An energy‐aware virtual machines consolidation method for cloud computing: Simulation and verification

2021 ◽  
Author(s):  
Rahmat Zolfaghari ◽  
Amir Sahafi ◽  
Amir Masoud Rahmani ◽  
Reza Rezaei
Keyword(s):  
Cloud Computing ◽  
Virtual Machines ◽  
Energy Aware ◽  
Computing Simulation

Energy-aware scheduling of virtual machines in heterogeneous cloud computing systems

2017 ◽  
Vol 74 ◽  
pp. 142-150 ◽  
Author(s):  
Hancong Duan ◽  
Chao Chen ◽  
Geyong Min ◽  
Yu Wu
Keyword(s):  
Cloud Computing ◽  
Virtual Machines ◽  
Energy Aware ◽  
Computing Systems ◽  
Energy Aware Scheduling ◽  
Heterogeneous Cloud

Novel energy-aware approach to resource allocation in cloud computing

2021 ◽  
Vol 17 (3) ◽  
pp. 197-218
Author(s):  
Karima Saidi ◽  
Ouassila Hioual ◽  
Abderrahim Siam
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Nearest Neighbor ◽  
Virtual Machines ◽  
K Nearest Neighbor ◽  
Energy Aware ◽  
Cloud Computing Environment ◽  
High Power Consumption ◽  
User Tasks ◽  
Consumption Decision

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.

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

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.

scholarly journals MeReg: Managing Energy-SLA Tradeoff for Green Mobile Cloud Computing

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Rahul Yadav ◽  
Weizhe Zhang
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Large Scale ◽  
Data Centers ◽  
Mobile Cloud Computing ◽  
Virtual Machines ◽  
Mobile Cloud ◽  
Energy Aware ◽  
Cpu Utilization ◽  
Selection Algorithms

Mobile cloud computing (MCC) provides various cloud computing services to mobile users. The rapid growth of MCC users requires large-scale MCC data centers to provide them with data processing and storage services. The growth of these data centers directly impacts electrical energy consumption, which affects businesses as well as the environment through carbon dioxide (CO2) emissions. Moreover, large amount of energy is wasted to maintain the servers running during low workload. To reduce the energy consumption of mobile cloud data centers, energy-aware host overload detection algorithm and virtual machines (VMs) selection algorithms for VM consolidation are required during detected host underload and overload. After allocating resources to all VMs, underloaded hosts are required to assume energy-saving mode in order to minimize power consumption. To address this issue, we proposed an adaptive heuristics energy-aware algorithm, which creates an upper CPU utilization threshold using recent CPU utilization history to detect overloaded hosts and dynamic VM selection algorithms to consolidate the VMs from overloaded or underloaded host. The goal is to minimize total energy consumption and maximize Quality of Service, including the reduction of service level agreement (SLA) violations. CloudSim simulator is used to validate the algorithm and simulations are conducted on real workload traces in 10 different days, as provided by PlanetLab.

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

2019 ◽  
Vol 93 ◽  
pp. 442-459 ◽  
Author(s):  
Mohammad Aldossary ◽  
Karim Djemame ◽  
Ibrahim Alzamil ◽  
Alexandros Kostopoulos ◽  
Antonis Dimakis ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Virtual Machines ◽  
Cost Prediction ◽  
Energy Aware ◽  
Computing Environments

scholarly journals A Two-Tier Energy-Aware Resource Management for Virtualized Cloud Computing System

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Wei Huang ◽  
Zhen Wang ◽  
Mianxiong Dong ◽  
Zhuzhong Qian
Keyword(s):  
Cloud Computing ◽  
Energy Saving ◽  
Virtual Machines ◽  
Dynamic Environment ◽  
Computing System ◽  
Energy Aware ◽  
Energy Prediction ◽  
Cloud Computing System ◽  
Save Energy

The economic costs caused by electric power take the most significant part in total cost of data center; thus energy conservation is an important issue in cloud computing system. One well-known technique to reduce the energy consumption is the consolidation of Virtual Machines (VMs). However, it may lose some performance points on energy saving and the Quality of Service (QoS) for dynamic workloads. Fortunately, Dynamic Frequency and Voltage Scaling (DVFS) is an efficient technique to save energy in dynamic environment. In this paper, combined with the DVFS technology, we propose a cooperative two-tier energy-aware management method including local DVFS control and global VM deployment. The DVFS controller adjusts the frequencies of homogenous processors in each server at run-time based on the practical energy prediction. On the other hand, Global Scheduler assigns VMs onto the designate servers based on the cooperation with the local DVFS controller. The final evaluation results demonstrate the effectiveness of our two-tier method in energy saving.

