Cloud Loading Balance algorithm

Zhang, Bo; Ai, Jieqing

doi:10.1109/icise.2010.5688910

Cited by 13 publications

(4 citation statements)

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“…The overall goal of this system is that, an agreed scheduling on the resources' allocation is to be obtained to perform the jobs. Consequently, with the resources allocation, the load balancing will be increased [27]. Here the scheduler is responsible to allocate one or more jobs to artificial machines in a distributed system [28].…”

Section: Description Of the Problemmentioning

confidence: 99%

Dynamic Resource Allocation in Cloud Computing

Mousavi¹,

Mosavi²,

Varkonyi-Koczy³

et al. 2017

ACTA POLYTECH HUNG

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Utilizing dynamic resource allocation for load balancing is considered as an important optimization process in cloud computing. In order to achieve maximum resource efficiency and scalability in a speedy manner this process is concerned with multiple objectives for an effective distribution of loads among virtual machines. In this realm, exploring new algorithms, as well as development of novel algorithms, is highly desired for technological advancement and continued progress in resource allocation application in cloud computing. Accordingly, this paper explores the application of two relatively new optimization algorithms and further proposes a hybrid algorithm for load balancing which can contribute well in maximizing the throughput of the cloud provider's network. The proposed algorithm is a hybrid of teaching-learning-based optimization algorithm (TLBO) and grey wolves optimization algorithm (GW). The hybrid algorithm performs more efficiently than utilizing every single one of these algorithms. Furthermore, it well balances the priorities and effectively considers load balancing based on time, cost, and avoidance of local optimum traps, which consequently leads to minimal amount of waiting time. To evaluate the effectiveness of the proposed algorithm, a comparison with the TLBO and GW algorithms is conducted and the experimental results are presented.

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Section: Description Of the Problemmentioning

confidence: 99%

Dynamic Resource Allocation in Cloud Computing

Mousavi¹,

Mosavi²,

Varkonyi-Koczy³

et al. 2017

ACTA POLYTECH HUNG

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“…Another aim of scheduling algorithms is making load balance, include Weighted Mean Time algorithm [8] and some of heuristic based scheduling algorithms [8][9][10][11].…”

Section: Related Workmentioning

confidence: 99%

PSO based Hierarchical Task Scheduling with QoS Preference Awareness in Cloud Storage Environment

Wang¹,

Li²,

Zhang³

et al. 2014

JSW

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Most existing task scheduling algorithms in cloud storage fail to aware users ' QoS preference. In addition, these algorithms result in low user satisfaction rate for they do not consider the characteristics of cloud storage. In order to address these problems, the "optimal order comparison method" is used to aware users ' QoS preference, and also helps experts use their professional knowledge to decide the weight of QoS classes. We redefined the fitness function of the particle swarm optimization (PSO) algorithm by using these weights and proposed the "PSO based hierarchical task scheduling with QoS preference awareness: PSO-HQoSPA" algorithm. By consider both user and expert experience, the method can aware users' QoS preference and deal with multiple QoS requirements. The simulation results show our method achieved acceptable user satisfaction rate, and the same time maintains the efficiency as traditional PSO based method.

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“…Obviously, finding the proper mappings between the jobs submitted by end users and the dynamic resources of the available VMs is an NP-hard optimization problem. For this problem, a variety of dynamic, static and mixed scheduling algorithms have been proposed [4][5][6][7][8][9][10]. Some of the well-known early static scheduling schemes were based on ISH, MCP and ETF algorithms [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

“…For this problem, a variety of dynamic, static and mixed scheduling algorithms have been proposed [4][5][6][7][8][9][10]. Some of the well-known early static scheduling schemes were based on ISH, MCP and ETF algorithms [8][9][10]. Most of these algorithms were built for the BNP class and were suitable for high efficiency network platforms in distributed environments.…”

Section: Introductionmentioning

confidence: 99%

Improved Task Scheduling for Virtual Machines in the Cloud based on the Gravitational Search Algorithm

Mamalis¹,

Perlitis²

2022

IJCA

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The rapid and convenient provision of the available computing resources is a crucial requirement in modern cloud computing environments. However, if only the execution time is taken into account when the resources are scheduled, it could lead to imbalanced workloads as well as to significant under-utilisation of the involved Virtual Machines (VMs). In the present work a novel task scheduling scheme is introduced, which is based on the proper adaptation of a modern and quite effective evolutionary optimization method, the Gravitational Search Algorithm (GSA). The proposed scheme aims at optimizing the entire scheduling procedure, in terms of both the tasks execution time and the system (VMs) resource utilisation. Moreover, the fitness function was properly selected considering both the above factors in an appropriately weighted function in order to obtain better results for large inputs. Sufficient simulation experiments show the efficiency of the proposed scheme, as well as its excellence over related approaches of the bibliography, with similar objectives.

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Cloud Loading Balance algorithm

Cited by 13 publications

References 0 publications

Dynamic Resource Allocation in Cloud Computing

Dynamic Resource Allocation in Cloud Computing

PSO based Hierarchical Task Scheduling with QoS Preference Awareness in Cloud Storage Environment

Improved Task Scheduling for Virtual Machines in the Cloud based on the Gravitational Search Algorithm

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