Towards a Load Balancing in a three-level cloud computing network

Wang, Shu-Ching; Yan, Kuo-Qin; Liao, W.P.; Wang, Shun-Sheng

doi:10.1109/iccsit.2010.5563889

Cited by 100 publications

(6 citation statements)

References 8 publications

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“…The task scheduling algorithms can also be distinguished into dependent and independent. In independent job/task scheduling no dependencies are assumed among the tasks [21][22][23][24][25]. All the tasks may be executed separately, and there is no need to keep any precedence log among them.…”

Section: Related Workmentioning

confidence: 99%

“…Trying to address the most known independent job/task scheduling schemes of the literature, one should definitely refer to classical approaches like OLB, MET [17][18], Min-Min, Max-Min [19][20], LBSM [21]), MCT [22], Sufferage [23], LBMM [24], LJFR-SJFR [25], and RDLBS [26], as well as to more recent ones (based on modern combined techniques) like ESTA [27] and improved PSO [30].…”

Section: Related Workmentioning

confidence: 99%

“…However, it also has a significant disadvantage; specifically, the largest in size tasks will probably be waiting for undesirably long time. Further, the LBMM [24] heuristic stands as the combination of Min-Min and OLB scheduling schemes, thus leading to even better performance on makespan.…”

Section: Related Workmentioning

confidence: 99%

“…Some of the more recent improved scheduling algorithms used for addressing the above problems were SJF, FCFS, MET, MCT, Min-Min, Max-Min, LBSM etc. [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27]. Recently, the swarm intelligence algorithms (like PSO) have also been used effectively towards the above direction [28][29][30].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Mamalis¹,

Perlitis²

2022

IJCA

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“…In [20] authors have proposed a version of Min-Min algorithm [21] for decreasing the make span and balance the load by considering the user priority as parameter. As Min-Min algorithm fails to utilize resource properly which leads to load imbalance, so, they proposed Load Balance Improved MinMin Scheduling Algorithm(LBIMM), to improve the balancing of load by utilizing every resource and for reducing overall completion time they extend LBIMM to User-Priority Awared Load Balance Improved Min-Min Scheduling Algorithm (PA-LBIMM).…”

Section: User-priority Guided Min-min Algorithmmentioning

confidence: 99%

Load Balancing Technique in Cloud Computing : A Review

Chamoli¹,

Suyal²,

Panwar³

et al. 2016

IJCA

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Cloud Computing (CC) term came into existence using existing technologies like Parallel computing, Grid computing, Distributing computing, Peer to Peer technology and Virtualization, etc. Load Balancing is a technique of sharing cloudlet of overloaded nodes to slug nodes. In CC, due to its elastic characteristic, load balancing (LB) is a critical issue as data processing occurs centrally in network using Virtual Machines (VM). LB helps in minimizing over consumption of resources, fault tolerance, scalability, increase throughput, response time, etc. The paper summarizes various recent techniques introduced for load balancing in Cloud. The metrics of analyses are objectives, achievements, challenges of discussed load balancing techniques and their comparison.

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Performance Optimization in Cloud Computing Through Cloud Partitioning-Based Load Balancing

Srivastava

Singh

2017

Advances in Computer and Computational Sciences

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Towards a Load Balancing in a three-level cloud computing network

Cited by 100 publications

References 8 publications

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

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

Load Balancing Technique in Cloud Computing : A Review

Performance Optimization in Cloud Computing Through Cloud Partitioning-Based Load Balancing

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