MOEAGAC: an energy aware model with genetic algorithm for efficient scheduling in cloud computing

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Nageswara Prasadhu Marri ◽  
N.R. Rajalakshmi
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Energy Consumption ◽  
Virtual Machines ◽  
Fitness Function ◽  
Research Work ◽  
Energy Aware ◽  
Content Type ◽  
Multi Objective ◽  
Proposed Model

PurposeMajority of the research work either concentrated on the optimization of scheduling length and execution cost or energy optimization mechanism. This research aims to propose the optimization of makespan, energy consumption and data transfer time (DTT) by considering the priority tasks. The research work is concentrated on the multi-objective approach based on the genetic algorithm (GA) and energy aware model to increase the efficiency of the task scheduling.Design/methodology/approachCloud computing is the recent advancement of the distributed and cluster computing. Cloud computing offers different services to the clients based on their requirements, and it works on the environment of virtualization. Cloud environment contains the number of data centers which are distributed geographically. Major challenges faced by the cloud environment are energy consumption of the data centers. Proper scheduling mechanism is needed to allocate the tasks to the virtual machines which help in reducing the makespan. This paper concentrated on the minimizing the consumption of energy as well as makespan value by introducing the hybrid algorithm called as multi-objective energy aware genetic algorithm. This algorithm employs the scheduling mechanism by considering the energy consumption of the CPU in the virtual machines. The energy model is developed for picking the task based on the fitness function. The simulation results show the performance of the multi-objective model with respect to makespan, DTT and energy consumption.FindingsThe energy aware model computes the energy based on the voltage and frequency distribution to the CPUs in the virtual machine. The directed acyclic graph is used to represent the task dependencies. The proposed model recorded 5% less makespan compared against the MODPSO and 0.7% less compared against the HEFT algorithms. The proposed model recorded 125 joules energy consumption for 50 VMs when all are in active state.Originality/valueThis paper proposed the multi-objective model based on bio-inspired approach called as genetic algorithm. The GA is combined with the energy aware model for optimizing the consumption of the energy in cloud computing. The GA used priority model for selecting the initial population and used the roulette wheel selection method for parent selection. The energy model is used as fitness function to the GA for selecting the tasks to perform the scheduling.

Distance Aware VM allocation process to minimize energy consumption in cloud computing

2019 ◽  
Vol 12 ◽  
Author(s):  
Gurpreet Singh ◽  
Manish Mahajan ◽  
Rajni Mohana
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Energy Consumption ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Research Work ◽  
Service Level Agreement ◽  
Service Level ◽  
Physical Machine ◽  
Allocation Process

BACKGROUND: Cloud computing is considered as an on-demand service resource with the applications towards data center on pay per user basis. For allocating the resources appropriately for the satisfaction of user needs, an effective and reliable resource allocation method is required. Because of the enhanced user demand, the allocation of resources has now considered as a complex and challenging task when a physical machine is overloaded, Virtual Machines share its load by utilizing the physical machine resources. Previous studies lack in energy consumption and time management while keeping the Virtual Machine at the different server in turned on state. AIM AND OBJECTIVE: The main aim of this research work is to propose an effective resource allocation scheme for allocating the Virtual Machine from an ad hoc sub server with Virtual Machines. EXECUTION MODEL: The execution of the research has been carried out into two sections, initially, the location of Virtual Machines and Physical Machine with the server has been taken place and subsequently, the cross-validation of allocation is addressed. For the sorting of Virtual Machines, Modified Best Fit Decreasing algorithm is used and Multi-Machine Job Scheduling is used while the placement process of jobs to an appropriate host. Artificial Neural Network as a classifier, has allocated jobs to the hosts. Measures, viz. Service Level Agreement violation and energy consumption are considered and fruitful results have been obtained with a 37.7 of reduction in energy consumption and 15% improvement in Service Level Agreement violation.

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