A Firefly Colony and Its Fuzzy Approach for Server Consolidation and Virtual Machine Placement in Cloud Datacenters

Perumal, Boominathan; Murugaiyan, Aramudhan

doi:10.1155/2016/6734161

Cited by 14 publications

(7 citation statements)

References 36 publications

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“…More recent is the work presented in [ 91 ], which solves the same problem by means of an ant colony system. In addition, the research introduced in [ 92 ] hybridizes a Firefly Algorithm with fuzzy logic for server consolidation and VM placement in cloud data centers. Also interesting is the study presented in [ 93 ], which focuses on Hadoop Big Data technology.…”

Section: A Joint Perspective On Bio-inspired Computation and Big Datamentioning

confidence: 99%

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Bio-inspired computation for big data fusion, storage, processing, learning and visualization: state of the art and future directions

Torre-Bastida

Díaz-de-Arcaya

Osaba

et al. 2021

Neural Comput & Applic

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This overview gravitates on research achievements that have recently emerged from the confluence between Big Data technologies and bio-inspired computation. A manifold of reasons can be identified for the profitable synergy between these two paradigms, all rooted on the adaptability, intelligence and robustness that biologically inspired principles can provide to technologies aimed to manage, retrieve, fuse and process Big Data efficiently. We delve into this research field by first analyzing in depth the existing literature, with a focus on advances reported in the last few years. This prior literature analysis is complemented by an identification of the new trends and open challenges in Big Data that remain unsolved to date, and that can be effectively addressed by bio-inspired algorithms. As a second contribution, this work elaborates on how bio-inspired algorithms need to be adapted for their use in a Big Data context, in which data fusion becomes crucial as a previous step to allow processing and mining several and potentially heterogeneous data sources. This analysis allows exploring and comparing the scope and efficiency of existing approaches across different problems and domains, with the purpose of identifying new potential applications and research niches. Finally, this survey highlights open issues that remain unsolved to date in this research avenue, alongside a prescription of recommendations for future research.

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Section: A Joint Perspective On Bio-inspired Computation and Big Datamentioning

confidence: 99%

“…92] hybridizes a Firefly Algorithm with fuzzy logic for server consolidation and VM placement in cloud data centers. Also interesting is the study presented in[93], which focuses on Hadoop Big Data technology.…”

mentioning

confidence: 99%

Bio-inspired computation for big data fusion, storage, processing, learning and visualization: state of the art and future directions

Torre-Bastida

Díaz-de-Arcaya

Osaba

et al. 2021

Neural Comput & Applic

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show abstract

“…The server union focuses to lessen depend of substantial machines utilized and the virtual PC position bother is to gain most valuable situation approach with both least power utilization and resources wastage. The introduced procedures show best introduction over the heuristics and meta-heuristic methodology respected as far as server combination and finding most fantastic situation system [7]. On tackling VM position issue with appreciate to the reachable data transfer capacity which is defined as factor measured container pressing issue.…”

Section: Related Workmentioning

confidence: 99%

Optimal Virtual Machines Placement in Cloud Computing Environment

Kumar*,

Awasthi,

Kumar

2020

IJITEE

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In the cloud computing frameworks, when utilizing virtualization technology, the client's mentioned information data or storing away assistance is obliged by a lot of imparted virtual machines (VM) in a scale and flexible way. These VMs are put in at least one server point as per the point stores capacities or probabilities. The VM arrangement accessibility refers to the probability that in any event one lot of all client's mentioned VMs works during the requested. In this research paper we proposed an algorithm for better VM placement load balancing. Allocation is made based on the Assignment Problems arrangement strategy idea, which is planned for cloud computing. Assignment issues whose decision making parameters are summed up numbers as triangular fuzzy set as well as Centroid Ranking Technique is used for ranking the dataset. The linguistic variables are used to change the subjective information into quantitative information. Another positioning strategy is proposed to defuzzify the fuzzy numbers, trailed by Hungarian system for ideal cost and time. A numerical model is proposed methodology for better assignment of VM in the physical machine for cloud computing environment.

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“…The absolute restrictions constraints in the above stated description is that each of the VM can be allocated on only one physical machine at a time. The details of constraints are also referred from the study of [62]. For each type of resources (CPU, memory and bandwidth, the quantity of resource requests of VMs) placed in the same physical machine must be less or equal to ability/capacity of the PMs hosting them; The total numbers of PMs that allocate VMs [47] are not more than m, m i=1 y i m.…”

Section: Problem Formulation Of Vm Placementmentioning

confidence: 99%

Energy-efficient virtual machine placement using enhanced firefly algorithm

Barlaskar

Singh

Issac

2016

MGS

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The consolidation of the virtual machines (VMs) helps to optimise the usage of resources and hence reduces the energy consumption in a cloud data centre. VM placement plays an important part in the consolidation of the VMs. The researchers have developed various algorithms for VM placement considering the optimised energy consumption. However, these algorithms lack the use of exploitation mechanism efficiently. This paper addresses VM placement issues by proposing two meta-heuristic algorithms namely, the enhanced modified firefly algorithm (MFF) and the hierarchical cluster based modified firefly algorithm (HCMFF), presenting the comparative analysis relating to energy optimisation. The comparisons are made against the existing honeybee (HB) algorithm, honeybee cluster based technique (HCT) and the energy consumption results of all the participating algorithms confirm that the proposed HCMFF is more efficient than the other algorithms. The simulation study shows that HCMFF consumes 12% less energy than honeybee algorithm, 6% less than HCT algorithm and 2% less than original firefly. The usage of the appropriate algorithm can help in efficient usage of energy in cloud computing.

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A Firefly Colony and Its Fuzzy Approach for Server Consolidation and Virtual Machine Placement in Cloud Datacenters

Cited by 14 publications

References 36 publications

Bio-inspired computation for big data fusion, storage, processing, learning and visualization: state of the art and future directions

Bio-inspired computation for big data fusion, storage, processing, learning and visualization: state of the art and future directions

Optimal Virtual Machines Placement in Cloud Computing Environment

Energy-efficient virtual machine placement using enhanced firefly algorithm

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