Enhanced cuckoo search algorithm for virtual machine placement in cloud data centres

Barlaskar, Esha; Singh, Yumnam Jayanta; Issac, Biju

doi:10.1504/ijguc.2018.090221

Cited by 18 publications

(6 citation statements)

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“…The work depends upon the job scheduling process of the VM allocation and modified best fit decreasing (MBFD) is used as a classifier using artificial neural network (ANN) [16]. (Barlaskar et al, 2018) have presented work based on the solving issue of energy consumption on data that is stored on the cloud data center. The author proposed an algorithm named enhanced cuckoo search (ECS) that is implemented.…”

Section: Literature Surveymentioning

confidence: 99%

“…As a result, a comparative result of ECS is evaluated against the genetic algorithm (GA), ant colony (AC) algorithm, and optimized firefly search (OFS) algorithm. The parameters that are used for results evaluations are; energy consumption, workload, the performance of SLA, and VM migration [17]. (Samriya and Kumar, 2020) proposed a work that is related to the (QoS) parameter including makespan, minimization of migration (MM) of tasks, security, and cost.…”

Section: Literature Surveymentioning

confidence: 99%

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Artificial bee colony optimized VM migration and allocation using neural network architecture

2023

IJATEE

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In recent years, cloud computing (CC) has been mostly preferred by users due to its advanced features as compared to other environments. There are many advantages to CC, like security, the privacy of data, fast speed of the internet, reliability, etc. [13]. But still, some issues arise in CC, like service attacks, less privacy, etc. The CC architecture is categorized into two parts; the user end and the cloud data center end. The CC architecture, which explains how a user interacts with a cloud data center, is shown in Figure 1. The front end defines the process of user interaction with the cloud by using the internet. Users can interact with any internet-connected device, including personal computers, mobile phones, etc., and the device communicates with the cloud data center in the backend.

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Section: Literature Surveymentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

Artificial bee colony optimized VM migration and allocation using neural network architecture

2023

IJATEE

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“…Boonhatai Kruekaew et al [20] combined the artificial bee colony intelligent optimization algorithm with the heuristic scheduling algorithm, and proposed an artificial bee colony heuristic scheduling algorithm (HABC), which comprehensively considers the execution time and load balancing factors for cloud platform performance Impact, applicable to cloud computing virtual machine resource scheduling in heterogeneous environments. In order to enhance the efficiency of cloud data center resource utilization, Esha Barlaskar et al [21] proposed an enhanced cuckoo search algorithm (ECS) and verified the effectiveness of the proposed algorithm through the Cloudsim simulation platform. Divya Chaudhary et al [22] released a new load scheduling technology, namely the hybrid genetic gravity search algorithm (HG-GSA), to reduce the execution and transmission costs.…”

Section: Related Workmentioning

confidence: 99%

Collaborative Optimization Scheduling of Cloud Service Resources Based on Improved Genetic Algorithm

Wang

2020

IEEE Access

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With the wide application of cloud computing technology, the services provided by cloud systems have become increasingly diverse, thus these systems are required to solve tasks of high variety and complexity, with a tremendously extensive amount of task data involved. That is why reasonable scheduling system resources are particularly important in cloud computing research. In this paper, a cloud computing system needs to take into account a wider range of cloud service resource types and collaborative optimization scheduling issues in order to solve the tasks at hand. Firstly, a new adaptive genetic algorithm (NAGA) was proposed. By improving the crossover mutation genetic operator, the algorithm was able to save excellent individuals as much as possible, enhance the algorithm's optimization ability, and greatly reduce the probability of the algorithm falling into the local optimal solution. Secondly, focusing on the main factors affecting service quality, such as task completion time, system load, and network bandwidth, an upgraded fitness operator method for the cloud resource collaborative optimization scheduling problem is set forth. Finally, an algorithm of cloud service resources based on an improved genetic algorithm (OSIG) is proposed. Experiments on the CloudSim cloud computing simulation platform demonstrate that the OSIG algorithm proposed in this paper can effectively optimize the resource scheduling strategy, shorten the task completion time, facilitate the system load balancing, and boost the system's service quality. The theoretical analysis was consistent with the experimental results.

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“…For instance, Barlaskar et al 14 proposed a resource allocation scheme to improve the resource usage. One is allocating hosts for VMs.…”

Section: Literature Reviewmentioning

confidence: 99%

“…One is allocating hosts for VMs. For instance, Barlaskar et al 14 described an enhanced cuckoo search algorithm to determine the placement of VMs in cloud data centers. The other is selecting VMs for tasks.…”

Section: Literature Reviewmentioning

confidence: 99%

An efficient virtual machine allocation algorithm for parallel and distributed simulation applications

Yao

Chen

et al. 2019

Concurrency and Computation

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Allocating appropriate resource for parallel and distributed simulation (PADS) applications in clouds is an intuitive way to improve their execution efficiency. However, the heterogeneity of virtual machine (VMs) in clouds with respect to both their computing power and network latency influences the execution efficiency of PADS applications on different combinations of VMs.Besides, frequent synchronization is one of the characteristics during the execution of PADS applications, which seriously challenges the prediction of the influence of VMs' computing power and network latency on their execution efficiency, and makes allocating appropriate VMs difficult as a result. This paper first proposes a revivification-based prediction model (ERP), which revives the execution based on statistical data from actual execution of PADS applications to predict the running time of PADS applications on different combinations of VMs. Then, an ERP-based Allocation algorithm, namely, ERPA, is raised to optimize VMs allocation to minimize the running time of PADS applications in clouds. A series of experiments are conducted to compare the proposed ERPA with three resource allocation algorithms, ie, Gang-scheduling-based, Makespan-based, and Max-Min-based algorithms, and the experimental results demonstrate the advantage of ERPA in improving execution efficiency of PADS applications in clouds. In particular, for communication-sensitive PADS applications, the advantage of ERPA is more significant. KEYWORDS execution revivification, parallel and distributed simulation, performance prediction, resource allocation INTRODUCTIONWith the development of simulation technology, the scale of parallel and distributed simulation (PADS) applications is expanding, and the demand for computing power increases as well. Currently, the parallel platforms or clusters are widely used to improve the execution efficiency, but the high cost of setting up, operating, and maintaining the basic hardware and software infrastructures severely limits the proliferation of simulation technology. The emergence of cloud computing paradigm, which receives considerable atttention in both academic and industry, 1 makes it possible to run PADS applications with reasonable cost. 2,3 In clouds, customers can access unlimited resources (eg, VMs) on demand at a ''pay-as-you-go'' model, and just pays for their actual use. Therefore, deploying PADS applications in clouds is attractive.In PADS, simulation entities are mapped on different machines/cores, and their states are modified by executing events at discrete time.These entities communicate with each other by sending and receiving time-stamped messages. During parallel execution, these entities are synchronized periodically to yield the same result as the sequential execution. As a result, the execution performance goes down. Due to the frequent synchronizations among entities, the running time of PADS is determined by the slowest entities. Besides, the network latency among entities can place a great influence on the runni...

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Enhanced cuckoo search algorithm for virtual machine placement in cloud data centres

Cited by 18 publications

References 0 publications

Artificial bee colony optimized VM migration and allocation using neural network architecture

Artificial bee colony optimized VM migration and allocation using neural network architecture

Collaborative Optimization Scheduling of Cloud Service Resources Based on Improved Genetic Algorithm

An efficient virtual machine allocation algorithm for parallel and distributed simulation applications

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