Optimal Virtual Machine Provisioning in Cloud Computing Using Game Theory

Temghart, Abdelkarim Ait; Omar, Driss Ait; Marwan, Mbarek

doi:10.1007/978-3-030-76508-8_22

Cited by 4 publications

(3 citation statements)

References 31 publications

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“…It can model interactions among these parties to reach a Nash equilibrium, which represents a stable resource allocation strategy [318]. Game theory-based resource allocation models can be complex, computationally expensive, and may require substantial communication overhead to coordinate decisions among different entities [319]. Nonetheless, in dynamic cloud environments, achieving equilibrium may be challenging due to changing conditions and participants' strategies.…”

Section: Trade-offs and Discussionmentioning

confidence: 99%

A Comprehensive Survey on Sustainable Resource Management in Cloud Computing Environments

Saxena,

Singh

2024

Preprint

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Section: Trade-offs and Discussionmentioning

confidence: 99%

A Comprehensive Survey on Sustainable Resource Management in Cloud Computing Environments

Saxena,

Singh

2024

Preprint

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“…It ensures fairness by selecting virtual machine groups with multiple resources fairly based on the current physical machine's remaining resource ratio and the available virtual machine resource ratio. In 2021, Abdelkarim Ait Temghart [9] introduced a non-cooperative game model to analyze the characteristics of market structure. This model ensures fairness in the sharing of available virtual machine resources in data centers while reducing the operating costs of operators and increasing profits.…”

Section: Related Workmentioning

confidence: 99%

SAUFEE: a datacenter task scheduling algorithm considering user fairness and energy efficiency

Peng,

Yan,

et al. 2024

International Conference on Communication, Information, and Digital Technologies (CIDT2024)

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The data center uses virtualization and isolation technologies to provide flexible and efficient services for multi-tenants. One of the most challenging aspect of resource sharing is task scheduling. During the scheduling process, it is crucial to ensure fairness in user resource usage and achieve high cluster utilization and energy efficiency. However, the heterogeneity of resources and the variations in user demands make it extremely difficult to provide an effective scheduling solution. In this paper, we propose an efficient heuristic scheduling algorithm called SAUFEE, which trades off the resource requirement of multi-tenants and cluster power consumption. First, we introduce a user fairness model, which prioritizes the tasks of users with the least resource allocation in each scheduling round, ensuring fairness among them. Next, we propose a resource utilization model to schedule user tasks to reduce resource waste. Additionally, idle machines are shut down to save overall cluster energy consumption. The simulation experiment results show that our algorithm increases the number of running tasks by 3.3% and the CPU utilization by 3.4% while ensuring fairness. Our algorithm plays an important role in improving cluster energy efficiency and user fairness.

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“…Since the placement problem is proven to be NP-hard in the general case [14][15], [28][29], [34][35], alternative algorithms using different techniques are presented to solve it. Ant colony optimization (ACO) [14], [36], genetic algorithm (GA) [15][16], [30], greedy algorithm [4], [28], [33], game theory [32], and biogeography-based optimization (BBO) [37] are some of the heuristic or metaheuristic methods. Furthermore, some other models like graph [5], [8], [29], [33], approximation algorithms [38], and linear programming [9-10], [33] have been widely used to solve the problem.…”

Section: Related Workmentioning

confidence: 99%

Virtual Machine Placement Optimization for Big Data Applications in Cloud Computing

Seyyedsalehi

Khansari

2022

IEEE Access

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Big data and cloud computing are two advanced technologies that have overcome many computing and analytical challenges in recent years. With the rise in the applications of these technologies, the necessity of efficiency and optimization in the utilization of related resources has made sense. The procedure of locating virtual machines (VM) in physical machines (PM) affects the performance, speed, and costs of cloud computing services. VM placement in cloud computing is an NP-hard problem. Indeed, the problem is more complicated in big data tasks due to the need for transferring high volumes of traffic between VMs. This paper proposes a new approach for VM placement in a multi data center (DC) cloud environment. The aware genetic algorithm first fit (AGAFF) is a context-aware algorithm that distinguishes big data tasks with an input tag and uses a structure to minimize the traffic between MapReduce nodes. This multi-objective algorithm is based on the genetic algorithm, which is incorporated with the first fit methodology. The algorithm minimizes energy usage by minimizing the number of used servers, intra-DC traffic of big data tasks, and VMs' live migration while maximizing relevant usage of CPU and RAM in every server. Furthermore, it improves job execution time, especially in big data processing, and reduces service level agreement (SLA) violations. A comparison between the results of AGAFF and four other algorithms shows by about 61% energy consumption reduction on average on different scales and approves a decrease in the number of needed PMs, intra-DC traffic of big data processing, and the number of live migrations.

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Optimal Virtual Machine Provisioning in Cloud Computing Using Game Theory

Cited by 4 publications

References 31 publications

A Comprehensive Survey on Sustainable Resource Management in Cloud Computing Environments

A Comprehensive Survey on Sustainable Resource Management in Cloud Computing Environments

SAUFEE: a datacenter task scheduling algorithm considering user fairness and energy efficiency

Virtual Machine Placement Optimization for Big Data Applications in Cloud Computing

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