020

IJATCSE

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Cloud computing has recently grown into a major global trend of computing. Using the Internet and Wide Area Network (WAN) to make services remotely is a modern design. This is a new solution and technique to achieve high availability, versatility, cost savings and demand-scalability. However, cloud computing faces many problems, such as wasteful resource use, which has a major effect on the performance of cloud computers. These issues have to be managed from time to time to avoid the utilization factors of the various attributes that are used while implementing the process. Because of the enormous amount of knowledge these issues emerged and are unaddressed for so many years even though they were just adjusted in between to carry out the normal activities. Therefore, one of the most critical issues in this area in improving cloud computing performance is the need for robust and efficient load balancing algorithms for cloud computing. Many researchers have proposed different load balancing and job scheduling algorithms in cloud computing, but system efficiency is still very unstable and load still unbalance. This has subsequently delayed the process of executing the algorithms within the required timelines. Hence, in this research, we propose a load balancing algorithm to improve performance and efficiency in a heterogeneous cloud computing environment. We propose a hybrid algorithm that utilizes both random and greedy algorithms. The algorithm takes into account the current resource data and the CPU capacity factor to attain the objectives. The hybrid algorithm was tested using Cloud Analyst simulator, and compared with other algorithms. This comparison has been carried out both in a subjective way and also objective way to establish the proposed method. The experimental investigations showed improvements in average response time and processing time by taking current resource information and the CPU capacity factor compared to other algorithms into consideration.

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Section: Figure 1 Displays Virtual Machine Load Balancer (Vms)mentioning

confidence: 99%

An Efficient Hybrid Load Balancing Algorithm for Heterogeneous Data Centers in Cloud Computing

N¹

2020

IJATCSE

View full text Add to dashboard Cite

show abstract

“…It is done based on the probabilistic nature and no changes can be made during the execution, so time of execution-period cannot be determined exactly. In dynamic load balancing, the tasks are executed dynamically between all resources and it is necessary to monitor the current load of the system [6], [7].…”

Section: Related Workmentioning

confidence: 99%

Osmotic Bio-Inspired Load Balancing Algorithm in Cloud Computing

et al. 2019

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Cloud computing is increasing rapidly as a successful paradigm presenting on-demand infrastructure, platform, and software services to clients. Load balancing is one of the important issues in cloud computing to distribute the dynamic workload equally among all the nodes to avoid the status that some nodes are overloaded while others are underloaded. Many algorithms have been suggested to perform this task. Recently, worldview is turning into a new paradigm for optimization search by applying the osmosis theory from chemistry science to form osmotic computing. Osmotic computing is aimed to achieve balance in highly distributed environments. The main goal of this paper is to propose a hybrid metaheuristics technique which combines the osmotic behavior with bio-inspired load balancing algorithms. The osmotic behavior enables the automatic deployment of virtual machines (VMs) that are migrated through cloud infrastructures. Since the hybrid artificial bee colony and ant colony optimization proved its efficiency in the dynamic environment in cloud computing, the paper then exploits the advantages of these bio-inspired algorithms to form an osmotic hybrid artificial bee and ant colony (OH_BAC) optimization load balancing algorithm. It overcomes the drawbacks of the existing bio-inspired algorithms in achieving load balancing between physical machines. The simulation results show that OH_BAC decreases energy consumption, the number of VMs migrations and the number of shutdown hosts compared to existing algorithms. In addition, it enhances the quality of services (QoSs) which is measured by service level agreement violation (SLAV) and performance degradation due to migrations (PDMs). INDEX TERMS Ant colony optimization, artificial bee colony, bio-inspired systems, cloud computing, load balancing, metaheuristics, osmotic computing.

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“…Traditional algorithms [7][8][9][10][11] are used to solve this problem. However, these algorithms have limitations in the case of complex and large scale problems.…”

Section: Introductionmentioning

confidence: 99%

Hybrid load balance based on genetic algorithm in cloud environment

Saber

Moussa

Ghuniem

et al. 2021

IJECE

Self Cite

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Load balancing is an efficient mechanism to distribute loads over cloud resources in a way that maximizes resource utilization and minimizes response time. Metaheuristic techniques are powerful techniques for solving the load balancing problems. However, these techniques suffer from efficiency degradation in large scale problems. This paper proposes three main contributions to solve this load balancing problem. First, it proposes a heterogeneous initialized load balancing (HILB) algorithm to perform a good task scheduling process that improves the makespan in the case of homogeneous or heterogeneous resources and provides a direction to reach optimal load deviation. Second, it proposes a hybrid load balance based on genetic algorithm (HLBGA) as a combination of HILB and genetic algorithm (GA). Third, a newly formulated fitness function that minimizes the load deviation is used for GA. The simulation of the proposed algorithm is implemented in the cases of homogeneous and heterogeneous resources in cloud resources. The simulation results show that the proposed hybrid algorithm outperforms other competitor algorithms in terms of makespan, resource utilization, and load deviation.

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LBSR: Load Balance Over Slow Resources

Cited by 8 publications

References 15 publications

An Efficient Hybrid Load Balancing Algorithm for Heterogeneous Data Centers in Cloud Computing

An Efficient Hybrid Load Balancing Algorithm for Heterogeneous Data Centers in Cloud Computing

Osmotic Bio-Inspired Load Balancing Algorithm in Cloud Computing

Hybrid load balance based on genetic algorithm in cloud environment

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