Optimal Resource Allocation and Quality of Service Prediction in Cloud

Baldoss, Priya; Thangavel, Gnanasekaran

doi:10.32604/cmc.2021.013695

Cited by 6 publications

(4 citation statements)

References 18 publications

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“…Figs. [5][6][7] show that our proposed hybrid NAGCSA algorithm minimizes the makespan and finishes the execution of all cloudlets earlier than GA, PSO, and CSA. The simulation has been carried out on a varied number of cloudlets from 500 to 2000, and a mean of 30 simulation results are presented in the form of bar charts.…”

Section: Resultsmentioning

confidence: 93%

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Hybrid Cuckoo Search Algorithm for Scheduling in Cloud Computing

Bhuvaneshwari¹,

Venkatachalam²,

Prabu³

2022

Computers, Materials &Amp; Continua

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Cloud computing has gained widespread popularity over the last decade. Scheduling problem in cloud computing is prejudiced due to enormous demands of cloud users. Meta-heuristic techniques in cloud computing have exhibited high performance in comparison to traditional scheduling algorithms. This paper presents a novel hybrid Nesterov Accelerated Gradient-based Cuckoo Search Algorithm (NAGCSA) to address the scheduling issue in cloud computing. Nesterov Accelerated Gradient can address trapping at local minima in CSA by updating the position using future approximation. The local search in the proposed algorithm is performed by using Nesterov Accelerated Gradient, while the global search is performed by using levy flights. The amalgamation of NAG and CSA helps in cost reduction and time-saving for users. The simulation has been carried out on the CloudSim tool on three different real datasets; NASA, HPC2N, and SDSC. The results of the proposed hybrid algorithm have been compared with state-of-art scheduling algorithms (GA, PSO, and CSA), and statistical significance is carried on mean, standard deviation, and best for each algorithm. It has been established that the proposed algorithm minimizes the execution cost and makespan, hence enhancing the quality of service for users.

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Section: Resultsmentioning

confidence: 93%

“…However, there are methods of resource provisioning like leased and reserved, which are used by big organizations [5]. The additional benefits of cloud computing are scalability, availability, reliability, flexibility, and sustainability [6,7].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Cuckoo Search Algorithm for Scheduling in Cloud Computing

Bhuvaneshwari¹,

Venkatachalam²,

Prabu³

2022

Computers, Materials &Amp; Continua

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“…Because different auction models provide various market-driven resource allocation mechanisms. Literature studies cloud computing resource allocation based on auctions [10]. The article gives an auction-based cloud computing resource allocation framework, discusses the main problems that need to be faced when designing a dynamic resource allocation mechanism, and shares the latest technology of auction-based cloud computing resource allocation.…”

Section: Related Workmentioning

confidence: 99%

Fair and Stable Matching Virtual Machine Resource Allocation Method

Dai¹,

He²,

Sun³

et al. 2022

Intelligent Automation &Amp; Soft Computing

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In order to unify the management and scheduling of cloud resources, cloud platforms use virtualization technology to re-integrate multiple computing resources in the cloud and build virtual units on physical machines to achieve dynamic provisioning of resources by configuring virtual units of various sizes. Therefore, how to reasonably determine the mapping relationship between virtual units and physical machines is an important research topic for cloud resource scheduling. In this paper, we propose a fair cloud virtual machine resource allocation method of using the stable matching theory. Our allocation method considers the allocation of resources from both user's demand and cloud computing resource provider's request. When multiple users apply for resources, firstly select a user by user priority, and then deal with this user's task. Because the user priority is dynamic, so as to avoid a user's long-term share of resources. This strategy makes user task scheduling is relatively fair. On the basis of weighing the fair allocation of user resources, the stable matching between physical machines and virtual machines is achieved. Our simulation experiments especially given that the main focus of the paper is not to develop a very novel algorithm, but to demonstrate our virtual machine resource allocation method, which effectively improves the average utilization rate of computing resources and reduces the operating costs of cloud providers.

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“…Internet technology is a way of communication by using the internet, which is to provide real-time network resources and services to the users. Mobile internet gives convenience and the facility to provide various new business opportunities, computational services, and quality assurance [5]. MC is about sharing different resources and exchanging data among intelligent devices like mobile phones, laptops, and computers.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Firefly Based Intelligent Algorithm for Load Balancing in Mobile Cloud Computing

Poonam¹,

Sangwan²

2023

Computers, Materials &Amp; Continua

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This paper presents a novel fuzzy firefly-based intelligent algorithm for load balancing in mobile cloud computing while reducing makespan. The proposed technique implicitly acts intelligently by using inherent traits of fuzzy and firefly. It automatically adjusts its behavior or converges depending on the information gathered during the search process and objective function. It works for 3-tier architecture, including cloudlet and public cloud. As cloudlets have limited resources, fuzzy logic is used for cloudlet selection using capacity and waiting time as input. Fuzzy provides human-like decisions without using any mathematical model. Firefly is a powerful meta-heuristic optimization technique to balance diversification and solution speed. It balances the load on cloud and cloudlet while minimizing makespan and execution time. However, it may trap in local optimum; levy flight can handle it. Hybridization of fuzzy firefly with levy flight is a novel technique that provides reduced makespan, execution time, and Degree of imbalance while balancing the load. Simulation has been carried out on the Cloud Analyst platform with National Aeronautics and Space Administration (NASA) and Clarknet datasets. Results show that the proposed algorithm outperforms Ant Colony Optimization Queue Decision Maker (ACOQDM), Distributed Scheduling Optimization Algorithm (DSOA), and Utility-based Firefly Algorithm (UFA) when compared in terms of makespan, Degree of imbalance, and Figure of Merit.

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Optimal Resource Allocation and Quality of Service Prediction in Cloud

Cited by 6 publications

References 18 publications

Hybrid Cuckoo Search Algorithm for Scheduling in Cloud Computing

Hybrid Cuckoo Search Algorithm for Scheduling in Cloud Computing

Fair and Stable Matching Virtual Machine Resource Allocation Method

Fuzzy Firefly Based Intelligent Algorithm for Load Balancing in Mobile Cloud Computing

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