Energy-aware cost prediction and pricing of virtual machines in cloud computing environments

In this paper, we address the issue of resource allocation in a Cloud Computing environment. Since the need for cloud resources has led to the rapid growth of data centers and the waste of idle resources, high-power consumption has emerged. Therefore, we develop an approach that reduces energy consumption. Decision-making for adequate tasks and virtual machines (VMs) with their consolidation minimizes this latter. The aim of the proposed approach is energy efficiency. It consists of two processes; the first one allows the mapping of user tasks to VMs. Whereas, the second process consists of mapping virtual machines to the best location (physical machines). This paper focuses on this latter to develop a model by using a deep neural network and the ELECTRE methods supported by the K-nearest neighbor classifier. The experiments show that our model can produce promising results compared to other works of literature. This model also presents good scalability to improve the learning, allowing, thus, to achieve our objectives.

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Heuristic-based Approach for Dynamic Consolidation of Software Licenses in Cloud Data Centers

International Journal of Intelligent Systems and Applications ◽

10.5815/ijisa.2021.06.01 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1-12

Author(s):

Leila Helali ◽

◽

Mohamed Nazih Omri

Keyword(s):

Cloud Computing ◽

Data Centers ◽

Virtual Machines ◽

Heuristic Algorithms ◽

Energy Aware ◽

Computing Systems ◽

Cloud Data ◽

Software Licenses ◽

Cloud Data Centers ◽

Distributed Cloud

Since its emergence, cloud computing has continued to evolve thanks to its ability to present computing as consumable services paid by use, and the possibilities of resource scaling that it offers according to client’s needs. Models and appropriate schemes for resource scaling through consolidation service have been considerably investigated,mainly, at the infrastructure level to optimize costs and energy consumption. Consolidation efforts at the SaaS level remain very restrained mostly when proprietary software are in hand. In order to fill this gap and provide software licenses elastically regarding the economic and energy-aware considerations in the context of distributed cloud computing systems, this work deals with dynamic software consolidation in commercial cloud data centers 𝑫𝑺𝟑𝑪. Our solution is based on heuristic algorithms and allows reallocating software licenses at runtime by determining the optimal amount of resources required for their execution and freed unused machines. Simulation results showed the efficiency of our solution in terms of energy by 68.85% savings and costs by 80.01% savings. It allowed to free up to 75% physical machines and 76.5% virtual machines and proved its scalability in terms of average execution time while varying the number of software and the number of licenses alternately.

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A Threshold-based Tournament Resource Allocation in Cloud Computing Environment

Asian Journal of Research in Computer Science ◽

10.9734/ajrcos/2021/v7i130169 ◽

2021 ◽

pp. 1-13

Author(s):

I. P. Oladoja ◽

O. S. Adewale ◽

S. A. Oluwadare ◽

E. O. Oyekanmi

Keyword(s):

Cloud Computing ◽

Virtual Machines ◽

Scheduling Algorithm ◽

Selection Probability ◽

Cloud Computing Environment ◽

High Resource ◽

Performance Requirements ◽

Tournament Selection ◽

Computing Environments ◽

Cloud Users

Cloud computing environments provide an apparition of infinite computing resources to cloud users so that they can increase or decrease resource consumption rate according to their demands. In the Cloud, computing resources need to be allocated and scheduled in a way that providers can achieve high resource utilization and users can meet their applications’ performance requirements with minimum expenditure. Due to these different intentions, there is the need to develop a scheduling algorithm to outperform appropriate allocation of tasks on resources. The paper focuses on the resource optimization using a threshold-based tournament selection probability for virtual machines used in the execution of tasks. The proposed approach was designed to create metatask and the proposed algorithm used was Median-Based improved Max-Min algorithm. The experimental results showed that the algorithm had better performance in terms of makespan, utilization of resources and throughput. The load balance of tasks was also fairly distributed on the two datacenters.

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