Dragonfly optimization and constraint measure-based load balancing in cloud computing

Polepally, Vijayakumar; Chatrapati, K. Shahu

doi:10.1007/s10586-017-1056-4

Cited by 77 publications

(37 citation statements)

References 24 publications

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“…9(a) that proactive approaches are always dynamic in nature while Fig. 9(b) depicts that most of the reactive approaches fall under dynamic state of algorithm which contribute to 68%, followed by static [40] Multi-Objective CloudSim [41] Single-Objective C++ [37] Multi-Objective CloudSim [38] Single-Objective SP2…”

Section: Resultsmentioning

confidence: 99%

“…Table 1 depicts proactive approaches in existing LB approaches. Polepally et al [37] proposed dragonfly optimization and constraint measure-based LB approach in cloud computing by distributing uniform load across VMs with minimal power consumption. Xiao, et al [38] proposed game theory based fairness-aware LB algorithm to minimize expected response time while maintain fairness.…”

Section: Nature Of the Algorithmmentioning

confidence: 99%

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Load balancing in cloud computing – A hierarchical taxonomical classification

2019

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Load unbalancing problem is a multi-variant, multi-constraint problem that degrades performance and efficiency of computing resources. Load balancing techniques cater the solution for load unbalancing situation for two undesirable facets-overloading and under-loading. In contempt of the importance of load balancing techniques to the best of our knowledge, there is no comprehensive, extensive, systematic and hierarchical classification about the existing load balancing techniques. Further, the factors that cause load unbalancing problem are neither studied nor considered in the literature. This paper presents a detailed encyclopedic review about the load balancing techniques. The advantages and limitations of existing methods are highlighted with crucial challenges being addressed so as to develop efficient load balancing algorithms in future. The paper also suggests new insights towards load balancing in cloud computing.

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Section: Resultsmentioning

confidence: 99%

Section: Nature Of the Algorithmmentioning

confidence: 99%

Load balancing in cloud computing – A hierarchical taxonomical classification

2019

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“…The specifications of used system are as follows: ➢ Operating system: windows 7 ➢ Type of operating system: 32 bit os ➢ RAM memory: 4 gigabytes -3.06 usable ➢ Processor: Intel processor-number of cores 7 ((Core™) i7 CPU) -Q 720@1.60GHz 1.60 GHz So, all of the results are based on a system with above specifications. All of the obtained results in this paper are compared with articles of [12] [15] [16]. In order to compare results some scenarios and criterions has been inspected.…”

Section: Evaluating Resultsmentioning

confidence: 99%

“…In first iterations number a big number of tasks will be migrated and gradually this number gets closer to Zero. In [16], dragonfly algorithm is used to maintain the load balancing between virtual machines in cloud computing. This article evaluates the number of migratory tasks.…”

Section: Fig6 Reduction Of Task Migration Level Using Dragonfly Algmentioning

confidence: 99%

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Providing a load balancing method based on dragonfly optimization algorithm for resource allocation in cloud computing

Amini¹,

Maeen²,

Jahangir³

2018

IJNDC

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Nowadays cloud computing is being highly considered by many of researchers, organizations, governments and so on. According to processing happening inside of cloud computing, some of the most important problems and challenges in cloud computing are load balancing, managing resource allocations, scheduling running of tasks. Load balancing on the surface of virtual machines on the internal surface of datacenters, scheduling and resource allocations in hosts and over virtual machines. Thus, by considering existing challenges in cloud computing, in this paper by the help of dragonfly optimization algorithm because of speed and preciseness in scheduling tasks, the process of allocating resources to virtual machines in cloud computing has been done. The proposed method has multiple steps that are as follows: initialization of algorithm and cloud computing, setting number of virtual machines and tasks, running dragonfly optimization algorithm, allocating resources and scheduling tasks with maintaining load balance in virtual machines. By simulation of the proposed method in this research, we observed that the rate of improvement in dragonfly optimization algorithm for resource allocation and keeping load balance between virtual machines is much higher than other methods when considering criterions like execution time, response time, number of migrated tasks and load balance.

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Magnum opus of an efficient hospitality technique for load balancing in cloud environment

Sakthivelmurugan

Vimala

Britto

2018

Concurrency and Computation

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Summary The load balancing approach in the cloud environment is an open issue in the recent days. It helps obtain better resource utilization in cloud environment. It also improves the performance of the system. A good load balancing algorithm in cloud ensures no under loading or over loading in a single host. This paper proposes an algorithm named Hospitality Load Balancing (HLB) algorithm, which aims to balance the load among virtual machines (VM) for increasing the performance and throughput. The proposed algorithm handles preemptive or nonpreemptive tasks, which may be independent. It helps to reduce make span and obtain low task migration. The HLB algorithm has been analyzed and compared with plenty of load balancing technique. An experimental result shows that it is an excellent and effective algorithm when compared with the existing load balancing algorithm. It also illustrates the performance of proposed load balancing scheme with analysis and proves that it attains low makespan and low task migration.

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Dragonfly optimization and constraint measure-based load balancing in cloud computing

Cited by 77 publications

References 24 publications

Load balancing in cloud computing – A hierarchical taxonomical classification

Load balancing in cloud computing – A hierarchical taxonomical classification

Providing a load balancing method based on dragonfly optimization algorithm for resource allocation in cloud computing

Magnum opus of an efficient hospitality technique for load balancing in cloud environment

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